Sleep Post Correction

An astute commenter pointed out that I misread the numbers in the paper on sleep and fat loss. I wrote that out of the total 3.0 kg lost, the high-sleep group lost 2.4 kg as fat, and the low-sleep group lost 1.4 kg of fat out of 2.9 kg total.

In fact, the high-sleep group lost 1.4 out of 2.9 kg as fat, and the low-sleep group lost 0.6 out of 3.0 kg as fat. So I got the numbers all mixed up. Sorry for the mistake. The main point of the post still stands though: sleep deprivation negatively influences body composition.

The correct numbers are even more interesting than the ones I made up. Even in the high-sleep group, nearly half the body weight lost by simple calorie restriction was lean mass. That doesn't make calorie restriction look very good!

In the sleep-deprived group, 80% of the weight lost by calorie restriction came out of lean mass. Ouch!

That illustrates one of the reasons why I'm skeptical of simple calorie restriction as a means of fat loss. When the body "wants" to be fat, it will sacrifice lean mass to preserve fat tissue. For example, the genetically obese Zucker rat cannot be starved thin. If you try to put it on a severe calorie-restricted diet, it will literally die fat because it will cannibalize its own lean mass (muscle, heart, brain, etc.) to spare the fat. That's an extreme example, but it illustrates the point.

The key is not only to balance energy intake with expenditure (which the brain does automatically when it's working correctly), but to allocate energy appropriately to lean and fat mass.

monday, monday

yep, it's definitely monday......
i was exhausted from traveling so i took a super long nap yesterday afternoon.  not such a good idea...i found myself staring at the clock at 2:00 AM...then i found taylor staring at me at 6:30.  coffee please.

*images courtesy of english muse, full house

And the Truthy Shall Set You Free

I've just read about a great project at Indiana called Truthy. (Here's a linky). The idea is simple: during the upcoming election, their system will detect smear campaigns on social networking sites in real time, and post some visualization of how the meme spreads over time. The idea is to try to prevent "astroturfing", which are well-organized political campaigns masquerading as grassroots efforts.

"The team will then generate diffusion network images that visitors to Truthy.indiana.edu can view as groups of nodes and edges that identify retweets, mentions, and the extent of the epidemic...

Menczer got the idea for the Truthy website after hearing researchers from Wellesley College speak earlier this year on their research analyzing a well-known Twitter bomb campaign conducted by the conservative group American Future Fund (AFF) against Martha Coakley, a democrat who lost the Massachusetts senatorial seat formerly held by the late Edward Kennedy. Republican challenger Scott Brown won the seat after AFF set up nine Twitter accounts in early morning hours prior to the election and then sent out 929 tweets in two hours before Twitter realized the information was spam. By then the messages had reached 60,000 people.

Menczer explained that because search engines now include Twitter trends in search results, an astroturfing campaign -- where the concerted efforts of special interests are disguised as a spontaneous grassroots movement -- that includes Twitter bombs can jack up how high a result shows up on Google even if the information is false...

'One of the concerns about social media is that people are being manipulated without realizing it because a meme can be given instant global popularity by a high search engine ranking, in turn perpetuating the falsehood,' Menczer said."

Definitely a clever approach to the problem, and if you're a twitter user, get involved!

Papers, citations, co-authorship, and genealogy

This summer I accidentally found out that there are two papers citing our CMRT 2009 paper. I was excited a bit about that since they were the first actual citations of me, so I even read those papers.

The first of them [Димашова, 2010] was published in Russian by OpenCV developers from Nizhny Novgorod. They implemented cascade-based face detection algorithm that used either Local Binary Patterns (LBP) or Haar features. The algorithm was released within OpenCV 2.0. They cite our paper as an example of using Random Forest on the stages of the cascade. However, they implemented the classical variation with the cascade over AdaBoost.

Another citation [Sikiric et al., 2010] is more relevant since it came from the road mapping community. They address the problem of recovering a road appearance mosaic from the orthogonal views to the surface. They contrast their approach with ours in the way that theirs do not employ human interaction. In fact, we need human input for recognizing road defects and lane marking, rectification is done in the previous stage, which is fully automatic.

The rest of the post is devoted to some interesting metrics and structures concerning citations, co-authorship and supervising students.

Citations

The number of citations is a weak measure of paper quality. We can also go further and estimate the impact of a journal or a particular researcher based on the number of citations. A generally accepted measure is the journal impact factor, which is simply the mean number of citations by paper published in the journal for some period in time. Individual researchers could be evaluated by the impact factor of the journals they published in, though it is considered as a bad practice. Another not-so-bad practise is h-index. By definition, one's h-index equals H if there are at least H citations of his top H papers¹. It has also been criticised widely.

So, citation-based scoring has a lot of flaws. But what can we use instead? Another interesting approach is introduced by ReaderMeter. They collect the information about the number of people who have added some paper to their Mendeley collections and compute something like h-index. Unfortunately, they recently excluded some papers from their database, so the statistics became less representative but more accurate.

Co-authorship and Erdős number

Paul Erdős was a Hungarian mathematician who published about 14 hundred research papers with 511 different co-authors. That's why he has a special role in bibliometrics. Erdős number is defined as a collaborative distance from a researcher to Erdős. More strictly, Wikipedia defines:

Paul Erdős is the one person having an Erdős number of zero. For any author other than Erdős, if the lowest Erdős number of all of his coauthors is k, then the author's Erdős number is k + 1.

My Erdős number is at most 7 (via Olga Barinova, Pushmeet Kohli, Philip H.S. Torr, Bernhard Schölkopf, John Shawe Taylor, David Godsil). To be honest, Erdős number system is primarily used for math papers. Even if we use the wider definition, it is not that beautiful, because there are not too many gates, e.g. all the vision community will probably connect to Erdős via the machine learning gate. Thus, the researchers who work on the edge will be closer. To illustrate this fact, David Marr probably had not any finite Erdős number during his lifetime (now he definitely has). So, we can introduce, say, Zisserman number for computer vision.² According to DBLP, Andrew Zisserman has 165 direct co-authors so far. Now, my Zisserman number is 3, David Marr's is 4 (via Tomaso Poggio, Lior Wolf, Yonatan Wexler).

The movie industry have their own metrics, which is the Bacon number (after Kevin Bacon). The distance is established 1 if two actors have appeared in a single movie. Someone tried to combine those two to the Erdős-Bacon number. For some person, it is just a sum of her Erdős and Bacon numbers. Of course, very few people have a finite Erdős-Bacon number, since one should both appear in a movie and publish a research paper (and it is still not sufficient). Often they are possessed by researchers who consulted the filming crew and accidentally were filmed. =) Erdős himself has this number 3 or 4 (depending on the details of definition), since he starred in N is a Number (1993) and his Bacon number is thus 3 0r 4.

The person with the probably lowest Erdős-Bacon number is Daniel Kleitman, an MIT mathematician, who has appeared in one of my favourite movies Good Will Hunting (1997) along with Minnie Driver, who collaborated with Bacon in Sleepers (1996). Since Kleitman has 6 joint papers with Erdős, his Erdős-Bacon number is 3.

Surprisingly, Marvin Minsky has his Erdős number (4) greater than his Bacon number (2), which he obtained via The Revenge of the Dead Indians (1993) and Yoko Ono. Another strange example is the paedophile's dream Natalie Portman. She has graduated from Harvard, saying she would rather "be smart than a movie star". That's my kind of a girl! A neuroscience paper [Baird et al., 2002] brought Natalie Hershlag (her real name) Erdős number of 5, and then she appeared in New York, I Love You (2009) along with Kevin Bacon, so she reached the same Erdős-Bacon number as Minsky, i.e. 6.

UPD (Mart 13, 2011). There is a totally relevant xkcd strip.

Scientific genealogy

Finally, I tell about what is known as scientific genealogy. Every grown-up researcher has a PhD advisor, usually one, while an advisor can have a lot of students. Let's just use the analogy with parents and children. We get a tree (or a forest) representing the historical structure of science. The mathematics genealogy project aims to recover this structure.

I tried to track my genealogy back. Unfortunately, I failed to find who was Yury M. Bayakovsky's PhD advisor. But if consider Olga Barinova my advisor, I am the 11th generation descendant of Carl Friedrich Gauß. Nice ancestry, huh?

The similar project exists for computer vision. There are 290 people in the base, though there are duplicates (I've found five Vitorio Ferrari's :). It seems strange that some trees have depth as big as 5 (e.g. Kristen Graumann and Adriana Quattoni are the 5th generation after David Marr), though vision is a relatively young field.

¹ Okay, take the supremum of the set if you want to stay formal
² It seems that Philip Torr has already used that number.

Jumat, 08/10/2010 15:47 WIB
Daging Merah Pemicu Kanker?
Renny Wahyuningsih - detikFood

Jakarta - Apakah Anda tergolong penggemar daging? Jika ya, ada baiknya sedikit berhati-hati dalam mengkonsumsi daging merah. Konon, daging merah disinyalir sebagai pemicu kanker payudara. Bagaimana mungkin?

Konsumsi daging merah pada awalnya adalah karena kandungan protein dan zat besi serta sejumlah nutrisi lain yang diperlukan oleh tubuh. Daging merah, merupakan sebutan untuk daging dari ternak seperti sapi, kerbau, domba, kambing, kuda, babi, dan lain-lain.

Seperti yang diulas oleh eatingwell, daging merah ternyata mengandung banyak lemak dan hormon yang sering ditambahkan pada hewan ternak. Inilah yang meningkatkan hormon penyebab kanker, seperti kanker payudara atau kanker prostat. Daging juga tinggi protein, protein akan memecah amonia yang bersifat karsinogenik pada manusia.

Dalam sebuah penelitian terhadap 35.372 wanita Inggris. Mereka diberi minimal 60 gram daging merah atau 30 gram daging olahan per hari dikaitkan dengan meningkatnya risiko kanker payudara. Resiko akan lebih besar pada wanita menopause.

Secara alami protein dan lemak sangat di butuhkan oleh tubuh, namun jika asupan protein yang masuk terlalu banyak, maka tubuh akan mengalami ketidakseimbangan. Hal ini berakibat obesitas atau kelebihan berat badan. Selain itu juga memicu timbulnya sel kanker dan batu ginjal.

Anda tidak perlu takut mengkonsumsi daging merah, namun harus lebih cermat memilih jenis daging baik. Misalnya dengan mengganti atau menyelingi konsumsi daging merah dengan daging putih (daging ayam atau ikan). Bisa juga mengkonsumsi daging sapi organik atau daging sapi bebas hormon yang sudha banyak dijual di pasaran.


( dev / Odi )