Neural Networks (NNs) in Confinement Fusion

I just found the following paper written in the "Proceedings of the National Academy of Sciences of the USA" (PNAS), which describes the application of machine learning surrogate models for prediction in confinement fusion:

Rushil Anirudh, Jayaraman J. Thiagarajan, Peer-Timo Bremer, and Brian K. Spears, "Improved surrogates in inertial confinement fusion with manifold and cycle consistencies"

Proceedings of the National Academy of Sciences of the USA (PNAS), 117 (18), 9741-9746, 2020.

1) Link to the paper www, HERE
2) Link to the .pdf file, HERE

It seems some specific types of neural networks may work well in prediction tasks, in these so hard to simulate plasma physics scenarios.

News at the National Ignition Facility (NIF) website, HERE.

[Almost] - Everything / Anything unified in ML?

I would like to think that the idea proposed by Prof. Eric P. Xing about the possibility to set out a standard model in Machine Learning has good chances to be accepted, or at least carefully analyzed.

Have a look at the video below:

And then - Magic!

Just uau!

I recently discovered Diego Catalán and I am amazed by his painting and drawing work.

I can not reproduce it here. Just go to the links above and enjoy!

Disentangling Luke Skywalker - Darth Vader connection

Two questions:

- Would Machine Learning be able to convince Luke Skywalker that Darth Vader is his father?

- What would have happened in the case of Luke and Princess Leia?

I just found by chance a very interesting paper by Prof. Theo Gevers et al about the possibility to infer not only the probability that two people are kins, but also the type of kinship between them. 

I was not even aware that kinship verification was another research topic in machine learning. More specifically, the aim now is about kinship identification.

Details are as follows:

1) Wei Wang, Shaodi You, Sezer Karaoglu, Theo Gevers, "Kinship Identification through Joint Learning Using Kinship Verification Ensemble" 

arXiv:2004.06382

PDF HERE 

Scientific Déjà Vu

I have just read a blog entry (HERE) by Prof. Yoshua Bengio about the current publishing dynamics in machine learning.

Insightful analysis about how scientific reviewing process might be improved.

Reading this blog has allowed me to discover The Slow Science Manifesto

Do your own homework: Fermi problem

This is not an Enrico Fermi problem, but somehow close to it (or inspired by).

Would you be really interested to know the impact a phenomenon like COVID-19 may have in a country's life?

Play with numbers a little bit:

1) Write down the entire population of a country: A

2) Assess the percentage of people would be expected to be in contact with the virus, and infected: 40%, for instance

3) Assume a 3% mortality rate in relation to the number of people that were infected

4) Do the following product:

Total death number = A * 0.4 * 0.03

Do you think this is not serious enough?

Building selfless and knowledgeable communities

A list of 24 sources of free machine learning knowledge:

https://blog.paralleldots.com/data-science/24-best-and-free-books-to-understand-machine-learning/

Enjoy learning!

Substantiation of rationality - human weaknesses

I just found a paper describing a way to beat a deep fake detector. In particular, the paper analyses how a state-of-the-art-deep face detector may determine that a face is real, when it is not.

The paper is:

Paarth Neekhara, Shehzeen Hussain, Malhar Jere, Farinaz Koushanfar, Julian McAuley,
"Adversarial Deepfakes: Evaluating Vulnerability of Deepfake Detectors to Adversarial Examples"
arXiv:2002.12749
PDF file HERE

No comments...

Hybrid Intelligence - Hybrid Moral Intelligence?

I understand that there might be a useful point in trying to enhance human decisions with some help (hint, whatever you would like to call it) from artificial intelligence (AI).

When should we take advantage of AI in our decisions?

As per Wikipedia:

"Morality (from Latin: moralitas, lit. 'manner, character, proper behavior') is the differentiation of intentions, decisions and actions between those that are distinguished as proper and those that are improper...Morality can be a body of standards or principles derived from a code of conduct from a particular philosophy, religion or culture, or it can derive from a standard that a person believes should be universal... Morality may also be specifically synonymous with "goodness" or "rightness".

Morality is a creation of the human being.

- Do we really need to have the help of an AI system for that?

- Would an AI agent help us decide whether an action is proper or improper?

I just read some information regarding research lines aiming at infer how AI can support human moral judgments.

NO need to help me. Thank you!

NO to no man's land

Nice video...A few months now, but hits the nail on the head.

Old code - New Loops

I just found by chance in the NASA web page (HERE) that they have been running a model named ROCKE-3D that:

"...calculates the details of the orbit of any planet around its star. This has been modified from the original Earth model so that it can handle any kind of planet in any kind of orbit, including planets that are "tidally locked,"..."

The model (code) can be found HERE.

My first approach to FORTRAN was during my BSc in Physics, in 1996. NASA still uses it, and for very modern purposes (so to speak).

"They have mouths, but cannot speak, eyes, but cannot see..."

2019 was the 2nd hottest year on record - as per NASA.

Psalm 115:5-7: "They have mouths, but cannot speak, eyes, but cannot see. They have ears, but cannot hear, noses, but cannot smell. They have hands, but cannot feel, feet, but cannot walk, nor can they utter a sound with their throats".

Unrolling [Deep] Neural Networks

When I wrote my last post, I was not aware of research avenues that link Neural Networks (specially, the "deep" ones) to "iterative" algorithms used in signal and image processing.

I just found by chance the following paper by Prof. Vishal Monga, Yuelong Li and Prof. Yonina Eldar:

1) "Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing"
Vishal Monga, Yuelong Li, Yonina C. Eldar
https://arxiv.org/abs/1912.10557

Good news when I read the word "interpretable" close to the words "Neural Networks"...

Having nice conversations with Generative Adversarial Networks (GANs)

I recently came across a blog by Bram Cohen and read a very interesting post (which can be found HERE) about one of the most intriguing flaws current neural networks have (so to speak): why they fail so tremendously (in some cases, under some circumstances) when a small amount of graciously created noise is introduced into the input data.

He particularly recommended reading the paper:

Adi Shamir, Itay Safran, Eyal Ronen, Orr Dunkelman
"A Simple Explanation for the Existence of Adversarial Examples with Small Hamming Distance"
https://arxiv.org/abs/1901.10861

Look at (the text surrounding and) Figure 1 (What the h...?)

That is the reason why I find particularly interesting the research approaches that Prof. Dr. Antonio Torralba and his PhD student David Bau are carrying out in a particular type of neural networks called Generative Adversarial Networks.

Not the same, but, (well) in some sense...

A summarising video of what I mean by this would be the one below:

Causal Discovery For Climate

I am very happy to see how Machine Learning is contributing to climate prediction. In particular, the initiative Causality 4 Climate tries to establish the maturity some models may have to infer causes leading to extreme climate processes happening nowadys.

The following paper is particularly in order:

Jakob Runge, Sebastian Bathiany, Erik Bollt, Gustau Camps-Valls, Dim Coumou, Ethan Deyle, Clark Glymour, Marlene Kretschmer, Miguel D. Mahecha, Jordi Muñoz-Marí, Egbert H. van Nes, Jonas Peters, Rick Quax, Markus Reichstein, Marten Scheffer, Bernhard Schölkopf, Peter Spirtes, George Sugihara, Jie Sun, Kun Zhang & Jakob Zscheischler, 
"Inferring causation from time series in Earth system sciences"
Nature Communications volume 10, Article number: 2553 (2019)

Link to www of the paper, HERE.

I am also proud to know some of the authors :-)

Try not to learn my [confounding] biases!

Dr. David Lopez-Paz's research never deceives. His (I think) last published work goes to the main core of the classifier training process, i. e., we should try to somehow avoid classifiers to learn spurious (not consistent) relationships that establish on the training data and which make generalization ability difficult to carry out. Their aim is to develop "invariant" and "causal predictors", to enable a "good generalisation behaviour".

Important (well, at least I consider so) piece of research:

Martin Arjovsky, Léon Bottou, Ishaan Gulrajani and David Lopez-Paz, "Invariant Risk Minimization", arXiv:1907.02893, 2019.

https://arxiv.org/abs/1907.02893

Link to pdf file HERE.

No reason not to see you

Walls and dark illumination conditions are becoming less of a problem to identify and infer the presence of people and even actions taking place "out of our sight".

Nice work by Prof. Dina Katabi (MIT Computer Science & Artificial Intelligence Lab, MIT-CSAIL) on "Making the invisible visible":

1) Tianhong Li, Lijie Fan, Mingmin Zhao, Yingcheng Liu, Dina Katabi, "Making the Invisible Visible: Action Recognition Through Walls and Occlusions"
https://arxiv.org/abs/1909.09300

PDF file of the paper available HERE.

Dignified AI

Happy to learn about this work made at the University of Minnesota

Too much -- too little

Reading Dr. Paulina Jaramillo I found the following opinion report.

I completely agree with Shoshanna Saxe. I think there will be a time when we will not be able to efficiently use all the data we are generating and our life may become quite difficult to manage.

Life seems to evolve in cycles and in an action-reaction dynamics...Difficult to "see the horizon"...

Will we choose?

I do not know what my future will be, but something is a cornerstone in my life: try to do your part in science. It will be bigger or smaller, better or worse, but, if honest, it will contribute somehow.

I recall President John F. Kennedy's speech "We choose to go to the moon...":

So, when I see pieces of work like the one made by Dr. Laure Zanna I, again, think to myself "Good for you!":

Laure Zanna, Samar Khatiwala, Jonathan M. Gregory, Jonathan Ison, and Patrick Heimbach, "Global reconstruction of historical ocean heat storage and transport", Proceedings of the National Academy of Sciences of the USA (PNAS) January 22, 2019 116 (4) 1126-1131; https://doi.org/10.1073/pnas.1808838115.

Equilibrium

Old (and not so) school rules...

Rita Pierson

Shred the tendency

Open access to textbooks simply means that more students are able to have access to knowledge, allowing them to eliminate a part of their financial problems in their university path. However, this means what it means, no more and no less. 

We have the tendency to look at a problem, and its *derivatives*, *consequences*, *similarities* to other problems, etc.

A recent study finds no significant effect on the student learning success when using open access textbooks, as compared to other resources:

Phillip J. Grimaldi, Debshila Basu Mallick, Andrew E. Waters, Richard G. Baraniuk, "Do open educational resources improve student learning? Implications of the access hypothesis", PLoS ONE 14(3): e0212508. https://doi.org/10.1371/journal.pone.0212508

Link: HERE

I did not expect another outcome. However, this study is not *definitive*...

Grateful

Christmas time is approaching. You can always help someone close to you. In so many ways...

My dad sent me this video, and the final sentence is so inspiring for me, not because I think I have some special talent but because I would like to think that I always tried to do the right thing.

"Your talent is God's gift to you. What you do with it is your gift back to God"

Leo Buscaglia

"Little drummer boy"

GENTRY.

Is there no end?

*Deep learning*...It seems the *panacea* for everything that is complicated in *computer vision and pattern recognition*.

Well, people can do very interesting things. For instance, THIS.

Youtube video, below.

Rachel Thomas and *The democratic Artificial Intelligence*

Very nice *Ted-Talk* by Prof. Rachel Thomas on avenues and opportunities in Artificial Intelligence.

There are several *take home messages* here, but my favourite appears at (mm:ss) 16:18 in this video:

...Sometimes people ask me if it is dangerous to make AI accessible to more people. What is dangerous is a homogeneous & exclusive group creating technology that impacts us all.

Video, below. Well worth the time.

Human Rights and Beings...

I am glad to see how machine learning is taking new avenues that will hopefully help us being *a better society*.

Research made by people like Dr. Kristian Lum (link HERE) is definitely going in this direction...

On the other hand, I can frankly feel how this field of research is accelerating and impacting more and more...

Examples of this are the *brand new* degree programs that are being offered (link HERE - and video in there worth having a look into).

Data driven science? Why not, *purists*? Come down from your pedestal!

Computer Vision and Machine Learning *reinforce* themselves in order to get aims that are impacting daily lives of almost any human being on Earth. Denying that is simply straying from the truth.

Models applied in Computer Vision work many times based on data and are *built from* data.

That is NOT inherently wrong!

Many times there is NO other way to do it!

Waiting for a *non data-driven* model to be obtained could take centuries, in some cases!

This is (below) what Computer Vision and Machine Learning have got so far. Congratulations to those who achieved this and also to those people who genuinely appreciate it!

It is a little bit old (2017, :-)) but worth seeing it.

Why didn't I ask that?

When I was at high school I used to hold my hand up and ask as many questions as I thought I needed in order to understand something.

Of course, this depends on the person, but there is another piece of research supporting the idea that (a significant amount of) women systematically think of failure, feel fear about it, and then act the way they would not really want to act.

I do not know if this is a by-pass product of the "Impostor Syndrome" some research studies also show when referring to women in academia and their careers.

This post in NATURE is very interesting (link HERE), related to the following paper:

Carter AJ, Croft A, Lukas D, Sandstrom GM (2018) Women’s visibility in academic seminars: Women ask fewer questions than men. PLoS ONE 13(9): e0202743. https://doi.org/10.1371/journal.pone.0202743

It can be read HERE.

"MACHOs aren't the answer"

It is quite strange, but sometines I find nice and even funny acronyms for R&D projects, scientific instruments, concepts, etc.

In a recent Discover magazine article (it can be found HERE) a Section was called "MACHOs aren't the answer".

In this case, MACHOs stands for Massive Compact Halo Objects and the author wanted to say that these objects do not satisfactorily explain the great rotation speeds that stars at a galaxy's edge have.

I know of some other acronyms, but I prefer to keep them for myself :-)

When I am happy (mostly) when you are...

This video has some time now...Anyway, worth seeing...

It was worth it for me, but...Katia Levecque et al Research

I just found the following paper:

Katia Levecque, Frederik Anseel, Alain De Beuckelaer, Johan Van der Heyden, LydiaGisle, "Work organization and mental health problems in PhD students", Research Policy, 46, 4, 868-879, 2017 (Link HERE).

which (I think) it is considered as one of the most serious quantitative research studies on the effect of the stress on students associated to carrying out a PhD thesis.

Katia Levecque was very recently interviewed (link HERE) and it seems they have plans to expand the aims and population of the research published in the Research Policy Journal. Good news then!

There is a wide range of symptoms called as "mental health problems", including just "depression" and "anxiety", and if that is the case, I had them (who did not?).

In the end, I believe it was worth it for me, but I can only talk for myself...

Good for you!

Good "reviews" the short film One Small Step is receiving, and awards as well (winner of the SIGGRAPH2018 Electronic Theater audience choice award; see HERE).

Haven't seen it yet, but would like to:

"I always believed the world is what we make of it"

I enjoy my life going by and I see some thoughts of mine have not changed almost anything for so many years...

Too few Dr. Eleanor Arroway like people...

It just comes and it will stay

I am not an expert in anything related to music. I feel, however, puzzling that one of the most beatiful effects music has in people, which is to create emotions, does not seem to have been researched in depth.

I do not know if the "Theory of musical equilibration" (link HERE) was just a beginning, and I do not know either whether this research is serious enough or not (sorry again, as I said, my knoweldge about music lays between zero and null space) but I can only say that, for may times I hear this song, for many years that time may go by, my heart always shrinks when I hear it.

No gates for the open plain (?)

Nice piece of research for 3D social interaction capture and analysis in:

Hanbyul Joo, Tomas Simon, Xulong Li, Hao Liu, Lei Tan, Lin Gui, Sean Banerjee, Timothy Scott Godisart, Bart Nabbe, Iain Matthews, Takeo Kanade, Shohei Nobuhara, Yaser A Sheikh, "Panoptic Studio: A Massively Multiview System for Social Interaction Capture", IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2018 (DOI: 10.1109/TPAMI.2017.2782743).

Link HERE.

Please, have a look at Figure 2.

Synthesizing me

I am always willing to reading the new research papers co-authored by Prof. Frédo Durand. I am really impressed by what they show that were able to achieve in:

1) Guha Balakrishnan, Amy Zhao, Adrian V. Dalca, Fredo Durand, John Guttag,
"Synthesizing Images of Humans in Unseen Poses",  arXiv:1804.07739 (pdf file available HERE).

What do you think about Figure 1? :-)

Peter Turchin and Evolution Of The (Complex) Human Societies

I find it very interesting the research approach that is carrying out Prof. Peter Turchin when analysing the evolution of human societies from thousand years ago to the resent day.

In this sense, I see that his (and co-workers) approach described in (link to paper HERE):

Peter Turchin et al. "Quantitative historical analysis uncovers a single dimension of complexity that structures global variation in human social organization", Proceedings of the National Academy of Sciences of the United States of America (PNAS), 115, 2, E144-E151, 2018

seem to show that Machine Learning and his field of research could live a golden age of cooperation in the very near future.

Don Quixote - "Freedom, Sancho..."

A part of my "wonder times".

Chapter LVIII

"When Don Quixote saw himself in open country, free, and relieved from the attentions of Altisidora, he felt at his ease, and in fresh spirits to take up the pursuit of chivalry once more; and turning to Sancho he said, "Freedom, Sancho, is one of the most precious gifts that heaven has bestowed upon men; no treasures that the earth holds buried or the sea conceals can compare with it; for freedom, as for honour, life may and should be ventured; and on the other hand, captivity is the greatest evil that can fall to the lot of man. I say this, Sancho, because thou hast seen the good cheer, the abundance we have enjoyed in this castle we are leaving; well then, amid those dainty banquets and snow-cooled beverages I felt as though I were undergoing the straits of hunger, because I did not enjoy them with the same freedom as if they had been mine own; for the sense of being under an obligation to return benefits and favours received is a restraint that checks the independence of the spirit. Happy he, to whom heaven has given a piece of bread for which he is not bound to give thanks to any but heaven itself!"

Hope for the best. Plan for the worst...

...and do your bit.

Leverhulme Centre for the Future of Intelligence

The Dunning-Kruger effect

A friend of mine just directed my attention to the so-called (in psychology) Dunning-Kruger effect. We can find in Wikipedia the following (LINK):

"In the field of psychology, the Dunning–Kruger effect is a cognitive bias wherein people of low ability suffer from illusory superiority, mistakenly assessing their cognitive ability as greater than it is. The cognitive bias of illusory superiority derives from the metacognitive inability of low-ability persons to recognize their own ineptitude; without the self-awareness of metacognition, low-ability people cannot objectively evaluate their actual competence or incompetence".

The original paper related to this effect is:

• J. Kruger, D. Dunning, "Unskilled and unaware of it: how difficulties in recognizing one's own incompetence lead to inflated self-assessments", Journal of Personality and Social Psychology, 77, 6, 1121-1134, 1999.

A pdf file of the paper can be found HERE

I found many problems to track recent research work on this topic and I find it very interesting...

Building Up A Bridge

R&D funding institutions sometimes insist on the importance of multi-disciplinary work in Science. Other times, they just simply seem to forget about that.

I think that a part of the recent work made by Prof. Jennifer Listgarten on using machine learning for CRISPR/Cas9 is an evident example of sum of forces.

You might want to have a look at the interview she gave HERE and in particular to her answer to the question:

"How did you get interested in applying machine learning to CRISPR technology?"

I Hadn't Realized Machine Learning Reached Maturity Already

My (BSc + MSc) background is Physics and I have always found Philosophy of Science very interesting to read and reflect on. However, I was not aware of the following until a few days ago: a knowledge corpus is being created that is called  Philosophy of Machine Learning.

Two examples:

1) Philosophy of machine learning: knowledge and causality

2) Machine Learning Meets Philosophy: From Epistemology to Ethics

A very good symptom in many senses...

Dark Matter: Machine Learning Will Shed Light On You

Very intriguing, nice and estimulating new workshops, conferences and meetings are currently appearing on applications of Machine Learning.

Have a look, for instance, to:

"Accelerating the search for dark matter with machine learning"

Description and aims: HERE

I can only partially feel you: Turning Corners Into Cameras

Katherine Bouman and co-workers just developed a clever way to infer movement of objects in a scene using subtle changes in radiance arriving at a camera, with the peculiarity that the camera does NOT see anything of the scene at all!

Good piece of work!

Paper, code and datasets can be found HERE

Youtube video below:

I probably did not say that...

For my opinion, see my previous post on "and then one president became another president" :-(

Food System Panorama

The following paper, co-authored by Prof. Molly Brown recently called my attention:

Do markets and trade help or hurt the global food system adapt to climate change?

ME Brown, et al

Food Policy 68, 154-159

Link to paper: HERE (ScienceDirect)

I really feel (I do not know her opinion. It is mine) we are assuming climate change as something to be accepted (without an alternative) and feel like saying Quo Vadis? 

On the other hand, I would have wanted to see an empirical/numerical analysis of the topic in the paper. I guess it has to be very hard to do, but worth trying it (?).

Heterogeneous dialogue

Facebook research seems to be taking research into dialogue understanding seriously.

You can read an interesting post HERE that I found looking for research papers authored by Dr. Antoine Bordes.

I see their point in trying to optimize a conversation with a machine and also trying that it might be more friendly, or more human (so to speak) but I don't feel comfortable when technology might be able to understand, some day, in an automated way, a written conversation that I may have with somebody.

May be I see ghosts everywhere (?!)

Light Fields and Mixed Reality

A world full of possibilities just in front of and close to us.

Link to Vimeo video HERE

All these ideas come in part from Prof. Gordon Wetzstein (Stanford university) and collaborators' minds.

I thik it is worth having a look at his group's web page.

Shannon will take care of me

Impact of Prof. Claude Shannon (link to Wikipedia HERE) work is so deep and diversified... It reaches almost any knowledge branch where "information" can be somehow measured.

I recently found a particularly useful and interesting application in the following paper:

R. Quax; B.D. Kandhai and P.M.A. Sloot: Information dissipation as an early-warning signal for the Lehman Brothers collapse in financial time series, Scientific Reports, vol. 3, 2013.

We can measure these (complex) dynamics and even predict their impact. The key issue for me is also a measure of immediacy.

Am I your cause or your effect?

Prof. Bernhard Schölkopf's work never disappoints. Herculean work but worth the effort.

Mooij, J. M.; Peters, J.; Janzing, D.; Zscheischler, J. & Schölkopf, B.,
"Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks",
Journal of Machine Learning Research, 2016, 17, 1-102

Link to the paper details HERE

Hammers and nails

Simple and very useful:

R. E. Kass, B. S. Caffo, M. Davidian, X. Meng, B. Yu, Nancy Reid* (2016). Ten simple rules for effective statistical practice. PLoS Comput. Biol., 12(6): e1004961. doi:10.1371/journal.pcbi.1004961

Old times' sake: "Accuracy, error, precision and uncertainty"

First principles...HERE

Karl Deisseroth and "Comparing Tissue Transparency Methods for Intact Analyses"

I think we are getting closer to have a clear, non destructive and non invasive strategy to create images of the brain at a neural circuitry level.

Images are simply astonishing.

Link to CLARITY HERE

Link to Dr. Karl Deisseroth publications, HERE

And then "one president became another president"

Computer graphics analysis and understanding is evolving at a speed that is really scaring me. A paper recently published in the 2015 edition of the International Conference on Computer Vision (ICCV) showed how to process a video with gestures from a person and then create a 3D computer graphics model of another person doing the same gestures.

In the video that can be shown in the following LINK (from minute 1:07 onwards) a 3D computer graphics model of a U. S. president makes the same gestures as those made by another president.

There are moral implications for me in this scenario.  

Joseph Lucas & Guillermo Sapiro: "Cancer: What's luck got to do with it?"

I enjoyed reading a communication by Prof. Joseph E. Lucas and Prof. Guillermo Sapiro about the conclusions that can be drawn from a research paper by Cristian Tomasetti and Bert Vogelstein.

You can read the "comment" HERE

I think we have here an example of at least two assumptions that might be misleading in statistical data analysis:

a) how "population averages" translate into individual "cases". Several different individual "configurations" may give the same "average".

b) consider that (process) variables are independent among them, and therefore if some have an effect-result on others, simply infer that the others do not have a "statistically significant" similar effect ("Environment" and "Genetics" in the "Number of stem cell divisions", Figure 1, in this paper).

Physical Sciences Informatics (PSI) Data Repository open to the public

Imagine a "combustion experiment" at the "International Space Station" (ISS).

How much does an experiment of this kind cost?

Should anyone have access to data associated to each one of these experiments?

NASA thinks so.

Links:

1)  "Physical Sciences Research Program" (HERE)

2) "Physical Sciences Informatics System" (HERE)

3) "Open Science data" (Wikipedia - HERE)

Summing and counting

No better summary than NASA's title:

"Record-Breaking Climate Trends 2016 – Rising Temperatures and Shrinking Sea Ice"

"Qui habet aures audiendi audiat" - Matthew 13 (Link HERE)

The meritocracy that once was?

The term meritocracy has fascinated me for so many years... You can find a good definition even in Wikipedia (HERE):

"Meritocracy (merit, from Latin mereō "I earn" and -cracy, from Ancient Greek κράτος kratos "strength, power") is a political philosophy holding that power should bevested in individuals almost exclusively based on ability and talent.^[1] Advancement in such a system is based on performance measured through examination and/or demonstrated achievement in the field where it is implemented"

In a society I would like to live in, I would like to feel that I am where my effort and capabilities have allowed me to arrive.

I am happy where I am. But not everybody can say the same.

In a study by Emilio J Castilla whose details are:

Gender, Race, and Meritocracy in Organizational Careers
Emilio J. Castilla
American Journal of Sociology
Vol. 113, No. 6 (May 2008), pp. 1479-1526

he shows that even companies that apply policies based on meritocracy, other factors affect decisions about who to hire, how much to pay, etc.

Can't you think of examples in your own life where you say to yourself: "this person should have...but he/she does not and the criteria do not have anything to do with capabilities...". All of us can think of at least one example, and that makes millions of examples in the end.

Therefore, I should think we do not live and will not live even in something similar to a meritocratic social system.

Final note: Even all of the above, I still find it fascinating some Youtube videos like the following one:

"NASA’s Global Tour of Precipitation in Ultra HD (4K)"

Dr. Dorin Comaniciu - Cinematic Rendering technology

Dr. Dorin Comaniciu is the example that shows a company can make a balance between good science and profits.

Why shouldn't Siemens apply the best machine learning tools available?

Why shouldn't a company and "university style research" be together?

The following video showing Siemens Cinematic Rendering technology is simply amazing!

Ron Mello - Kintsugi - My professional life

Ron Mello's studio and gallery web page can be found HERE

Extraordinary new things are always "just around the corner"

Just look and will find.

Walt Whitman - oppression and shame - I sit and look out

List of ongoing armed conflicts.

From Wikipedia: HERE

- And the sea also has a preminence

- And the prisoners

- And the tyranny

- And I read Walt Whitman's "I sit and look out":

"I sit and look out upon all the sorrows of the world, and upon all oppression and shame; 

I hear secret convulsive sobs from young men, at anguish with themselves, remorseful after
 deeds done; 

I see, in low life, the mother misused by her children, dying, neglected, gaunt,
 desperate; 

I see the wife misused by her husband—I see the treacherous seducer of young women; 

I mark the ranklings of jealousy and unrequited love, attempted to be hid—I see these
 sights on the earth;

I see the workings of battle, pestilence, tyranny—I see martyrs and prisoners; 

I observe a famine at sea—I observe the sailors casting lots who shall be
 kill’d, to preserve the lives of the rest; 

I observe the slights and degradations cast by arrogant persons upon laborers, the poor,
 and upon negroes, and the like; 

All these—All the meanness and agony without end, I sitting, look out upon, 
See, hear, and am silent."

"Even if the cold burns, even if fear bites..." - Mario Benedetti

Below you will find a poem by uruguayan writer Mario Benedetti

The spanish only version: HERE

English and spanish: HERE

Noah Smith and "Economics Has a Math Problem"

I found this story almost by chance, and I do not quite agree with this person.

Link HERE

Economics will be "saved" by mathematics, I agree with that, but I do not think it may not be evolving in a mathematical framework.

I am not convinced either by the fact that its guidlines might be "data driven".

Some science disciplines are fact is evolving under two competing "pulses":

a) the need to have a "model" to explain the data

b) the possibility to infer data behaviour without a model (i. e., to use "data driven prediction" tools)

I think there is not a clear answer about which approach is best.

The little boy - Helen Buckley

I could not resist the temptation to write this post.

Text (one option): HERE

A story inspired by that text (sorry, only in spanish):

¿Bailamos? - Shall we dance? (New opportunity)

I have found this video subtitled in french, but not into english, sorry!

Machine Learning for Social Good - II

May be this kind of R&D projects and initiatives is just starting to "take off" and also take a "fair shape" in terms of recognition.

Link (to the information - call) below:

IBM Social Good Fellowship

Do we really want this?

As you can read HERE:

"Earth’s 2015 surface temperatures were the warmest since modern record keeping began in 1880, according to independent analyses by NASA and the National Oceanic and Atmospheric Administration (NOAA)..."

Casey Greene and "Why we preprint"

I would recommend you to read the following post by Prof. Casey Greene titled:

"Why we preprint"

That post is in the very center of a "hot" discussion currently happening at the vision sciences community. However, it has been taking part in other fields like biology, physics, etc. for some time now.

Should we publish our work in a freely available public platform, or should we submit our papers and let them get into a typical/traditional peer review process?

There are reasons for both "processes"...

It is "El Niño", stupid!

"El Niño" is going to create severe and prolonged climate changes during the next few months. That is what NASA just released in a Dec. 29 statement (read it HERE).

Please, try not to do the logical inference from "El Niño" -> Temporal change in climate, but the other way round: Climate change -> Change in "El Niño" features.

Better now?

Truth and Fallacy in Educational Theory

I am thinking of buying this book:

I really find it hard:

a) to understand the way we try to teach our "pupils" today. I just inherit the aims others had before for the subjects which I give lectures in the university for.

b) to understand why students today do NOT want to learn.

Will I have to re-invent myself before being able to teach?

Will I have to learn the very basic foundations about how students acquire knowledge?

It doesn't seem to be just a matter of a new way to look at EPISTEMOLOGY

A different way to look at the amazing Earth

Earth's beauty is just one click away.

Go to: "NASA Earth Observatory"

My field has an impact - Data Science for Social Good

I am happy and proud to read how my research field evolves and what some people spend their time in:

DATA SCIENCE FOR SOCIAL GOOD

There are projects particularly interesting for me. For instance:

"College Persistence: Helping Students Beyond High School Graduation"

I am here for you - my son

Please, always remind me that you must grow up the way you WANT

The Aral Sea: the sea that was

We are able to reach the most commendable achievements, but our power to destroy is infinite.

There were port-cities in the Aral sea. The cities are still there, but the sea is not there anymore.

The water was necessary for something else.

15 minutes of fame

I think everybody needs to feel accepted by the community, but the way a person would like them to accept him/her varies substantially among us.

Some people need to hear they are beautiful, some other need to hear they are smart, and others just need to hear they care someone else.

In science, do we need to feel we are "recognised" by our colleages?

How do we need them to recognise us?

Have you never felt you are in a spiral like movement where the more you publish the more you need to?

Why?

What for?

I think only a few things really matter in life.

Sic Transit Gloria Mundi

Terminators? No, thank you!

It seems we are within reach to create Artificial Intelligent machines that may be able to autonomously behave like soldiers in the battlefield.

I found an open letter HERE through Prof. Bernhard Schölkopf web page

This is an open debate that is happening now, and should!

Organisations like Future of Life Institute seem to be doing a good job.

Maths And Women

Or (read the following interview to Prof. Ingrid Daubechies):

"Maths is (also) for women"

Amazing grace

8000 years ago, europeans were not white. Please have a look at the following Science journal report HERE

My heart shakes when I see people hate each other because of color skin.

USA is a great country, I can say it, and I can believe it when I hear "Amazing grace" HERE

My son grows up so fast!

"NASA Releases Detailed Global Climate Change Projections"

HERE

Shame on you, humanity: Please give us water!

I have no words for this (LINK)

Journal of Universal Rejection

All the issues are empty? :-)

Link to the journal HERE.

Machines that think (Prof. Tomaso Poggio)

It seems to me we are still far away from the appearance of what we might call an "intelligent machine". In particular, should our definition of an intelligent machine be dependent on new/emergent understanding of what we can even consider that "human intelligence" is?.

I think so. I recommend you to read this short interview to Prof. Tomaso Poggio (link HERE).

In this moment of life - Ruth Bader Ginsburg

I think the following article is worth to be read:

"What Ruth Bader Ginsburg Taught Me About Being a Stay-at-Home Dad" (you can find it HERE)

that I could find when reading Dr. Laura Balzano's Blog

Enjoy!

Impostor Syndrome

I came accross this sentence when I arrived at Alexandra K. Schofield web page. In particular, in her blog

Wikipedia defines this syndrome as (link HERE):

"Impostor syndrome is a psychological phenomenon in which people are unable to internalize their accomplishments. Despite external evidence of their competence, those with the syndrome remain convinced that they are frauds and do not deserve the success they have achieved. Proof of success is dismissed as luck, timing, or as a result of deceiving others into thinking they are more intelligent and competent than they believe themselves to be. Notably, impostor syndrome is particularly common among high-achieving women".

Probably also happens in other situations (under-represented minorities)?.

2014 is the warmest year in record

NASA just published the following release:

http://www.nasa.gov/press/2015/january/nasa-determines-2014-warmest-year-in-modern-record/#.VLk1bStfZ5p

Merry Christmas!

No regrets...

Statistics in perspective

Mathematics are funny:

1) Correlation and causality:

"It is proven that the celebration of birthdays is healthy. Statistics show that those people who celebrate the most birthdays become the oldest" (credited to S. den Hartog)

2) Power of arithmetics:

"A physicist, a biologist, and a statistician see two people enter a house, and then after some time, they see three people leave the house.

The physicist concludes, "My initial observation must have been incorrect." The biologist concludes, "Clearly, the two reproduced..." The statistician concludes, "Well, if one more person enters the house, then there will be no-one in the house!"

3) Bayesian flavour:

"A Bayesian is one who, vaguely expecting a horse, and catching a glimpse of a donkey, strongly believes he has seen a mule".

Bad news by NASA: West Antarctic Glaciers in Irreversible Decline

I will remember these research findings for a long time:

William Freeman's vision about How to Do Research

Around 1.5 years before reading my PhD I bought a book about how to write a PhD. In that book I found some discussions about something more important: which topic you want to work in and how one should try to solve problems one faces when doing research.

Prof. William Freeman's approach is a very efficient way to make oneself a clear and fast idea about the main guidelines:

1) William T. Freeman How To Do Research (Informal note written for new graduate students), March 6, 2013

In this document you will find a link (you can also click HERE) to a presentation where important people in my field have given their opinion about how to do research.

I hope will be useful!.

Gibbs & Simpson: Conditions Under Which Assessment Supports Students’ Learning

I would recommend you to read an article, which can be found HERE by Graham Gibbs and Claire Simpson which:

"... sets out and attempts to justify a set of ‘conditions under which assessment can support learning’. The evidence is rarely conclusive enough to argue that if your assessment fulfils these conditions then learning
will inevitably be more effective. They are offered as a plausible set of guidelines".

Can we make our students learn and underpin the acquired knowledge better with tools different from exams?...

Information based Model for the Classroom

I could not say it better. Made by Robert T. Balder. Link to web page HERE

FameLab Talking Science

The idea is simple:

just try to explain an idea/problem from a scientific point of view in a direct, easy and innovative way. See the following:

- Jonathan Webb FameLab UK 2013 Finalist:

Why not?. You can do it!.

Do you have a dream?. They also had it!

a) Nadra Hatchett: Marshall Space Flight Center, NASA

b) Bill Allen, Jet Propulsion Laboratory (JPL) @ NASA

c) Karin Bozak, Glenn Research Center, NASA

BICEP2 and inflation

Imagine Science becomes part of your way to look at life, and the consequences of your work are unimaginable, how would you react?. Look for one minute the video starting at 0:32.

Do schools go (somehow) against creativity?

I had never thought about whether the current hierarchical structure of subjects taught at schools is correct or not, but may be some people leave them because they do not feel integrated or motivated enough.

It might not be a matter of abilities, but a matter to find what you are good at, and be that "discovery" the reason not to "give up".

Please look at the following video. A good story at the end of it!.

Making Open Education Resources (OER) possible

This post is related to my previous OpenStax College post.

You might be interested to have a look at efforts towards making education resources widely accessible. Link is HERE

Conference at a glance (from their web page):

"Each year, the conference brings together over 200 leaders from academia, education, and industry who are seeking solutions to the rising cost of higher education and are poised to initiate change and increased access in their communities.

The 2014 Connexions Conference theme, “Making OER Work,” is an opportunity for educational leaders to focus on pragmatic solutions in open education".

Landfill harmonic

If you are in a hurry just see the video from 0:28 to 1:06...If not, see it completely

It will just cover your face as you wail and cry and scream

Some discussions I have had with people who teach young students is about the main aim they want to achieve when they TEACH.

Making their student feel happy is not what I would expect they would like to obtain, but I feel sometimes education goes in parallel, and sometimes I think this should go first.

Dead Poets Society

And Todd says: "...You push it, stretch it, it'll never be enough. You kick at it, beat it, it'll never cover any of us. From the moment we enter crying to the moment we leave dying, it will just cover your face as you wail and cry and scream."

Simply marvellous. Don't you think so?.