WordPress ate 90% of this in an editing problem, so here is an abbreviated version.

Half of a Yellow Sun (Chimamanda Ngozi Adichie): A really powerful book set during the Biafran War in Nigeria. I had read Americanah first, and this book could not be more different. In reading I kept thinking that the Biafran War occupies the same place as Partition only the outcome was very different and it was much more recent. I put it up there with the must-read books of postcolonial literature.

The Annihilation Score (Charles Stross): This is a Laundry novel, and the n-th in the series, so if you haven’t read the rest it won’t be the right place to start. I liked this bits about government bureaucracy and coverups and shell games, but somehow it was less engaging than some of the previous novels. One of the strong points to me is the change in narrator — this one is from the perspective of Dominique O’Brien, as opposed to her husband Bob. Definitely some good bits in there about women in positions of authority and so on. Recommended if you’re a Laundry fan already.

The Absolutely True Diary of a Part-Time Indian (Sherman Alexie): This novel is about a teenage kid growing up on the Rez. It’s definitely pitched for the YA crowd, but it doesn’t really pull that many punches. I think he captures with some lightness the early high school anxieties, despite the really grim reality of the situation. I wonder what students reading it think versus adults. Highly recommended.

Anya’s Ghost (Vera Brosgol): a lovely YA graphic novel about a teenage girl who becomes friends with a ghost who seems to have… other plans.

Range of Ghosts / Shattered Pillars / Steles of the Sky (Elizabeth Bear): Really great high-fantasy series set in a fictionalized Central Asia (plus China plus Russia). Distances are vast, and communication is poor as Henry Farrell noted, so the book has very different themes than most. There’s some nods to this being a post-apocalyptic Earth but those are not pursued, which was the right tactic I think.

The Just City (Jo Walton): Have you ever wondered what would happen if people tried to actually create Plato’s Republic in real life? In this book, Athina (the goddess) tries to just that, and Apollo decides to become mortal to see what “volition” and “equal significance” are all about. The Masters are Plato fans from across the ages, snapped up out of time. The students are Greek slave children, rescued from markets to live in the Just City and become their best selves. Socrates makes an appearance. There are robots. Do they have free will? Lots of philosophy here, but there’s a story too, and character development, and all that. Really a great read, but you have to like talking about Ideas. Unlike other fictionalized philosophies, this one is actually a novel first, which makes it a delight.

Tracks: Composing Uncomposed

  1. Mustang Sally (The Commitments)
  2. World Weary (Noël Coward)
  3. Lefty Teachers at Home (Don Byron)
  4. There’ll Be Some Changes Made (Dave Brubeck with Jimmy Rushing)
  5. Central Park Blues (Ultimate Painting)
  6. I’m A Shy Guy (Ed Reed w. Randy Porter, Jamie Fox, John Wiitala, and Akira Rana)
  7. Good Enough For Granddad (Squirrel Nut Zippers)
  8. Manchester (Kishi Bashi)
  9. Coffee (Sylvan Esso)
  10. He’s Funny That Way (Billie Holiday)
  11. Together (Lightning Love)
  12. Mark My Words (Holly Miranda)
  13. Asa Branca (Forro in the Dark feat. David Byrne)
  14. A Little Lost (Nat Baldwin)
  15. Sunday Candy (Donnie Trumpet & the Social Experiment feat. Chance The Rapper)
  16. O Mistress Mine (George Stiles)
  17. Lullaby (Graham Gordon Ramsay feat. Scott Nicholas)

Postdoctoral opportunity at NYU CS/Global Health

Post-doctoral opportunity in developing novel computational approaches for disease surveillance. The laboratory of Dr. Rumi Chunara in Computer Science & Engineering, and the College of Global of Public Health at New York University is seeking highly motivated researchers to develop and study crowdsourced and point-of-care data for understanding infectious and chronic disease in populations worldwide.

Ideal postdoctoral candidates will have a Ph.D. with a strong background in bioinformatics, biostatistics, computer science or related field. Expertise in statistical machine learning and/or data mining are required. Preferred requirements for this position include experience designing software applications and/or storing, retrieving, and analyze large datasets. Experience with R, Python, SQL, JavaScript is preferred. Experience in hacking with cloud technologies (e.g., AWS, Hadoop) is a big plus. You must demonstrate an interest or experience in working with biological data such as genomic sequence, syndromic surveillance or physiological data.

This is an exciting research area and New York City provides great opportunities for networking and support of innovative work. Our group is engaged in many high-profile studies in collaboration with startups and other groups. The selected post-doc will be supported and encouraged to generate high impact publications, gain experience in supervising students and in grant writing if interested. All applicants should send an updated CV to Rumi Chunara (

A letter to the GlobalSIP Technical Program Chairs

I am a big supporter of robust peer review. However, I feel very strongly that issuing short review deadlines weakens the review process and has a negative impact on the quality of research. I had a previous experience with a machine learning conference that assigned 9 papers requiring an in-depth review and was given less than 3 weeks to complete these reviews. I immediately wrote back saying that this was infeasible and the deadline was extended by more than a week, as I recall. It was still hard to get the reviews done on time, but I managed it.

People may think I am being petty here, but I think it is important to not get caught in the dilemma of “phone it in and get it done by the deadline” and “pull some all-nighters to get it done right.”

I regret to inform you that I must resign from the Technical Program Committee for GlobalSIP because I will be unable to complete reviews required of me in the time required by the conference.

On July 12 at around 11:45 EST I was assigned 12 papers to review for the conference, for a total of around 60 pages of material (including references). The deadline given was “before July 22, 2015 (AoE)” which I take to mean approximately 8 AM EST July 22 given the location international date line. This is around 9 days to review 12 papers.

At that time I responded indicating that given my other responsibilities, I would be unable to review such a large volume of material at such short notice in the given time frame. I received no response.

On July 15 at 12:33 AM I received a second request to review the same papers with a revised deadline of “before July 25, 2015 (AoE)”. That is, 2 days after the initial assignment, the deadline was extended by 3 days.

Given my other professional and personal commitments, I will not be able to provide the level of scrutiny required to review the papers in under two weeks. As it stands, the modest extension covering 3 additional business days is not enough, especially given the delay in issuing the extension. I realize that conference submissions do not entail the same depth of review as a journal paper, but they still take time, and the review requests came quite unexpectedly.

Finally, I recognize that the delay in assignment was caused by “system glitches” (as stated in your email) and is not the fault of the PC chairs. However, the brunt of the effect is faced by the reviewers. Without any prior communication or information regarding the delay in review assignments, I am not able to juggle/move/delay other obligations at such short notice.

Anand D. Sarwate
Assistant Professor
Department of Electrical and Computer Engineering
Rutgers, The State University of New Jersey

Manual Cinema’s ADA | AVA

Chicago performance group Manual Cinema has a performance running for another week down in the Financial District at 3LD Arts and Technology Center. It’s a co-production with The Tank, a great nonprofit that supports the development of new works. The show uses 4 overhead projectors, a live band, and actors to make a dialogue-free live-created shadow-play animation, complete with sound effects. The overall aesthetics reminded me of Limbo, an independent game, although less zombie-filled. The story is about two sisters, Ada, and Ava. Ava passes away and Ada has to cope with the grief of losing someone so close to her. We go through her memories of their time together and her fantasies light and dark as she mourns and perhaps begins to heal.

I don’t have too much to say except to recommend it highly. If I had one critique it would be that I wanted the story to be more surprising, or revelatory. The medium is at the same time familiar (animation) and new (live performance). They can show so many things and use metaphor (sometimes a bit heavy-handed in a 1940s way) in ways that a conventional play with dialogue would be hard-pressed to do. I wanted to learn something new about grieving, and afterwards I felt like I hadn’t. But then again, I’m still thinking about it, so perhaps I cannot yet put what I have learned into words.

ISIT 2015 : statistics and learning

The advantage of flying to Hong Kong from the US is that the jet lag was such that I was actually more or less awake in the mornings. I didn’t take such great notes during the plenaries, but they were rather enjoyable, and I hope that the video will be uploaded to the ITSOC website soon.

There were several talks on entropy estimation in various settings that I did not take great notes on, to wit:

  • DOES DIRICHLET PRIOR SMOOTHING SOLVE THE SHANNON ENTROPY ESTIMATION PROBLEM? (Yanjun Han, Tsinghua University, China; Jiantao Jiao, Tsachy Weissman, Stanford University, United States)
  • ADAPTIVE ESTIMATION OF SHANNON ENTROPY (Yanjun Han, Tsinghua University, China; Jiantao Jiao, Tsachy Weissman, Stanford University, United States)

I would highly recommend taking a look for those who are interested in this problem. In particular, it looks like we’re getting towards more efficient entropy estimators in difficult settings (online, large alphabet), which is pretty exciting.

Javad Heydari, Ali Tajer, Rensselaer Polytechnic Institute, United States
This talk was about hypothesis testing where the observer can control the samples being taken by traversing a graph. We have an n-node graph (c.f. a graphical model) representing the joint distribution on n variables. The data generated is i.i.d. across time according to either F_0 or F_1. At each time you get to observe the data from only one node of the graph. You can either observe the same node as before, explore by observing a different node, or make a decision about whether the data from from F_0 or F_1. By adopting some costs for different actions you can form a dynamic programming solution for the search strategy but it’s pretty heavy computationally. It turns out the optimal rule for switching has a two-threshold structure and can be quite a bit different than independent observations when the correlations are structured appropriately.

Yanting Ma, Dror Baron, North Carolina State University, United States; Ahmad Beirami, Duke University, United States
The mismatch studied in this paper is a mismatch in the prior distribution for a sparse observation problem y = Ax + \sigma_z z, where x \sim P (say a Bernoulli-Gaussian prior). The question is what happens when we do estimation assuming a different prior Q. The main result of the paper is an analysis of the excess MSE using a decoupling principle. Since I don’t really know anything about the replica method (except the name “replica method”), I had a little bit of a hard time following the talk as a non-expert, but thankfully there were a number of pictures and examples to help me follow along.

Yonatan Kaspi, University of California, San Diego, United States; Ofer Shayevitz, Tel-Aviv University, Israel; Tara Javidi, University of California, San Diego, United States
This was another search paper, but this time we have, say, K targets W_1, W_2, \ldots, W_K uniformly distributed in the unit interval, and what we can do is query at each time n a set S_n \subseteq [0,1] and get a response Y_n = X_n \oplus Z_n where X_n = \mathbf{1}( \exists W_k \in S_n ) and Z_n \sim \mathrm{Bern}( \mu(S_n) + b ) where \mu is the Lebesgue measure. So basically you can query a set and you get a noisy indicator of whether you hit any targets, where the noise depends on the size of the set you query. At some point \tau you stop and guess the target locations. You are (\epsilon,\delta) successful if the probability that you are within \delta of each target is less than \epsilon. The targeting rate is the limit of \log(1/\delta) / \mathbb{E}[\tau] as \epsilon,\delta \to 0 (I’m being fast and loose here). Clearly there are some connections to group testing and communication with feedback, etc. They show there is a significant gap between the adaptive and nonadaptive rate here, so you can find more targets if you can adapt your queries on the fly. However, since rate is defined for a fixed number of targets, we could ask how the gap varies with K. They show it shrinks.

Varun Jog, University of California, Berkeley, United States; Po-Ling Loh, University of Pennsylvania, United States
The graphical model for jointly Gaussian variables has no edge between nodes i and j if the corresponding entry (\Sigma^{-1})_{ij} = 0 in the inverse covariance matrix. They show a relationship between the KL divergence of two distributions and their corresponding graphs. The divergence is lower bounded by a constant if they differ in a single edge — this indicates that estimating the edge structure is important when estimating the distribution.

Aolin Xu, Maxim Raginsky, University of Illinois at Urbana–Champaign, United States
Max gave a nice talk on the problem of minimizing an expected loss \mathbb{E}[ \ell(W, \hat{W}) ] of a d-dimensional parameter W which is observed noisily by separate encoders. Think of a CEO-style problem where there is a conditional distribution P_{X|W} such that the observation at each node is a d \times n matrix whose columns are i.i.d. and where the j-th row is i.i.d. according to P_{X|W_j}. Each sensor gets independent observations from the same model and can compress its observations to b bits and sends it over independent channels to an estimator (so no MAC here). The main result is a lower bound on the expected loss as s function of the number of bits latex b, the mutual information between W and the final estimate \hat{W}. The key is to use the strong data processing inequality to handle the mutual information — the constants that make up the ratio between the mutual informations is important. I’m sure Max will blog more about the result so I’ll leave a full explanation to him (see what I did there?)

More on Shannon theory etc. later!