Vegan Caldo Verde (Portuguese Kale Soup)

I am not a vegetarian but I don’t usually cook meat when eating at home. Back in grad school I had a CSA and they would put a recipe in with every box (along with some news from the farm). One week it was a recipe for Portuguese kale soup, or caldo verde, and I remember it being delicious. Since the weather has been getting cold here I decided last night to make a batch to keep we warm during the last week of classes. When I went to the store to pick up the chorizo, however, I thought it would be more fun (and easier to share) to make a vegetarian version — that way I could use up my shiitake mushrooms too!

Vegan Caldo Verde

Vegan Caldo Verde

The proportions are not too fussy — it depends on how starchy/soupy you want it.

Vegan Caldo Verde

2 vegan chouriço (or chorizo) sausages, sliced
12 shiitake mushrooms, sliced (should cook down to same volume as chorizo)
1/2 – 1 lb potatoes, diced (chunk size based on how you want to eat it)
1 onion, sliced
2 cloves garlic
6 cups liquid (used a 1:2 mix of veg. broth and water)
1 lb kale, shredded (thin slices are more traditional, but laziness wins often)
olive oil
salt and pepper

  1. Using a soup pot or dutch oven, brown sausage slices in olive oil (you don’t need too much), remove and set aside.  Add additional oil if needed and cook shiitakes until they lose their liquid.  Remove those too.
  2. Add additional oil and cook onion (with a little salt) until translucent, then add garlic and cook until aromatic (be sure not to burn).
  3. Add potatoes and mix to coat, then add broth (make sure you cover the vegetables, add more if needed), cover, and bring to a boil. Uncover and reduce to a simmer until the potatoes are cooked through (15 min or maybe longer depending on the variety and size of your dice).
  4. (Optional) Use an immersion blender to partially puree some of the onion/potato mixture to thicken the soup.
  5. Add mushrooms and chorizo. Return to a boil.
  6. Add kale and cook down, around 5 minutes.  Be sure not to overcook the kale. Grind generous amounts of pepper and mix in.

If you are feeling fancy you can add some additional spice by adding pimenton ahumado (smoked paprika) or other spicy element.  I purposefully diluted the broth so that the mushrooms and spices in the chorizo could lend some flavor. I think you could also cook some more chorizo and garnish the bowl with a slice or two of browned chorizo in the middle. The mix of mushrooms and chorizo adds some textural interest and additional flavor, I think. Perhaps a little soy sauce in there would help up the umami.

I have no idea how many portions this makes, but I am guessing it’s at least 4-6 servings for me.  Appetites vary of course.

WIFS 2014

This week I took a quick jaunt down to Atlanta to attend part of WIFS 2014 (co-located with GlobalSIP 2014). Kamalika and I were invited to give a talk on differential privacy and machine learning, based on our IEEE Signal Processing Magazine article. I’ve uploaded the slides of the tutorial to my website and we’re planning on making a video (audio over slides) version for SigView as well as on YouTube.

Much like last year, GlobalSIP had a somewhat disjointed, semi-chaotic feel (exacerbated by tiredness, I am sure) — it’s really a collection of semi-interacting workshops in the same space, and I knew people in several of the other workshops. Since I was there for a day and giving a tutorial at WIFS, I decided to stick with WIFS for the day. To give a sense of how confusing it all was, here’s a picture of the guide to deciphering the program book:

Overly-complicated rules for encoding sessions

Overly-complicated rules for encoding sessions

The keynote for GlobalSIP was given by Vince Poor on information-theoretic privacy via rate distortion (this is the work with Lalitha). Vince did a good job of not over-IT-ing it I think, which was good because the audience was pretty diverse and it’s not clear that many of the people there had even taken a course on information theory. This seems to be the big challenge in multi-disciplinary conferences like GlobalSIP (or large signal processing conferences in general) — everyone is in signal processing, but it’s a big tent and it’s hard to reach everyone.

Min Wu was the keynote speaker for the WIFS workshop on the day I attended. Her talk, on “Exploring Power Network Signatures for Information Forensics” was about how to glean information from power fluctuations in networks, or electronic network frequency (ENF). Different processes or operations have different power demands — by matching these signatures to an observed signal (e.g. a video), one can make inferences about the time/location/integrity of the data. For example, were the audio and visual tracks in a video taken at the same time or merged later? This whole area is quite interesting, and while I was sort of aware of this work I hadn’t really read up on much of it.

Perhaps it was the end of the semester kicking in, but I sort of took terrible notes on most of the talks and poster sessions at the conference, so I can’t really write coherently about the papers I saw. Unfortunately I had to run back to teach the penultimate lecture in my class. I guess now that I have a “real job” this is going to be the way it works from now on. Kind of sad, really.

Feature Engineering for Review Times

The most popular topic of conversation among information theory afficionados is probably the long review times for the IEEE Transactions on Information Theory. Everyone has a story of a very delayed review — either for their own paper or for a friend of theirs. The Information Theory Society Board of Governors and Editor-in-Chief have presented charts of “sub-to-pub” times and other statistics and are working hard on ways to improve the speed of reviews without impairing their quality. These are all laudable. But it occurs to me that there is room for social engineering on the input side of things as well. That is, if we treat the process as a black box, with inputs (papers) and outputs (decisions), what would a machine-learning approach to predicting decision time do?

Perhaps the most important (and overlooked in some cases) aspects of learning a predictor from real data is figuring out what features to measure about each of the inputs. Off the top of my head, things which may be predictive include:

  • length
  • number of citations
  • number of equations
  • number of theorems/lemmas/etc.
  • number of previous IT papers by the authors
  • h-index of authors
  • membership status of the authors (student members to Fellows)
  • associate editor handling the paper — although for obvious reasons we may not want to include this

I am sure I am missing a bunch of relevant measurable quantities here, but you get the picture.

I would bet that paper length is a strong predictor of review time, not because it takes a longer time to read a longer paper, but because the activation energy of actually picking up the paper to review it is a nonlinear function of the length.

Doing a regression analysis might yield some interesting suggestions on how to pick coauthors and paper length to minimize the review time. This could also help make the system go faster, no? Should we request these sort of statistics from the EiC?

Transactions on Signal and Information Processing Over Networks: now accepting papers

I’m an Associate Editor for the new IEEE Transactions on Signal and Information Processing Over Networks, and we are accepting submissions now. The Editor-In-Chief is Petar M. Djurić.

The new IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data.

To submit a paper, go to Manuscript Central.

Topics of interest include, but are not limited to the following:

Adaptation, Detection, Estimation, and Learning

  • Distributed detection and estimation
  • Distributed adaptation over networks
  • Distributed learning over networks
  • Distributed target tracking
  • Bayesian learning; Bayesian signal processing
  • Sequential learning over networks
  • Decision making over networks
  • Distributed dictionary learning
  • Distributed game theoretic strategies
  • Distributed information processing
  • Graphical and kernel methods
  • Consensus over network systems
  • Optimization over network systems

Communications, Networking, and Sensing

  • Distributed monitoring and sensing
  • Signal processing for distributed communications and networking
  • Signal processing for cooperative networking
  • Signal processing for network security
  • Optimal network signal processing and resource allocation

Modeling and Analysis

  • Performance and bounds of methods
  • Robustness and vulnerability
  • Network modeling and identification
  • Simulations of networked information processing systems
  • Social learning
  • Bio-inspired network signal processing
  • Epidemics and diffusion in populations

Imaging and Media Applications

  • Image and video processing over networks
  • Media cloud computing and communication
  • Multimedia streaming and transport
  • Social media computing and networking
  • Signal processing for cyber-physicalsystems
  • Wireless/mobile multimedia

Data Analysis

  • Processing, analysis, and visualization of big data
  • Signal and information processing for crowd computing
  • Signal and information processing for the Internet of Things
  • Emergence of behavior

Emerging topics and applications

  • Emerging topics
  • Applications in life sciences, ecology, energy, social networks, economic networks, finance, social sciences, smart grids, wireless health, robotics, transportation, and other areas of science and engineering

Rutgers ECE is hiring!

Faculty Search, Department of Electrical and Computer Engineering, Rutgers University.

The Department of Electrical and Computer Engineering at Rutgers University anticipates multiple faculty openings in the following areas: (i) High-performance distributed computing, including cloud computing and data-intensive computing, (ii) Electronics, advanced sensors and renewable energy, including solar cells and detectors (bio, optical, RF) and, (iii) Bioelectrical engineering.

We are interested in candidates who can combine expertise in these areas with cyber-security, software engineering, devices, embedded systems, signal processing and or communications. In addition, we particularly welcome candidates who can contribute to broader application initiatives such as biomedical and health sciences, smart cities, or sustainable energy.

Outstanding applicants in all areas and at all ranks are encouraged to apply. Suitable candidates may be eligible to be considered for Henry Rutgers University Professorships in Big Data as part of a University Initiative.

Excellent facilities are available for collaborative research opportunities with various university centers such as the Wireless Information Network Laboratory (WINLAB), Microelectronics Research Laboratory (MERL), Institute for Advanced Materials, Devices and Nanotechnology (IAMDN), Center for Advanced Infrastructure and Transportation (CAIT), Rutgers Energy Institute (REI), and the Center for Integrative Proteomics Research, as well as with local industry.

A Ph.D. in a related field is required. Responsibilities include teaching undergraduate and graduate courses and establishing independent research programs. Qualified candidates should submit a CV, statements on teaching and research, and contacts of three references to this website. The review process will start immediately. For full consideration applications must be received by January 15, 2015.

Questions may be directed to:

Athina P. Petropulu
Professor and Chair
Department of Electrical and Computer Engineering
Rutgers University
athinap @ rutgers.edu.

EEO/AA Policy:
Rutgers is an Equal Opportunity / Affirmative Action Employer. Rutgers is also an ADVANCE institution, one of a limited number of universities in receipt of NSF funds in support of our commitment to increase diversity and the participation and advancement of women in the STEM disciplines.

Embargoes and the process of science

Last week I attended the National Academies Keck Futures Initiative (NAKFI) Conference on Collective Behavior, which was really a huge amount of fun. I learned a ton of science (and that I basically know nothing about science — or rather, there is soooo much science to know), and had some very interesting discussion about… stuff. Why am I so cagey? Because the details of discussions at the conference are officially embargoed until the report is issued by the National Academies in spring.

This embargo concept is not entirely new to me, but coming as I do from a tribe that tries to post things on ArXiV as fast as possible, the idea that one should keep mum for a few months feels a bit strange. It makes a lot of sense — people presented posters on work in progress or partial results that they were still working on, and without an embargo there is a potential danger of getting scooped, which could inhibit the free and open sharing of ideas. I certainly felt more comfortable talking about (possibly half-baked) future research ideas, although that was primarily because I didn’t think the ecologist I was conversing with would care as much about stochastic gradient methods.

Embargoes seem to be the norm in Science because of… Science… and Nature… and PNAS. If you have a high-profile article to appear in one of those fancy journals, they want the credit for having chosen it/are the venue in which it appeared. Slapping up your preprint on ArXiV is not on, since it bursts the balloon (although Nature says “[n]either conference presentations nor posting on recognized preprint servers constitute prior publication”). This is newsworthy science, and there’s a relationship between the press, the academic press, and the research community that has been discussed at length.

I came across a blog called Embargo Watch that looks to see how the media/reporters breach the embargoes imposed by the publisher. Indeed, if you look at various embargo policies (even PLoS has one!) show that the embargo thing is really about controlling the news media’s description of the article prior to publication. There’s been a longstanding (un?)healthy debate about the value of embargoes. Personally, I’d prefer to see a someone who studies communication and science studies (like Marisa) do a more critical evaluation of the role of embargoes in enforcing particular constructions and interpretations of the scientific process, the role of power and control, and how researchers propagate and resist the tensions inherent in publishing in high-impact journals.

Regardless, I am following the embargo and keeping quiet while trying to process everything I learned last week. I guess I am glad the ArXiV is there for me — it’s a little more my speed. Actually, it may be a bit too speedy, but it works for now. I think people working in engineering, computer science, and mathematics might find the notion of an embargo somewhat puzzling, as I did. Does this concept even make sense in those fields?

Tracks: Nothing Gets Done

  1. I Thought About You (The Four Freshmen)
  2. Tenere (Bombino)
  3. We Only Come Out At Night (Sugar Stems)
  4. Never Work For Free (Tennis)
  5. Crueler Kind (San Fermin)
  6. One By One (Deep Sea Diver)
  7. Everything (FM Belfast)
  8. I Wish You (CAPSULE)
  9. The Natural World (CYMBALS)
  10. One Second Of Love (Nite Jewel)
  11. Diamond Days (HABITATS)
  12. Turn It Around (Lucius)
  13. Bucolismo (Garota Suecas)
  14. Mercy Mercy Me (Marvin Gaye)
  15. Dark Comedy Morning Show (Open Mike Eagle feat. Toy Light)
  16. A Ho A Hand (FAMY)
  17. I’ll See You In My Dreams (Django Reinhardt Trio)