Ingredients:

• 1 tsp groundnut oil (or vegetable oil)
• 2 medium onions, roughly chopped
• 2cm piece of ginger, finely chopped
• 2 cloves garlic, crushed/finely chopped/whatever
• 4 small carrots, halfed lengthwise and finely sliced
• 1 tsp garam masala
• 1 tsp curry powder
• 1 tsp dried coriander leaves
• 1/2 tsp ground cinnamon
• 1/2 tsp turmeric
• 2 chillies (or just the one if you’re not wanting it too hot) finely sliced
• 75g Greek yoghurt
• 2 large (bonless and skinless) chicken thighs (approx 250g), chopped into small 2cm-ish chunks
• 800ml chicken stock
• 50g (uncooked) basmati rice
• cornflour to thicken (optional)

Start by heating the oil in a medium-large pan, and sweat off the onions for about 5 minutes. Add the garlic, ginger, carrot and half of the chilli, and cook for 2-3 more minutes. Transfer about 2/3-3/4 of the mixture to a small blender, and with a bit of water added, blend until you get a very smooth paste. Return the paste back to the pan, add all of the spices, and cook for a couple more minutes. Stir in the yoghurt (a small amount at a time, so that it doesn’t split), followed by the chicken and cook slowly until not-quite bubbling, before adding the rice and stock. Cook for ten minutes, or so, until the rice and chicken is cooked. If the sauce is too thin, add a couple of teaspoons of cornflour let down with a small amount of warm water, and stir into the soup.

Serves 4, and if you’re counting, each serving weighs in at a fairly lean 225 calories

Download:
1440 x 900

Skip to September, and with the end-of-month deadline nearing, I’m putting the finishing touches to the project report, now titled: ‘The Social Network in the Music Blogosphere’. It’s an exploration of the relationships between blogs and the artists that they write about, using network theory, with an analysis of some subject discovery and classification methods. There’s a chunk of Python code in the appendices (I used a lot of NetworkX), as well as some subject-based clusterings represented as dendrograms. If that sounds like your bag, you can get your copy of my project report here:

Download PDF: The Social Network in the Music Blogosphere (3.6MB)

The network dataset of blog-artist relationships is also available:

blog-artist_network.tar.gz (301K)
blog-artist_network.zip (299K)

It contains the following files:

artists.txt: id, and normalized and denormalized versions of all artist names

ARTIST_ID  ARTIST_NAME  DENORM_ARTIST_NAME

blogs.txt: id and name of each blog

BLOG_ID  BLOG_NAME

blog-artist.txt: the edges between a blog and an artist, and the weighting given to the relationship

BLOG_ID  ARTIST_ID  WEIGHT

I’ve got a number of bags that I like, but I wouldn’t want to chuck a camera in them without some sort of protection. Inside a bag, if not held in place, lens caps can come off and lenses can get scratched, and things get rearranged so that it becomes difficult to find what you’re looking for. And that’s not to mention all the casual bumps that a bag will inevitably get when worn and on the move.

Both Billingham and Domke make stand-alone inserts for their really rather lovely but sadly aspirationally priced bags, for around £30-40. There’s something in me that finds spending that amount on the padding alone a bit too much. Driven by this, and inspired by a number of DIY camera bag blog posts from people who have attempted a similar thing, I took a trip to the rather excellent Cass Art in Islington to see what I could find to help me. I left with a couple of 50cmx50cm neoprene foam sheets (£4 each) and a 2.5m pack of self-adhesive velcro (£7).

Back home, I got a small-ish bag – at least big enough for a digital SLR an a lens, but not much more. A nice sized man bag, effectively. My aim was to create an adaptable design for an insert that would create enough room for my SLR with a standard zoom lens attached, and whatever extra room I could make. After measuring approximate sizes, I made a rough template for an open-ended boxed rectangle, and cut out panels from the neopreme foam. I then attached them to each other with duck tape (I already had this) and used extra to create flaps to which I attached two lengths of velcro (comb-side). Two strips of the fuzzy side of velcro were added to the other panel, so I ended up with something looking a bit like this:

I’ve intentionally not mentioned any measurements here, since it’s very much dependant on the bag, although I found that a depth of around 10cm would be enough for an SLR. Dividers can be made in a similar way. A velcro-flanked square of foam can be strengthed with wire-reinforced cardboard strips. I inserted thickish garden wire into the cardboard’s corrogated ‘pipes’ before attaching the strips to the foam with more duct tape. This provides more vertical strength to the divider to support the weight of an SLR with a lens attached.

The following is the end result, shown from the top:

And from the front…:

I managed an insert and divider from a single sheet of foam, about 60cm of velcro tape, and a bit of duck tape, so total the cost is about £7. I’m now thinking that my Freitag bag will be getting a similar treatment shortly.

I’d been thinking about making a DIY laptop stand for a few weeks, but a glut of reasonably strong cardboard from a recent trip to IKEA spurred me on to make it for real. In addition to cardboard, I also used double-sided tape, and brown packing tape.

It’s a really really simple design. Here’s the template:

I created two of everything here, and stuck them together with double-sided tape, so that each piece was twice-thickness (I guess 5-6mm thick in total). I also re-enforced the cardboard with packing tape – just wrapping it around and trimming it. This gave me two side panels, a front panel and a slightly thicker back panel.

Cuts were made where the dotted lines are shown on the template; about 1.5cm from the front and 2cm from the back on the side panels. (N.B. the ‘front’ is the lowest part on the side panel). All cuts were 2cm deep and wide enough for the thickness of the panels to slot together without a squeeze. On the front panel (the slightly thinner of the two 32cm long pieces, the cuts are made about 1cm from each end, and about 2cm from each end on the back panel. When assembled, this creates a slight tapering towards the back panel, rather than a more flimsy square.

This was designed for a 15″ Macbook Pro with room for an Apple bluetooth keyboard to nuzzle in underneath. I guess it could be prettier, but it does the job and disammembles and reassembles easily for going in and out of storage.

This assumes a simple webapp, living on a server on the local machine, sitting under the context called ‘context’, responding to the URL:

http://localhost:8080/context/hello/world?foo=bar

-Dsun.net.client.defaultConnectTimeout=<CONNECT_TIMEOUT>
-Dsun.net.client.defaultReadTimeout=<READ_TIMEOUT>

In future, I’d be more inclined to use something like the Apache HttpCommons Client which provides a full-featured HTTP client.

• a pack of lardons
• two leeks, sliced into small rings
• an onion, roughly chopped
• 1/2 swede, peeled and roughly cubed
• a pack of about six good sized skinless, boneless chicken thighs, chopped into bite-sized pieces
• three cloves garlic, sliced
• handful of parsley, roughly chopped
• 1 tsp turmeric
• 1/2 tsp cayenne pepper
• 1 tsp ground ginger
• a bottle of white wine
• couple of handfuls of frozen peas
• couple of cups of chicken stock
• salt and pepper to taste
• 6-8 dumplings made from packet mix

Brown off the lardons in a hot stock pot, before adding the leeks, onion, garlic and swede. You don’t need extra oil, just let them cook in the tasty bacon fat until they’ve softened up a bit, for about 5 minutes or so. Add the chicken, cayenne pepper, ground ginger, and turmeric, and stir about in the mix before adding in the bottle of white wine and frozen peas. Top up with the chicken stock until it looks like there will be enough liquid for about half an hour’s cooking, and sit the doughy dumplings made from the mix on the top.

Cook for half an hour and when almost done, throw in the chopped parsley and season. This can be served on it’s own in a bowl, or with a generous serving of mashed potatos should make enough for four people.

I’ve offered the idea that good recommendations lie on a scale between Obvious and Interesting. Taken to their extreme, the full line could actually run from Boring through to Random, with Obvious and Interesting somewhere in between. ‘Boring’ recommendations could be said to exist where the Advisor’s Preference Set is entirely made up of the Common Set (i.e. the Advisee knows at least as much as the Advisor). ‘Random’ recommendations could be said to exist where there is no Common Set at all (i.e. A disjoint B )

I’ve also attempted to explain my thoughts on the kinds of relationships that could exist between an Advisor and an Advisee, and ways in which they could be elaborated. I think I’ve done that, albeit in a not very scientific way. So there’s a lot of room for refinement, and there are some gaps to be filled (specifically around calculating variation within a set of preferences and analysing effects of different weightings of those preferences) and some of my assumptions are a little more tenuous than maybe they should be, but I think this could provide the basis for some interesting results.

I’m also keen to explore the possibility that subjective relationships (rather than behavioural relationships) between Things would produce better/more interesting recommendations and routes for discovery. For my MSc project, I’m intending to focus on the Music Domain, analysing music blogs to deduce relationships between artists to augment recommendations from existing services such as last.fm.

Does all of this just mean that the underlying rule is to pair the Advisee to the Advisor with biggest Preference Set? And how does that relate to what was concluded earlier about the similarity of the two sets; the Jaccard co-effient. Taking an extreme scenario illustrated here:

Advisor (A) has a Preference Set of a large order of magnitude greater than the Advisee (B). Assuming that there’s a similar variance within the two sets, and that the previous assertions were correct, then this would clearly be approaching a Utopian case.

In reality we would find it difficult to assume so much about such a small sample relative to the larger one. It would be much better if we could transform the smaller set in to a larger sample on the basis of predicted future recommendations. Such as in the following diagram, where the dashed line shows the expansion of the Advisee’s original Preference Set to the Advisee’s Predicted Preference Set (B).

However, this makes the assumption that growth would be uniformly distributed out from the current Preference Set, and that the coupling of Advisor (A) with Advisee (B) was a good one to begin with and one that holds throughout the introduction of future recommendations.

My assumption would be that the variation within the Preference Sets holds the solution for this. Taking the difference in variation in the Common Set (A∩B) and the Recommendation Set (A—B) as ‘pull’ factors towards the Advisor and the difference in variation between the Common Set (A∩B) and B—A as ‘push’ factors away from the Advisor, we could infer growth of the Common Set with a the centre weighted towards or away from the Advisee. A greater pull could be shown as in the following diagram:

Whilst a greater push could be illustrated as:

The Jaccard co-effient could then be applied to work out the predicted similarity between the two sets. And from this, predict whether the original relationship is likely to provide more interesting, average, or more obvious recommendations.

Formally this can be represented as:

A∩PPS(B) / A∪PPS(B)

where: PPS(X)  = weighted Predicted Preference Set of X

Summary: Between Obvious and Interesting