The limits of university power

Keyser Quadrangle in Spring at the Johns Hopkins University (Photo credit: Wikipedia)

I am writing this from a hotel room in Washington DC, but this afternoon was spent in Baltimore, watching the Ravens play their last home game this season.

(The eldest daughter being a big Ravens fan this was a must. It was not a great game, though the atmosphere improved dramatically in the final quarter as the Ravens went ahead.)

One lasting impression is going to be of the depressed nature of Baltimore – this really does seem to be a city that was dying on its feet. For sure, I had faithfully watched every last episode of The Wire but it was still a surprise to see a city that only appeared run-down (one daylight rail journey, one taxi journey and one light rail trip are hardly comprehensive, but it is still remarkable that nowhere seemed to shine with prosperity).

But one thing struck me more than anything else – and that was that Baltimore appears to be the living rebuttal of the idea that high quality higher education could and should be at the core of urban economic growth and renewal.

Baltimore houses what is undoubtedly one of the world’s greatest scientific universities – Johns Hopkins – and travelling from Penn Station to the Ravens’ stadium also took us past buildings of Baltimore University and the University of Maryland. What there was not was the sense that Baltimore was a city thriving on the scientific and medical spin-offs and the thriving cultural and knowledge economies that we should expect.

 

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The death of Dr Dobb’s

It seems that Dr Dobb’s has come to the end of its useful life – and is to be, in effect, placed in an online casket.

To be honest it is some time since I read it – I used to like the magazines as they were something that you could take out at any time and flick through – you just cannot do that with online media.

Like Byte before it Dr Dobbs was a magazine for those who had an interest in computing that stretched beyond what one operating system on one sort of computer could do. And like Byte before it, it seems it has been brought to its knees by the disruptive technology it for so long championed.

The statement about the “sunset” of Dr Dobb’s contains a warning for all those who say that publications should just face the facts and go online:

four years ago, when I came to Dr. Dobb’s, we had healthy profits and revenue, almost all of it from advertising. Despite our excellent growth on the editorial side, our revenue declined such that today it’s barely 30% of what it was when I started. While some of this drop is undoubtedly due to turnover in our sales staff, even if the staff had been stable and executed perfectly, revenue would be much the same and future prospects would surely point to upcoming losses. This is because in the last 18 months, there has been a marked shift in how vendors value website advertising. They’ve come to realize that website ads tend to be less effective than they once were. Given that I’ve never bought a single item by clicking on an ad on a website, this conclusion seems correct in the small.

Simulating global and local memory on OVP

The Simons’ BASIC start-up screen (Photo credit: Wikipedia)

Had a good meeting with my PhD supervisor today: he was in London – I didn’t have to make a flying visit to York.

So the next steps with my OVPsim Microblaze code is to model global and local memory – by default OVPsim treats all memory as local, mapping the full 32-bit address space and sparsely filling that as needed. I have imposed an additional constraint of only mapping a few pages, but these are still all “local”: so while the code takes time to execute, what is in effect, a FIFO page replacement algorithm, there is no time for page loads.

The way round this seems to be to build my global memory as a memory-mapped peripheral device – I can then impose time delays on reads and writes.

But I suppose I am writing this blog instead of writing that code…

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Predicting political outcomes with neural nets

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The application of neural networks to politics has long been a personal interest – but until today that’s all it has been – an interest, not anything pursued practically.

My initial inspiration – more than twenty years ago – was the simple insight that, when canvassing for votes you often know the answer of the voter before they open the door and certainly in the majority of cases – at least, I thought – certainly before any words were exchanged: the brain of an experienced canvasser was able to compute a likely outcome from looks alone. The task, then, was to find some data that could substitute for that and speedily identify supporters using the non-linear calculations that neural nets, as analogues of the canvasser’s brain, made.

I still think that is a valid idea – though in the era of “big data” other approaches are now being used. But it wasn’t the one I experimented with today.

Working as a political consultant the thing I am often asked by clients (and other consultants) is: who do I think is going to win the next UK election? The truthful answer is “I don’t know” – there has never been a moment like this in my lifetime. But that is not a particular helpful answer – so is there something different we can do that goes beyond the uniform swing projections that anyone with an opinion poll and wikipedia could manage?

This is where a neural net might help – deep in the demographics perhaps there are non-linear relationships that will point us to the result in individual constituencies?

My initial experiments – with Christmas coming work is a little quieter and so I had a bit of time to mess about with some C code looking at this – suggest that if the relationships are there neural nets are not going to reveal them without effort.

I picked Scotland as my testing ground: the boundaries there have been unchanged since 2005 so that should give us two full sets of elections to test with and – because the insurgent populist party there, the SNP, is well established, it is a bit more data-rich than the English and Welsh experience with UKIP.

If I am able to make more progress with the model – perhaps as I read more of the really rather good Practical Neural Network Recipes in C++ – long since out of print but a great tour round the issues – I may write some more.

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