This is an excellent book, but as other comments have pointed out, the review misrepresents its argument.
I wouldn't say Desmond is exactly sympathetic to the landlords he profiles, but he does provide a well-rounded picture of them. The main thrust of his analysis is that eviction is a major cause of an inescapable cycle of poverty, and that preventing eviction would go further to reduce poverty and inequality than nearly any other government policy.
IIRC his recommendations involve a huge bump in the federal housing voucher program, decoupling it from work requirements (since it's difficult to get a job without a permanent home), and tying federal aid to code reform in cities (simplifying and loosening the housing code so that landlords would be less likely to ignore it).
Those are great policy prescriptions: reduce eviction by giving poor people more money and more flexibility, as well as by making it cheaper and easier to offer housing to poor people. You're right that the article misrepresents that argument.
The fire has little to do with it. The stock is down because earlier this week it was priced at 100x Tesla's earning estimates for next year, and implies a 30x growth in sales by 2020.
Simply put, while Tesla's fundamentals are strong, there's a bubble in the stock. Retail investors (e.g. us) have been excitedly buying it without taking a careful look at the stock, and several institutional investors have decided it's a good time to sell and reap the rewards (i.e. fleece the excitable retail investors).
The Baird Capital report is a little more nuanced; it boils down to "Tesla have been kicking ass, and that's already priced into stock, so don't buy it now because everyone else is buying it":
http://www.forbes.com/sites/chuckjones/2013/10/02/tesla-down...
Anyway, yes, this isn't anything wrong with Tesla, it's just a natural market correction.
I wouldn't be so sure of that. Try reading the comments on any Boston globe article about this affair. Ortiz has strong support from law-and-order voters and many people in Massachusetts outside of tech; in addition to Swartz, she's been very aggressive in her prosecution of corruption and misconduct in the state government. She has a compelling back story and (at least before this) was blessed by Deval Patrick.
I think she could definitely replace Martha Coakley as state Attorney General, at minimum (Coakley also did many odious things as a prosecutor and has never, to my knowledge, paid a political price for it). And a decade from now, long after Aaron has disappeared from the press cycle, she'll make a run for Senate or Governor and his prosecution will likely be nothing more than a subsection of her Wikipedia bio. These are the sad realities of politics in Massachusetts.
That's certainly possible, but given that Massachusetts has a pretty deep bench of ambitious democrats, it's hard not to see how this is anything but a handicap.
There's something to the law-and-order point, but that would be more true in a general election, which I don't see her reaching.
Alternatively, not all voters are one issue voters. If she has done amazing things in the state and misguidedly pressed hard on Aaron, perhaps she should be judged by her whole record.
Given, I don't know much about it so I could be completely off base, but there is more to Ortiz's career than one case that was extremely misguided and ended horrifically.
Most people here have had good advice. The only thing I would add is that there's a difference between vision and delusion. Visionaries have a strong and clear focus, but are grounded enough to recognize the merits of their competition and work within the ecosystem that customers / users already enjoy. Delusionaries pour scorn on their competition (or even claim to be so revolutionary that they HAVE no competition), and try to create a walled garden or vendor lock-in with zero install base. Even Apple made iTunes for Windows.
My first job out of college was at a startup. The founders were reasonable people who were heavily influenced and invested in by a delusionary, who saw their company as a vehicle to realize his "vision" of how technology should work. As a result we never really managed to grow beyond a couple of million in revenue, and that mostly from what amounted to R&D outsourcing from large firms with technically naive execs. Said delusionary repeatedly vetoed our attempts to make our product more relevant to a wider range of customers since it would compromise his vision. I bailed out of frustration, and a year later one of our customers just outright bought the firm to acquire the founders. I heard the delusionary was very satisfied with his exit. :p
I've had to do this a few times when building simple input normalization features. The trouble with scoring based on Levenshtein distance in that case is that it improperly penalizes phrases that are significantly different in length but similar in content.
For example, let's say I was searching a database of countries for "North Korea". In my list I have:
South Africa (LD: 6)
Congo (LD: 9)
Republic of Korea (LD: 11)
...
Democratic People's Republic of Korea (LD: 28)
The actual answer (DPRK) will be pushed far to the bottom based on a naive ranking that uses LD.
The hack that's worked best for me? Rank based on the number of common two-character substrings between the source and target. It's simple, easy to build an index for, and has surprisingly great results. Its ideal use case is if you don't need to return a single absolute best result and can, say, present the three best to a human being and let them pick the match.
For the above search I'd get the following results using the two-character method:
Republic of Korea: 4
DPRK: 4
South Africa: 1
Congo: 0
The problem is that what you call the actual answer - DPRK - is subjective. By picking DPRK you need a more intelligent algorithm than fuzzy string matching because you are looking for the right answer in terms of semantic knowledge of countries, not blind character comparisons. Fundamentally, the fact that DPRK is the right answer is an accident of History and English, there really is no right answer and no algorithm can capture this insight without being told. By biasing in one direction you are sacrificing performance in some other unknown. The trick is in balancing the bias with general ability. As always.
Knowing which algorithm to use for a problem is most important; sometimes padded hamming will do, other times minhash or bitap or dice's coefficient etc are good. Each algorithm is a kind of balance between a metric and priorities - deciding what is subjectively most important in this problem. Your method for example is not as robust as Damerau Levenshtein when it comes to analyzing DNA sequences. As far as the best default string comparison, I have found dice coefficient to be ideal (your method appears to be based on similar ideas but my hunch is it's less robust).
For your example Dice's Coefficient does as well as can be expected without a concept of countries:
("dice", "South Africa", 0.125);
("dice", "Congo", 0.0);
("dice", "Republic of Korea", 0.44);
("dice", "Democratic People's Republic of Korea", 0.24)]
and by blind luck ("ort Korea"), longest common sub-sequence does even better:
[("lcs", "South Africa", 6.0);
("lcs", "Congo", 2.0);
("lcs", "Republic of Korea", 7.0);
("lcs", "Democratic People's Republic of Korea", 9.0)]
Oh, I'm under no illusions that it's a universally applicable string matcher. What it seems to be useful for is normalizing human input in situations where someone can pick the best match. It handles typical normalization transforms well (word fragments, transpositions) and is very easy to index.
In my example, DPRK is the objective best answer .. There's pretty clearly a correct match in the value domain, the challenge is to help the user find it as quickly as possible.
Use the Jaccard over a sweeping window or something.
over 5 letter windows:
TEST: A 0.4
TEST: B 0.540540540541
TEST: C 0.692307692308
over words:
TEST: A 0.555555555556
TEST: B 0.714285714286
TEST: C 1.0
Here's the Korea one over a 5 letter window:
north korea:congo 0.0
north korea:democratic people's republic of korea 0.0526315789474
north korea:republic of korea 0.111111111111
north korea:south africa 0.0
I wonder how the weights would turn out if your algo were added as a term in his formula and run through the optimization, or even applied to his sample data standalone.
Yeah, I can't really remember the last time I watched a Starz movie on Netflix. It's been nothing but TV series for me for at least a year now. Occasionally I'll watch a movie that shows up in new releases, but they've been signing a ton of premium shows lately, far more than I can watch.
I think it's a perfectly viable strategy for them to focus on series - more content per deal and a perfect match for a long-tail streaming strategy.
Disclaimer: I work for Google, this is my personal opinion, yadda yadda yadda.
I've used (and continue to use) Fastmail for many years behind a personal domain.
To call Fastmail reliable is a bit of a laugh. They've had at least three severe outages in my time there (we're talking 24+ hours without mail access). To their credit, nothing was lost in the end, however I was fuming by the end of the last one and determined to switch to Gmail. However inertia has kept me from doing so -- last time I checked it was tricky in Gmail to import a complicated set of folders like I have and retain the structure, so it's always been something I plan to get around to once I have time. Yeah right.
FWIW, I don't think there's been a significant outage since the Opera purchase. And I'm quite pleased with what I've seen of FM's beta interface.
I've done a few migrations to Gmail/Google Apps, I'm actually in the middle of one right now. It's pretty easy to retain your folder structure, especially if you have IMAP and are going to Google Apps. Nested Labels has been moved from Labs to a standard feature. I think there is a still a 40 char folder path limit, so make sure your folder names are short enough before migration, e.g., rename "Mailing Lists" to "Lists".
Google has the Migration for Microsoft Exchange tool, which does IMAP sync with any normal IMAP server. The tool is Windows-only, they used to have a web-based version but they discontinued it (WTF Google?!). It requires 2-legged OAuth, which requires Google Apps for Business. But Google Apps for Business has a free trial with no billing info required. So what I do is sign up for Google Apps Free, upgrade to Business, do the migration, and downgrade to Free.
If your mail in stored in a mail client (POP3-style), you can add Google Apps IMAP to your mail client, and drag and drop your folders/mail from the old account to Google IMAP. Be sure to hold down the Ctrl key (or whatever) to copy not move. This method also works if you are going to normal Gmail.
There is also imapsync, a Perl command line tool, and a few other tools that do things like Maildir to IMAP sync.
I wouldn't say Desmond is exactly sympathetic to the landlords he profiles, but he does provide a well-rounded picture of them. The main thrust of his analysis is that eviction is a major cause of an inescapable cycle of poverty, and that preventing eviction would go further to reduce poverty and inequality than nearly any other government policy.
IIRC his recommendations involve a huge bump in the federal housing voucher program, decoupling it from work requirements (since it's difficult to get a job without a permanent home), and tying federal aid to code reform in cities (simplifying and loosening the housing code so that landlords would be less likely to ignore it).