Unusual for an on-the-record Park Service employee, Schullery does have his own opinions. There are friends and enemies. In the twenty years since this book was originally published, the world of Yellowstone has changed. Its greatest threat does not come from extractive and exploitative corporations, nor a complicitous government, nor even from the dingbat Congressional delegation attached to its home states. Rather, it comes from the citizenry that most professes to cherish the resource. People like you and me who are loving it to death, slicing subdivisions into critical habitat in a fevered quest for their piece of the West.

Ahhh, but Yellowstone belongs as much to the sunburned bricklayer from Ohio and his sausage-legged wife as it does to me. Schullery understands that this land was made for you and me, no matter how difficult the mediation between user groups. And as the title of the book reminds us, Yellowstone is indeed on mountain time. All the snowmobilers and timber company executives and tree-hugging commies who visit the park this year will be rotting in their graves in a hundred years but Yellowstone will endure in spite of us.

This is an engaging and relaxing read.

Basement

Then to spy
in an unused cellar spot

Under a bulb fixture
long since jury-rigged
in deal cast-off

And between oil tank
and salt-scalloped stone wall

--Between a ruck
and a carapace--

A tiny skeleton--mouse.

My instinct:
to trip-tipsy the dark

--As even the Dean
and Cuchulain might--

fantastic.

[My opinion is that Muldoon peaked in 1990 with his tour de force, MADOC--A Mystery, the book-length poem and astounding work of the imagination. MADOC was large, confounding, mysterious, lyrical, and sui generis (really). Yet many readers/reviewers did not appreciate it. Since that work, Muldoon seemingly has tried to obtain such appreciation by offering more manageable fare--featuring topical themes, easy wit, sentiment, form, and rhyme (not to mention all those pretty names of Irish places). He has served up plates of warm apercus. If that is your thing--fine. He is terribly accomplished--his more recent poems, including those of Moy Sand and Gravel, sparkle with polish and panache. But I will take the polar edge of the creative MADOC thankyouverymuch.]

This book (published in the UK as "Muffin and the Keeper)" is a well illustrated fantasy story about a young child recovering the family treasure.

There are three books in the series so far (others are "Muffin and the Urgs" and "Muffin and the Mouth of Doom". There's also an illustrated write-up of Warren's backstory, "Chronicle of the Three Counties" (note Counties, not Countries, Amazon!) which illustrates the considerable amount of thought and potential that have gone into Warren's fantasy world of Fumoria.

The stories are great for young children - my daughters are 3 and 6 and the elder one has continued to revisit it as she has learned to read. The "Chronicle" book is less suitable, despite high production values - an encyclopedia doesn't play so well, even of a fantasy world.

It's difficult to find this kind of well thought out, consistent fantasy for youngsters of this age. If you particularly liked the Hobbit, but your children aren't quite old enough for it, you'll find Warren's Muffin books a good primer.

Some reviews from the back of Muffin and the Mouth of Doom:

"The voices of Tolkien and Peake breathe o'er Fumoria" - London Times

"... brilliant: deep, detailed, humourous, teeming with characters, societies and languages" - SF Britain

"... an elaborate and impressive Tolkienesque fantasy, illustrated with great accomplishment and masses of detail" - 'Mother Goose' judging panel

The book starts out with introducing imaging, images, image operations etc. The authors are well aware that this introduction can not replace the vast literature in the area, it has to be basic, but it is well done. It is interesting to note that the necessity of knowing the characteristics of the image (depending on sensor technology) and the (imaging) experiment is better described here than in conventional image processing literature. A good analysis depend on these factors, right? The main application area magnetic resonance imaging (MRI) is described, but of course it becomes basic too. The used algorithm is principal component analysis (PCA). It is well described, using theory, examples and graphics. I especially appreciate the chapter on pre-processing techniques, with coupling to image and experiment characteristics.

After these introductions, it is time for MIA. The corner stones of MIA; visualisations, data reduction and iterative model work, are described and used in many examples. To be more specific, local models can be created, different matrices can be used, residuals are analysed, a multitude of visualizations are used, and the examples cover many applications (some are a little strange, for example hard bread (kn„ckebr"d)). The main example is an MRI example. The result is a segmentation, or an understanding of the data which helps in further experiments.

The book includes many examples and also high level language code, so it is possible to understand everything in depth if desirable. My own experience is that MIA is quite multi-disciplinary, it demands several experts (sensor technology, image processing, possibly statistics and especially the application expert!) to be successful. Without no do doubt, the book fills the role of creating a common platform for any such project. This book is to my knowledge the only dedicated one presently. There is an historical overview of work in the area which I appreciate very much. The references are adequate, and there are pointers to relevant journals.

Unfortunately, the book can not escape two crucial questions for MIA: What software should be used? After reading about MIA, I would expect a chapter on software. Just think of all the visualisation needed, and what you would like to have in the future. To write your own software would be a never ending project, I know, I have done it. What does the created images show? This is the curse of PCA (and factor analysis in general). Any application expert would wonder, and it therefore becomes a crucial question. The only way I know (and used) to explain this is to use synthetic data, of which you have control, and it is a good exercise to model the image characteristics. This approach is not used, and I can understand that. It puts even more demands on the used software.

Finn.Pedersen (formerly Uppsala University, Sweden)