One of the central tenets of signal processing is the Shannon/Nyquist sampling theory: the number of samples needed to capture a signal is dictated by its bandwidth. Very recently, an alternative theory of "compressive sampling"has emerged. By using nonlinear recovery algorithms (based on convex optimization), super-resolved signals and images can be reconstructed from what appears to be highly incomplete data. Compressive sampling shows us how data compression can be implicitly incorporated into the data acquisition process, a gives us a new vantage point for a diverse set of applications including accelerated tomographic imaging, analog-to-digital conversion, and digital photography.

The Regressive Imagery Dictionary (Martindale, 1975,1990) is a content analysis coding scheme designed to measure primordial vs. conceptual thinking. Conceptual thought is abstract, logical, reality oriented, and aimed at problem solving. Primordial thought is associative, concrete, and takes little account of reality. It is the type of thinking found in fantasy, reverie, and dreams. A running tally of category occurrences is kept, and final output consists of the categories and the percentage of words in a document that were assigned to each category. This output can then be subjected to statistical analyses. The Regressive Imagery Dictionary contains about 3000 words divided into twenty-nine categories designed to measure primordial content and another set of seven categories designed to measure conceptual thought, the inverse of primordial cognition. These categories were derived from the theoretical and empirical literature on regressive thought (Martindale, 1975, 1990). The rationale behind the dictionary is that psychological processes will be reflected in the content of a text. Thus, for example, the more primordial the thought involved in producing a text, the less abstract and the more drive- and sensation-oriented words it should contain. It should be noted that the view of regressive cognition upon which construction of the dictionary was based is not a narrowly psychoanalytic one but is closer to Werner's (1948) or Goldstein's views (1939). Thus, the construct that the dictionary ultimately measures might as well be called dedifferentiated thinking as regressive or primary process thinking. For these reasons, we use the terms conceptual vs. primordial thought.

As you all may know, I watched and posted my lecture notes of the whole MIT Introduction to Algorithms course. In this post I want to summarize all the topics that were covered in the lectures and point out some of the most interesting things in them.

Actually, before I wrote this article, I had started writing an article called “The coolest things that I learned from MIT’s Introduction to Algorithms” but quickly did I realize that what I was doing was listing the topics in each article and not really pointing out the coolest things. Therefore I decided to write a summary article first (I had promised to do so), and only then write an article on really the most exciting topics.

Talking about the summary, I watched a total of 23 lectures and it resulted in 14 blog posts. It took nearly a year to publish them here. The first blog post in this series was written in August 2008, and the last in July 2009.

Snowball is a language in which stemming algorithms can be easily represented. The Snowball compiler translates a Snowball script (a .sbl file) into either a thread-safe ANSI C program or a Java program. For ANSI C, each Snowball script produces a program file and corresponding header file (with .c and .h extensions). The language has a full manual, and the various stemming scripts act as example programs.

Open source data visualization and analysis for novice and experts. Make your own data analysis schemata by visual programming or Python scripting. Extensions for bioinformatics and text mining. Comprehensive, flexible and fast.

The book aims to provide a modern approach to information retrieval from a computer science perspective. It is based on a course we have been teaching in various forms at Stanford University and at the University of Stuttgart.

The probability of the original input list being in the exact order it's in is 1/(n!). There is such a small likelihood of this that it's clearly absurd to say that this happened by chance, so it must have been consciously put in that order by an intelligent Sorter. Therefore it's safe to assume that it's already optimally Sorted in some way that transcends our naïve mortal understanding of "ascending order". Any attempt to change that order to conform to our own preconceptions would actually make it less sorted.

The project describes teaching process of multi-layer neural network employing backpropagation algorithm. To illustrate this process the three layer neural network with two inputs and one output,which is shown in the picture below, is used:

Really cool number spiral play.

Peteris Krumins has been posting his notes on MIT’s Introduction to Algorithms. The notes are valuable for anyone interested in working their way through the CLRS text and MIT Open Courseware videos.

Some of these supplemental notes are good, others only rough drafts or half-completed. Because they are designed only to provide additional examples, summaries, or explanations where the textbook is weak, they do not cover many obviously important topics. You can download a zipped version: notes-cpp-2004-12-19.zip [less than 400 KB]. -- Fred

A total reference of the C++ standard template library.

Algorithms for developing a good Othello program.

Cluster 3.0 provides a Graphical User Interface to access to the clustering routines. It is available for Windows, Mac OS X, and Linux/Unix. Python users can access the clustering routines by using Pycluster, which is an extension module to Python.

WELCOME to [wu:riddles]! This is an archive of problems I've been collecting since the Spring of 2002. They come from many places, including word of mouth, college courses, books, and job interviews for hi-tech positions. Many are even written by members