IBM

About: IBM is a company organization based out in Armonk, New York, United States. It is known for research contribution in the topics: Layer (electronics) & Cache. The organization has 134567 authors who have published 253905 publications receiving 7458795 citations. The organization is also known as: International Business Machines Corporation & Big Blue.

Concept Decompositions for Large Sparse Text Data Using Clustering

Abstract: Unlabeled document collections are becoming increasingly common and availables mining such data sets represents a major contemporary challenge. Using words as features, text documents are often represented as high-dimensional and sparse vectors–a few thousand dimensions and a sparsity of 95 to 99% is typical. In this paper, we study a certain spherical k-means algorithm for clustering such document vectors. The algorithm outputs k disjoint clusters each with a concept vector that is the centroid of the cluster normalized to have unit Euclidean norm. As our first contribution, we empirically demonstrate that, owing to the high-dimensionality and sparsity of the text data, the clusters produced by the algorithm have a certain “fractal-like” and “self-similar” behavior. As our second contribution, we introduce concept decompositions to approximate the matrix of document vectorss these decompositions are obtained by taking the least-squares approximation onto the linear subspace spanned by all the concept vectors. We empirically establish that the approximation errors of the concept decompositions are close to the best possible, namely, to truncated singular value decompositions. As our third contribution, we show that the concept vectors are localized in the word space, are sparse, and tend towards orthonormality. In contrast, the singular vectors are global in the word space and are dense. Nonetheless, we observe the surprising fact that the linear subspaces spanned by the concept vectors and the leading singular vectors are quite close in the sense of small principal angles between them. In conclusion, the concept vectors produced by the spherical k-means algorithm constitute a powerful sparse and localized “basis” for text data sets.

Experimental quantum cryptography

Abstract: We describe initial results from an apparatus and protocol designed to implement quantum public key distribution, by which two users, who share no secret information initially: 1) exchange a random quantum transmission, consisting of very faint flashes of polarized light; 2) by subsequent public discussion of the sent and received versions of this transmission estimate the extent of eavesdropping that might have taken place on it, and finally 3) if this estimate is small enough, can distill from the sent and received versions a smaller body of shared random information (key), which is certifiably secret in the sense that any third party's expected information on it is an exponentially small fraction of one bit. Because the system depends on the uncertainty principle of quantum physics, instead of usual mathematical assumptions such as the difficulty of factoring, it remains secure against an adversary with unlimited computing power.

Single spin detection by magnetic resonance force microscopy

Abstract: Magnetic resonance imaging (MRI) is well known as a powerful technique for visualizing subsurface structures with three-dimensional spatial resolution. Pushing the resolution below 1 micro m remains a major challenge, however, owing to the sensitivity limitations of conventional inductive detection techniques. Currently, the smallest volume elements in an image must contain at least 10(12) nuclear spins for MRI-based microscopy, or 10(7) electron spins for electron spin resonance microscopy. Magnetic resonance force microscopy (MRFM) was proposed as a means to improve detection sensitivity to the single-spin level, and thus enable three-dimensional imaging of macromolecules (for example, proteins) with atomic resolution. MRFM has also been proposed as a qubit readout device for spin-based quantum computers. Here we report the detection of an individual electron spin by MRFM. A spatial resolution of 25 nm in one dimension was obtained for an unpaired spin in silicon dioxide. The measured signal is consistent with a model in which the spin is aligned parallel or anti-parallel to the effective field, with a rotating-frame relaxation time of 760 ms. The long relaxation time suggests that the state of an individual spin can be monitored for extended periods of time, even while subjected to a complex set of manipulations that are part of the MRFM measurement protocol.

On-line fingerprint verification

Abstract: Fingerprint verification is one of the most reliable personal identification methods. However, manual fingerprint verification is incapable of meeting today's increasing performance requirements. An automatic fingerprint identification system (AFIS) is needed. This paper describes the design and implementation of an online fingerprint verification system which operates in two stages: minutia extraction and minutia matching. An improved version of the minutia extraction algorithm proposed by Ratha et al. (1995), which is much faster and more reliable, is implemented for extracting features from an input fingerprint image captured with an online inkless scanner. For minutia matching, an alignment-based elastic matching algorithm has been developed. This algorithm is capable of finding the correspondences between minutiae in the input image and the stored template without resorting to exhaustive search and has the ability of adaptively compensating for the nonlinear deformations and inexact pose transformations between fingerprints. The system has been tested on two sets of fingerprint images captured with inkless scanners. The verification accuracy is found to be acceptable. Typically, a complete fingerprint verification procedure takes, on an average, about eight seconds on a SPARC 20 workstation. These experimental results show that our system meets the response time requirements of online verification with high accuracy.