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) & Signal. 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.

Metadata search results ranking system

Abstract: A method for presenting to an end-user the intermediate matching search results of a keyword search in an index list of information. The method comprising the steps of: coupling to a search engine a graphical user interface for accepting keyword search terms for searching the indexed list of information with the search engine; receiving one or more keyword search terms with one or more separation characters separating there between; performing a keyword search with the one or more keyword search terms received when a separation character is received; and presenting the number of documents matching the keyword search terms to the end-user presenting a graphical menu item on a display. A system and method of metadata search ranking is disclosed. The present invention utilizes a combination of popularity and/or relevancy to determine a search ranking for a given search result association.

Mining the most interesting rules

Abstract: Several algorithms have been proposed for finding the “best,” “optimal,” or “most interesting” rule(s) in a database according to a variety of metrics including confidence, support, gain, chi-squared value, gini, entropy gain, laplace, lift, and conviction. In this paper, we show that the best rule according to any of these metrics must reside along a support/confidence border. Further, in the case of conjunctive rule mining within categorical data, the number of rules along this border is conveniently small, and can be mined efficiently from a variety of real-world data-sets. We also show how this concept can be generalized to mine all rules that are best according to any of these criteria with respect to an arbitrary subset of the population of interest. We argue that by returning a broader set of rules than previous algorithms, our techniques allow for improved insight into the data and support more user-interaction in the optimized rule-mining process.

Lower bounds for the partitioning of graphs

Abstract: Let a k-partition of a graph be a division of the vertices into k disjoint subsets containing m1 ≥ m2,..., ≥mk vertices. Let Ec be the number of edges whose two vertices belong to different subsets. Let λ1 ≥ λ2, ..., ≥ λk, be the k largest eigenvalues of a matrix, which is the sum of the adjacency matrix of the graph plus any diagonal matrix U such that the suomf all the elements of the sum matrix is zero. Then Ec ≥ 1/2Σr=1k-mrλr. A theorem is given that shows the effect of the maximum degree of any node being limited, and it is also shown that the right-hand side is a concave function of U.C omputational studies are madoef the ratio of upper bound to lower bound for the two-partition of a number of random graphs having up to 100 nodes.

Equivalent-accuracy accelerated neural-network training using analogue memory

Abstract: Neural-network training can be slow and energy intensive, owing to the need to transfer the weight data for the network between conventional digital memory chips and processor chips. Analogue non-volatile memory can accelerate the neural-network training algorithm known as backpropagation by performing parallelized multiply-accumulate operations in the analogue domain at the location of the weight data. However, the classification accuracies of such in situ training using non-volatile-memory hardware have generally been less than those of software-based training, owing to insufficient dynamic range and excessive weight-update asymmetry. Here we demonstrate mixed hardware-software neural-network implementations that involve up to 204,900 synapses and that combine long-term storage in phase-change memory, near-linear updates of volatile capacitors and weight-data transfer with 'polarity inversion' to cancel out inherent device-to-device variations. We achieve generalization accuracies (on previously unseen data) equivalent to those of software-based training on various commonly used machine-learning test datasets (MNIST, MNIST-backrand, CIFAR-10 and CIFAR-100). The computational energy efficiency of 28,065 billion operations per second per watt and throughput per area of 3.6 trillion operations per second per square millimetre that we calculate for our implementation exceed those of today's graphical processing units by two orders of magnitude. This work provides a path towards hardware accelerators that are both fast and energy efficient, particularly on fully connected neural-network layers.

Pinocchio: Nearly Practical Verifiable Computation

Abstract: To instill greater confidence in computations outsourced to the cloud, clients should be able to verify the correctness of the results returned. To this end, we introduce Pinocchio, a built system for efficiently verifying general computations while relying only on cryptographic assumptions. With Pinocchio, the client creates a public evaluation key to describe her computation; this setup is proportional to evaluating the computation once. The worker then evaluates the computation on a particular input and uses the evaluation key to produce a proof of correctness. The proof is only 288 bytes, regardless of the computation performed or the size of the inputs and outputs. Anyone can use a public verification key to check the proof. Crucially, our evaluation on seven applications demonstrates that Pinocchio is efficient in practice too. Pinocchio's verification time is typically 10ms: 5-7 orders of magnitude less than previous work; indeed Pinocchio is the first general-purpose system to demonstrate verification cheaper than native execution (for some apps). Pinocchio also reduces the worker's proof effort by an additional 19-60x. As an additional feature, Pinocchio generalizes to zero-knowledge proofs at a negligible cost over the base protocol. Finally, to aid development, Pinocchio provides an end-to-end toolchain that compiles a subset of C into programs that implement the verifiable computation protocol.