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.

Coupled quantum dots as quantum gates

Abstract: We consider a quantum-gate mechanism based on electron spins in coupled semiconductor quantum dots. Such gates provide a general source of spin entanglement and can be used for quantum computers. We determine the exchange coupling $J$ in the effective Heisenberg model as a function of magnetic $(B)$ and electric fields, and of the interdot distance $a$ within the Heitler-London approximation of molecular physics. This result is refined by using $\mathrm{sp}$ hybridization, and by the Hund-Mulliken molecular-orbit approach, which leads to an extended Hubbard description for the two-dot system that shows a remarkable dependence on $B$ and $a$ due to the long-range Coulomb interaction. We find that the exchange $J$ changes sign at a finite field (leading to a pronounced jump in the magnetization) and then decays exponentially. The magnetization and the spin susceptibilities of the coupled dots are calculated. We show that the dephasing due to nuclear spins in GaAs can be strongly suppressed by dynamical nuclear-spin polarization and/or by magnetic fields.

A Programming Language

Abstract: The paper describes a succinct problem-oriented programming language. The language is broad in scope, having been developed for, and applied effectively in, such diverse areas as microprogramming, switching theory, operations research, information retrieval, sorting theory, structure of compilers, search procedures, and language translation. The language permits a high degree of useful formalism. It relies heavily on a systematic extension of a small set of basic operations to vectors, matrices, and trees, and on a family of flexible selection operations controlled by logical vectors. Illustrations are drawn from a variety of applications.

Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts

Abstract: Sentiment analysis of short texts such as single sentences and Twitter messages is challenging because of the limited contextual information that they normally contain. Effectively solving this task requires strategies that combine the small text content with prior knowledge and use more than just bag-of-words. In this work we propose a new deep convolutional neural network that exploits from characterto sentence-level information to perform sentiment analysis of short texts. We apply our approach for two corpora of two different domains: the Stanford Sentiment Treebank (SSTb), which contains sentences from movie reviews; and the Stanford Twitter Sentiment corpus (STS), which contains Twitter messages. For the SSTb corpus, our approach achieves state-of-the-art results for single sentence sentiment prediction in both binary positive/negative classification, with 85.7% accuracy, and fine-grained classification, with 48.3% accuracy. For the STS corpus, our approach achieves a sentiment prediction accuracy of 86.4%.

Mining Generalized Association Rules

Abstract: We introduce the problem of mining generalized association rules. Given a large database of transactions, where each transaction consists of a set of items, and a taxonomy (is-a hierarchy) on the items, we find associations between items at any level of the taxonomy. For example, given a taxonomy that says that jackets is-a outerwear is-a clothes, we may infer a rule that “people who buy outerwear tend to buy shoes”. This rule may hold even if rules that “people who buy jackets tend to buy shoes”, and “people who buy clothes tend to buy shoes” do not hold. An obvious solution to the problem is to add all ancestors of each item in a transaction to the transaction, and then run any of the algorithms for mining association rules on these “extended transactions”. However, this “Basic” algorithm is not very fast; we present two algorithms, Cumulate and EstMerge, which run 2 to 5 times faster than Basic (and more than 100 times faster on one real-life dataset). Finally, we present a new interest-measure for rules which uses the information in the taxonomy. Given a user-specified “minimum-interest-level”, this measure prunes a large number of redundant rules; 40–60% of all the rules were pruned on two real-life datasets.

Equivalent Circuit Models for Three-Dimensional Multiconductor Systems

Abstract: Multiconductor or multiwire arrangements find many applications in electronic systems. Examples are interconnections between digital circuits or integrated microwave circuits. Equivalent circuit models are derived here from an integral equation to establish an electrical description of the physical geometry. The models, which are appropriately called partial element equivalent circuits (PEEC), are general in that they include losses. Models of different complexity can be constructed, to suit the application at hand.