Giant Magnons and Spiky Strings on S^3 with B-field

We study solutions for a rotating string on S^3 with a background NS-NS B-field and show the existence of spiky string and giant magnon as two limiting solutions. We make a connection to the sine-Gordon model via the Polyakov worldsheet action and study the effect of B-field. In particular, we find the magnon solution can be mapped to the excitation of a fractional spin chain. We conjecture a B-deformed SYM to be the gauge theory dual to this background.Comment: 22 pages, 3 figures, more references adde

Domain-Based Predictive Models for Protein-Protein Interaction Prediction

Protein interactions are of biological interest because they orchestrate a number of cellular processes such as metabolic pathways and immunological recognition. Recently, methods for predicting protein interactions using domain information are proposed and preliminary results have demonstrated their feasibility. In this paper, we develop two domain-based statistical models (neural networks and decision trees) for protein interaction predictions. Unlike most of the existing methods which consider only domain pairs (one domain from one protein) and assume that domain-domain interactions are independent of each other, the proposed methods are capable of exploring all possible interactions between domains and make predictions based on all the domains. Compared to maximum-likelihood estimation methods, our experimental results show that the proposed schemes can predict protein-protein interactions with higher specificity and sensitivity, while requiring less computation time. Furthermore, the decision tree-based model can be used to infer the interactions not only between two domains, but among multiple domains as well

Domain-Based Predictive Models for Protein-Protein Interaction Prediction

Protein interactions are of biological interest because they orchestrate a number of cellular processes such as metabolic pathways and immunological recognition. Recently, methods for predicting protein interactions using domain information are proposed and preliminary results have demonstrated their feasibility. In this paper, we develop two domain-based statistical models (neural networks and decision trees) for protein interaction predictions. Unlike most of the existing methods which consider only domain pairs (one domain from one protein) and assume that domain-domain interactions are independent of each other, the proposed methods are capable of exploring all possible interactions between domains and make predictions based on all the domains. Compared to maximum-likelihood estimation methods, our experimental results show that the proposed schemes can predict protein-protein interactions with higher specificity and sensitivity, while requiring less computation time. Furthermore, the decision tree-based model can be used to infer the interactions not only between two domains, but among multiple domains as well

Gapped spin liquid with Z2\mathbb{Z}_2-topological order for kagome Heisenberg model

We apply symmetric tensor network state (TNS) to study the nearest neighbor spin-1/2 antiferromagnetic Heisenberg model on Kagome lattice. Our method keeps track of the global and gauge symmetries in TNS update procedure and in tensor renormalization group (TRG) calculation. We also introduce a very sensitive probe for the gap of the ground state -- the modular matrices, which can also determine the topological order if the ground state is gapped. We find that the ground state of Heisenberg model on Kagome lattice is a gapped spin liquid with the $\mathbb{Z}_2$-topological order (or toric code type), which has a long correlation length $\xi\sim 10$ unit cell length. We justify that the TRG method can handle very large systems with over thousands of spins. Such a long $\xi$ explains the gapless behaviors observed in simulations on smaller systems with less than 300 spins or shorter than 10 unit cell length. We also discuss experimental implications of the topological excitations encoded in our symmetric tensors.Comment: 10 pages, 7 figure

Knowledge-guided inference of domain–domain interactions from incomplete protein–protein interaction networks

Motivation: Protein-protein interactions (PPIs), though extremely valuable towards a better understanding of protein functions and cellular processes, do not provide any direct information about the regions/domains within the proteins that mediate the interaction. Most often, it is only a fraction of a protein that directly interacts with its biological partners. Thus, understanding interaction at the domain level is a critical step towards (i) thorough understanding of PPI networks; (ii) precise identification of binding sites; (iii) acquisition of insights into the causes of deleterious mutations at interaction sites; and (iv) most importantly, development of drugs to inhibit pathological protein interactions. In addition, knowledge derived from known domain–domain interactions (DDIs) can be used to understand binding interfaces, which in turn can help discover unknown PPIs

A Calibration Method for Wide Field Multicolor Photometric System

The purpose of this paper is to present a method to self-calibrate the spectral energy distribution (SED) of objects in a survey based on the fitting of an SED library to the observed multi-color photometry. We adopt for illustrative purposes the Vilnius (Strizyz and Sviderskiene 1972) and Gunn & Stryker (1983) SED libraries. The self-calibration technique can improve the quality of observations which are not taken under perfectly photometric conditions. The more passbands used for the photometry, the better the results. This technique has been applied to the BATC 15-passband CCD survey.Comment: LateX file, 1 PS file, submitted to PASP number 99-025 The English has been improved and some mistakes have been correcte