Trimer-Monomer Mixture Problem on (111) 1×11 \times 1 Surface of Diamond Structure

We consider a system of trimers and monomers on the triangular lattice, which describes the adsorption problem on (111) $1 \times 1$ surface of diamond structure. We give a mapping to a 3-state vertex model on the square lattice. We treat the problem by the transfer-matrix method combined with the density-matrix algorithm, to obtain thermodynamic quantities.Comment: 9 pages, 7 figures, PTPTeX ver. 1.0. To appear Progress of Theoretical Physics, Jan. 2001. http://www2.yukawa.kyoto-u.ac.jp/~ptpwww/ (Full Access

Link invariants from NN-state vertex models: an alternative construction independent of statistical models

We reproduce the hierarchy of link invariants associated to the series of $N$-state vertex models with a method different from the original construction due to Akutsu, Deguchi and Wadati. The alternative method substitutes the `crossing symmetry' property exhibited by the Boltzmann weights of the vertex models by a similar property which, for the purpose of constructing link invariants, encodes the same information but requires only the limit of the Boltzmann weights when the spectral parameter is sent to infinity.Comment: 20 pages, LaTeX, uses epsf.sty. To appear in Nucl. Phys.

Universal Asymptotic Eigenvalue Distribution of Density Matrices and the Corner Transfer Matrices in the Thermodynamic Limit

We study the asymptotic behavior of the eigenvalue distribution of the Baxter's corner transfer matrix (CTM) and the density matrix (DM) in the White's density-matrix renormalization group (DMRG), for one-dimensional quantum and two-dimensional classical statistical systems. We utilize the relationship ${\rm DM}={\rm CTM}^4$ which holds for non-critical systems in the thermodynamic limit. Using the known diagonal form of CTM, we derive exact asymptotic form of the DM eigenvalue distribution for the integrable $S=1/2$ XXZ chain (and its related integrable models) in the massive regime. The result is then recast into a ``universal'' form without model-specific quantities, which leads to $\omega_{m}\sim \exp[-{\rm const.}(\log m)^2]$ for $m$-th DM eigenvalue at larg $m$. We perform numerical renormalization group calculations (using the corner-transfer-matrix RG and the product-wavefunction RG) for non-integrable models, verifying the ``universal asymptotic form'' for them. Our results strongly suggest the universality of the asymptotic eigenvalue distribution of DM and CTM for a wide class of systems.Comment: 4 pages, RevTeX, 4 ps figure

Maximum margin classifier working in a set of strings

Numbers and numerical vectors account for a large portion of data. However, recently the amount of string data generated has increased dramatically. Consequently, classifying string data is a common problem in many fields. The most widely used approach to this problem is to convert strings into numerical vectors using string kernels and subsequently apply a support vector machine that works in a numerical vector space. However, this non-one-to-one conversion involves a loss of information and makes it impossible to evaluate, using probability theory, the generalization error of a learning machine, considering that the given data to train and test the machine are strings generated according to probability laws. In this study, we approach this classification problem by constructing a classifier that works in a set of strings. To evaluate the generalization error of such a classifier theoretically, probability theory for strings is required. Therefore, we first extend a limit theorem on the asymptotic behavior of a consensus sequence of strings, which is the counterpart of the mean of numerical vectors, as demonstrated in the probability theory on a metric space of strings developed by one of the authors and his colleague in a previous study [18]. Using the obtained result, we then demonstrate that our learning machine classifies strings in an asymptotically optimal manner. Furthermore, we demonstrate the usefulness of our machine in practical data analysis by applying it to predicting protein--protein interactions using amino acid sequences.Comment: This manuscript has been withdrawn because the experiments in Section 6 are insufficien