Abstract:
By removing one empty site between two occupied sites, we map the ground states of chains of hardcore bosons and spinless fermions with infinite nearest-neighbor repulsion to ground states of chains of hardcore bosons and spinless fermions without nearest-neighbor repulsion respectively, and ultimately in terms of the one-dimensional Fermi sea. We then introduce the intervening-particle expansion, where we write correlation functions in such ground states as a systematic sum over conditional expectations, each of which can be ultimately mapped to a one-dimensional Fermi-sea expectation. Various ground-state correlation functions are calculated for the bosonic and fermionic chains with infinite nearest-neighbor repulsion, as well as for a ladder model of spinless fermions with infinite nearest-neighbor repulsion and correlated hopping in three limiting cases. We find that the decay of these correlation functions are governed by surprising power-law exponents.

Abstract:
Given the ground state wavefunction for an interacting lattice model, we define a "correlation density matrix"(CDM) for two disjoint, separated clusters $A$ and $B$, to be the density matrix of their union, minus the direct product of their respective density matrices. The CDM can be decomposed systematically by a numerical singular value decomposition, to provide a systematic and unbiased way to identify the operator(s) dominating the correlations, even unexpected ones.

Abstract:
Building upon an analytical technique introduced by Chung and Peschel [M. Chung and I. Peschel, Phys. Rev. B 64, art. 064412 (2001)], we calculated the density matrix rho_B of a finite block of B sites within an infinite system of free spinless fermions. In terms of the block Green function matrix G (whose elements are G_ij = < c_i^+ c_j>, where c_i^+ and c_j are fermion creation and annihilation operators acting on sites i and j within the block respectively), the density matrix can be written as rho_B = det(1 - G) exp[ sum_ij (log G(1 - G^{-1})_ij c_i^+ c_j]. Implications of such a result to Hilbert space truncation for real-space renormalization schemes is discussed.

Abstract:
The reduced density matrix of an interacting system can be used as the basis for a truncation scheme, or in an unbiased method to discover the strongest kind of correlation in the ground state. In this paper, we investigate the structure of the many-body fermion density matrix of a small cluster in a square lattice. The cluster density matrix is evaluated numerically over a set of finite systems, subject to non-square periodic boundary conditions given by the lattice vectors $\bR_1 \equiv (R_{1x}, R_{1y})$ and $\bR_2 \equiv (R_{2x}, R_{2y})$. We then approximate the infinite-system cluster density-matrix spectrum, by averaging the finite-system cluster density matrix (i) over degeneracies in the ground state, and orientations of the system relative to the cluster, to ensure it has the proper point-group symmetry; and (ii) over various twist boundary conditions to reduce finite size effects. We then compare the eigenvalue structure of the averaged cluster density matrix for noninteracting and strongly-interacting spinless fermions, as a function of the filling fraction $\nbar$, and discuss whether it can be approximated as being built up from a truncated set of single-particle operators.

Abstract:
In [S. A. Cheong and C. L. Henley, cond-mat/0206196 (2002)], we found that the many-particle eigenvalues and eigenstates of the many-body density matrix $\rho_B$ of a block of $B$ sites cut out from an infinite chain of noninteracting spinless fermions can all be constructed out of the one-particle eigenvalues and one-particle eigenstates respectively. In this paper we developed a statistical-mechanical analogy between the density matrix eigenstates and the many-body states of a system of noninteracting fermions. Each density matrix eigenstate corresponds to a particular set of occupation of single-particle pseudo-energy levels, and the density matrix eigenstate with the largest weight, having the structure of a Fermi sea ground state, unambiguously defines a pseudo-Fermi level. We then outlined the main ideas behind an operator-based truncation of the density matrix eigenstates, where single-particle pseudo-energy levels far away from the pseudo-Fermi level are removed as degrees of freedom. We report numerical evidence for scaling behaviours in the single-particle pseudo-energy spectrum for different block sizes $B$ and different filling fractions $\nbar$. With the aid of these scaling relations, which tells us that the block size $B$ plays the role of an inverse temperature in the statistical-mechanical description of the density matrix eigenstates and eigenvalues, we looked into the performance of our operator-based truncation scheme in minimizing the discarded density matrix weight and the error in calculating the dispersion relation for elementary excitations. This performance was compared against that of the traditional density matrix-based truncation scheme, as well as against a operator-based plane wave truncation scheme, and found to be very satisfactory.

Abstract:
In this paper, we describe the context sensitivity problem encountered in partitioning a heterogeneous biological sequence into statistically homogeneous segments. After showing signatures of the problem in the bacterial genomes of Escherichia coli K-12 MG1655 and Pseudomonas syringae DC3000, when these are segmented using two entropic segmentation schemes, we clarify the contextual origins of these signatures through mean-field analyses of the segmentation schemes. Finally, we explain why we believe all sequence segmentation schems are plagued by the context sensitivity problem.

Abstract:
In this paper, we extend a previously developed recursive entropic segmentation scheme for applications to biological sequences. Instead of Bernoulli chains, we model the statistically stationary segments in a biological sequence as Markov chains, and define a generalized Jensen-Shannon divergence for distinguishing between two Markov chains. We then undertake a mean-field analysis, based on which we identify pitfalls associated with the recursive Jensen-Shannon segmentation scheme. Following this, we explain the need for segmentation optimization, and describe two local optimization schemes for improving the positions of domain walls discovered at each recursion stage. We also develop a new termination criterion for recursive Jensen-Shannon segmentation based on the strength of statistical fluctuations up to a minimum statistically reliable segment length, avoiding the need for unrealistic null and alternative segment models of the target sequence. Finally, we compare the extended scheme against the original scheme by recursively segmenting the Escherichia coli K-12 MG1655 genome.

Abstract:
Atoms and molecules are important conceptual entities we invented to understand the physical world around us. The key to their usefulness lies in the organization of nuclear and electronic degrees of freedom into a single dynamical variable whose time evolution we can better imagine. The use of such effective variables in place of the true microscopic variables is possible because of the separation between nuclear/electronic and atomic/molecular time scales. Where separation of time scales occurs, identification of analogous objects in financial markets can help advance our understanding of their dynamics. To detect separated time scales and identify their associated effective degrees of freedom in financial markets, we devised a two-stage statistical clustering scheme to analyze the price movements of stocks in several equity markets. Through this two-time-scale clustering analysis, we discovered a hierarchy of levels of self-organization in real financial markets. We call these statistically robust self-organized dynamical structures financial atoms, financial molecules, and financial supermolecules. In general, the detailed compositions of these dynamical structures cannot be deduced based on raw financial intuition alone, and must be explained in terms of the underlying portfolios, and investment strategies of market players. More interestingly, we find that major market events such as the Chinese Correction and the Subprime Crisis leave many tell-tale signs within the correlational structures of financial molecules.

Abstract:
We calculate the widths, migration barriers, effective masses, and quantum tunneling rates of kinks and jogs in extended screw dislocations in copper, using an effective medium theory interatomic potential. The energy barriers and effective masses for moving a unit jog one lattice constant are close to typical atomic energies and masses: tunneling will be rare. The energy barriers and effective masses for the motion of kinks are unexpectedly small due to the spreading of the kinks over a large number of atoms. The effective masses of the kinks are so small that quantum fluctuations will be important. We discuss implications for quantum creep, kink--based tunneling centers, and Kondo resonances.

Abstract:
Controlling severe outbreaks remains the most important problem in infectious disease area. With time, this problem will only become more severe as population density in urban centers grows. Social interactions play a very important role in determining how infectious diseases spread, and organization of people along social lines gives rise to non-spatial networks in which the infections spread. Infection networks are different for diseases with different transmission modes, but are likely to be identical or highly similar for diseases that spread the same way. Hence, infection networks estimated from common infections can be useful to contain epidemics of a more severe disease with the same transmission mode. Here we present a proof-of-concept study demonstrating the effectiveness of epidemic mitigation based on such estimated infection networks. We first generate artificial social networks of different sizes and average degrees, but with roughly the same clustering characteristic. We then start SIR epidemics on these networks, censor the simulated incidences, and use them to reconstruct the infection network. We then efficiently fragment the estimated network by removing the smallest number of nodes identified by a graph partitioning algorithm. Finally, we demonstrate the effectiveness of this targeted strategy, by comparing it against traditional untargeted strategies, in slowing down and reducing the size of advancing epidemics.