arXiv subjects

J. P. Crutchfield

Publications and source records attributed to J. P. Crutchfield.

30 records · Page 2Linked to original sources

arXiv2016

Anatomy of a Spin: The Information-Theoretic Structure of Classical Spin Systems

Collective organization in matter plays a significant role in its expressed physical properties. Typically, it is detected via an order parameter, appropriately defined for each given system's observed emergent patterns. Recent developments in information theory, however, suggest quantifying collective organization in a system- and phenomenon-agnostic way: decompose the system's thermodynamic entropy density into a localized entropy, that solely contained in the dynamics at a single location, and a bound entropy, that stored in space as domains, clusters, excitations, or other emergent structures. We compute this decomposition and related quantities explicitly for the nearest-neighbor Ising model on the 1D chain, the Bethe lattice with coordination number k=3, and the 2D square lattice, illustrating its generality and the functional insights it gives near and away from phase transitions. In particular, we consider the roles that different spin motifs play (in cluster bulk, cluster edges, and the like) and how these affect the dependencies between spins.

cond-mat.stat-mech↗

arXiv2015

Identifying Functional Thermodynamics in Autonomous Maxwellian Ratchets

We introduce a family of Maxwellian Demons for which correlations among information bearing degrees of freedom can be calculated exactly and in compact analytical form. This allows one to precisely determine Demon functional thermodynamic operating regimes, when previous methods either misclassify or simply fail due to approximations they invoke. This reveals that these Demons are more functional than previous candidates. They too behave either as engines, lifting a mass against gravity by extracting energy from a single heat reservoir, or as Landauer erasers, consuming external work to remove information from a sequence of binary symbols by decreasing their individual uncertainty. Going beyond these, our Demon exhibits a new functionality that erases bits not by simply decreasing individual-symbol uncertainty, but by increasing inter-bit correlations (that is, by adding temporal order) while increasing single-symbol uncertainty. In all cases, but especially in the new erasure regime, exactly accounting for informational correlations leads to tight bounds on Demon performance, expressed as a refined Second Law of Thermodynamics that relies on the Kolmogorov-Sinai entropy for dynamical processes and not on changes purely in system configurational entropy, as previously employed. We rigorously derive the refined Second Law under minimal assumptions and so it applies quite broadly---for Demons with and without memory and input sequences that are correlated or not. We note that general Maxwellian Demons readily violate previously proposed, alternative such bounds, while the current bound still holds.

cond-mat.stat-mech↗

arXiv2015

Occam's Quantum Strop: Synchronizing and Compressing Classical Cryptic Processes via a Quantum Channel

A stochastic process's statistical complexity stands out as a fundamental property: the minimum information required to synchronize one process generator to another. How much information is required, though, when synchronizing over a quantum channel? Recent work demonstrated that representing causal similarity as quantum state-indistinguishability provides a quantum advantage. We generalize this to synchronization and offer a sequence of constructions that exploit extended causal structures, finding substantial increase of the quantum advantage. We demonstrate that maximum compression is determined by the process's cryptic order---a classical, topological property closely allied to Markov order, itself a measure of historical dependence. We introduce an efficient algorithm that computes the quantum advantage and close noting that the advantage comes at a cost---one trades off prediction for generation complexity.

quant-ph↗

arXiv2014

Diffraction Patterns of Layered Close-packed Structures from Hidden Markov Models

We recently derived analytical expressions for the pairwise (auto)correlation functions (CFs) between modular layers (MLs) in close-packed structures (CPSs) for the wide class of stacking processes describable as hidden Markov models (HMMs) [Riechers \etal, (2014), Acta Crystallogr.~A, XX 000-000]. We now use these results to calculate diffraction patterns (DPs) directly from HMMs, discovering that the relationship between the HMMs and DPs is both simple and fundamental in nature. We show that in the limit of large crystals, the DP is a function of parameters that specify the HMM. We give three elementary but important examples that demonstrate this result, deriving expressions for the DP of CPSs stacked (i) independently, (ii) as infinite-Markov-order randomly faulted 2H and 3C stacking structures over the entire range of growth and deformation faulting probabilities, and (iii) as a HMM that models Shockley-Frank stacking faults in 6H-SiC. While applied here to planar faulting in CPSs, extending the methods and results to planar disorder in other layered materials is straightforward. In this way, we effectively solve the broad problem of calculating a DP---either analytically or numerically---for any stacking structure---ordered or disordered---where the stacking process can be expressed as a HMM.

cond-mat.mtrl-sci↗

arXiv2014

Pairwise Correlations in Layered Close-Packed Structures

Given a description of the stacking statistics of layered close-packed structures in the form of a hidden Markov model, we develop analytical expressions for the pairwise correlation functions between the layers. These may be calculated analytically as explicit functions of model parameters or the expressions may be used as a fast, accurate, and efficient way to obtain numerical values. We present several examples, finding agreement with previous work as well as deriving new relations.

cond-mat.mtrl-sci↗

arXiv2012

Enumerating Finitary Processes

We show how to efficiently enumerate a class of finite-memory stochastic processes using the causal representation of epsilon-machines. We characterize epsilon-machines in the language of automata theory and adapt a recent algorithm for generating accessible deterministic finite automata, pruning this over-large class down to that of epsilon-machines. As an application, we exactly enumerate topological epsilon-machines up to eight states and six-letter alphabets.

cs.FL↗

arXiv2005

Persistent Chaos in High Dimensions

An extensive statistical survey of universal approximators shows that as the dimension of a typical dissipative dynamical system is increased, the number of positive Lyapunov exponents increases monotonically and the number of parameter windows with periodic behavior decreases. A subset of parameter space remains in which topological change induced by small parameter variation is very common. It turns out, however, that if the system's dimension is sufficiently high, this inevitable, and expected, topological change is never catastrophic, in the sense chaotic behavior is preserved. One concludes that deterministic chaos is persistent in high dimensions.

nlin.CD↗

arXiv2003

From Finite to Infinite Range Order via Annealing: The Causal Architecture of Deformation Faulting in Annealed Close-Packed Crystals

We analyze solid-state phase transformations that occur in zinc-sulfide crystals during annealing using a random deformation-faulting mechanism with a very simple interaction between adjacent close-packed double layers. We show that, through annealing, infinite-range structures emerge from initially short-range crystal order. That is, widely separated layers carry structurally significant information and so layer stacking cannot be completely described by any finite-range Markov process. We compare our results to two experimental diffraction spectra, finding excellent agreement.

cond-mat.mtrl-sci↗

arXiv2003

Inferring Pattern and Disorder in Close-Packed Structures from X-ray Diffraction Studies, Part I: epsilon-Machine Spectral Reconstruction Theory

In a recent publication [D. P. Varn, G. S. Canright, and J. P. Crutchfield, Phys. Rev. B {\bf 66}:17, 156 (2002)] we introduced a new technique for discovering and describing planar disorder in close-packed structures (CPSs) directly from their diffraction spectra. Here we provide the theoretical development behind those results, adapting computational mechanics to describe one-dimensional structure in materials. By way of contrast, we give a detailed analysis of the current alternative approach, the fault model (FM), and offer several criticisms. We then demonstrate that the computational mechanics description of the stacking sequence--in the form of an epsilon-machine--provides the minimal and unique description of the crystal, whether ordered, disordered, or some combination. We find that we can detect and describe any amount of disorder, as well as materials that are mixtures of various kinds of crystalline structure. Underlying this approach is a novel method for epsilon-machine reconstruction that uses correlation functions estimated from diffraction spectra, rather than sequences of microscopic configurations, as is typically used in other domains. The result is that the methods developed here can be adapted to a wide range of experimental systems in which spectroscopic data is available.

cond-mat.mtrl-sci↗

arXiv2003

Inferring Pattern and Disorder in Close-Packed Structures from X-ray Diffraction Studies, Part II: Structure and Intrinsic Computation in Zinc Sulphide

In the previous paper of this series [D. P. Varn, G. S. Canright, and J. P. Crutchfield, Physical Review B, submitted] we detailed a procedure--epsilon-machine spectral reconstruction--to discover and analyze patterns and disorder in close-packed structures as revealed in x-ray diffraction spectra. We argued that this computational mechanics approach is more general than the current alternative theory, the fault model, and that it provides a unique characterization of the disorder present. We demonstrated the efficacy of computational mechanics on four prototype spectra, finding that it was able to recover a statistical description of the underlying modular-layer stacking using epsilon-machine representations. Here we use this procedure to analyze structure and disorder in four previously published zinc sulphide diffraction spectra. We selected zinc sulphide not only for the theoretical interest this material has attracted in an effort to develop an understanding of polytypism, but also because it displays solid-state phase transitions and experimental data is available.

cond-mat.mtrl-sci↗

arXiv2002

Discovering Planar Disorder in Close-Packed Structures from X-Ray Diffraction: Beyond the Fault Model

We solve a longstanding problem--determining structural information for disordered materials from their diffraction spectra--for the case of planar disorder in close-packed structures (CPSs). Our solution offers the most complete possible statistical description of the disorder, and, from it, we find the minimum effective memory length of disordered stacking sequences. We also compare our model of disorder with the so-called fault model (FM) and demonstrate that in simple cases our approach reduces to the FM, but in cases that are more complex it provides a general and more accurate structural description than the FM. We demonstrate our technique on two previously published zinc sulphide diffraction spectra.

cond-mat.dis-nn↗

arXiv1993

Dynamics, Computation, and the "Edge of Chaos": A Re-Examination

In this paper we review previous work and present new work concerning the relationship between dynamical systems theory and computation. In particular, we review work by Langton \cite{Langton90} and Packard \cite{Packard88} on the relationship between dynamical behavior and computational capability in cellular automata (CA). We present results from an experiment similar to the one described in \cite{Packard88}, that was cited there as evidence for the hypothesis that rules capable of performing complex computations are most likely to be found at a phase transition between ordered and chaotic behavioral regimes for CA (the ``edge of chaos''). Our experiment produced very different results from the original experiment, and we suggest that the interpretation of the original results is not correct. We conclude by discussing general issues related to dynamics, computation, and the ``edge of chaos'' in cellular automata.

adap-org↗