A cluster model with random anisotropy for hysteresis jumps in CeNi1−x_{1-x}Cux_{x} alloys

Some Cerium compounds exhibit hysteresis cycles with sharp macroscopic jumps in the magnetization at very low temperatures. This effect is attributed to the formation of clusters in which the anisotropy competes with the applied magnetic field. Here, we present a simple model where a lattice of ferromagnetically coupled spins is separated in clusters of random sizes and with random anisotropy. Within this model, we obtain hysteresis cycles presenting jumps that behave in a similar way that the experimental ones, and that disappear when increasing the temperature. The results are in good agreement with the hysteresis cycles measured at very low temperatures in CeNi$_{1-x}$Cu$_{x}$ and the comparison with these experimental results allows to discriminate the relative importance of the mechanisms driving the thermal evolution of the cycles.Comment: Accepted in PR

Visualizing probabilistic models: Intensive Principal Component Analysis

Unsupervised learning makes manifest the underlying structure of data without curated training and specific problem definitions. However, the inference of relationships between data points is frustrated by the `curse of dimensionality' in high-dimensions. Inspired by replica theory from statistical mechanics, we consider replicas of the system to tune the dimensionality and take the limit as the number of replicas goes to zero. The result is the intensive embedding, which is not only isometric (preserving local distances) but allows global structure to be more transparently visualized. We develop the Intensive Principal Component Analysis (InPCA) and demonstrate clear improvements in visualizations of the Ising model of magnetic spins, a neural network, and the dark energy cold dark matter ({\Lambda}CDM) model as applied to the Cosmic Microwave Background.Comment: 6 pages, 5 figure

Nucleation at the DNA supercoiling transition

Twisting DNA under a constant applied force reveals a thermally activated transition into a state with a supercoiled structure known as a plectoneme. Using transition state theory, we predict the rate of this plectoneme nucleation to be of order 10^4 Hz. We reconcile this with experiments that have measured hopping rates of order 10 Hz by noting that the viscosity of the bead used to manipulate the DNA limits the measured rate. We find that the intrinsic bending caused by disorder in the base-pair sequence is important for understanding the free energy barrier that governs the transition. Both analytic and numerical methods are used in the calculations. We provide extensive details on the numerical methods for simulating the elastic rod model with and without disorder.Comment: 18 pages, 15 figure

Deep Spin-Glass Hysteresis Area Collapse and Scaling in the d=3d=3 ±J\pm J Ising Model

We investigate the dissipative loss in the $\pm J$ Ising spin glass in three dimensions through the scaling of the hysteresis area, for a maximum magnetic field that is equal to the saturation field. We perform a systematic analysis for the whole range of the bond randomness as a function of the sweep rate, by means of frustration-preserving hard-spin mean field theory. Data collapse within the entirety of the spin-glass phase driven adiabatically (i.e., infinitely-slow field variation) is found, revealing a power-law scaling of the hysteresis area as a function of the antiferromagnetic bond fraction and the temperature. Two dynamic regimes separated by a threshold frequency $\omega_c$ characterize the dependence on the sweep rate of the oscillating field. For $\omega < \omega_c$, the hysteresis area is equal to its value in the adiabatic limit $\omega = 0$, while for $\omega > \omega_c$ it increases with the frequency through another randomness-dependent power law.Comment: 6 pages, 6 figure

Universally Sloppy Parameter Sensitivities in Systems Biology

Quantitative computational models play an increasingly important role in modern biology. Such models typically involve many free parameters, and assigning their values is often a substantial obstacle to model development. Directly measuring \emph{in vivo} biochemical parameters is difficult, and collectively fitting them to other data often yields large parameter uncertainties. Nevertheless, in earlier work we showed in a growth-factor-signaling model that collective fitting could yield well-constrained predictions, even when it left individual parameters very poorly constrained. We also showed that the model had a `sloppy' spectrum of parameter sensitivities, with eigenvalues roughly evenly distributed over many decades. Here we use a collection of models from the literature to test whether such sloppy spectra are common in systems biology. Strikingly, we find that every model we examine has a sloppy spectrum of sensitivities. We also test several consequences of this sloppiness for building predictive models. In particular, sloppiness suggests that collective fits to even large amounts of ideal time-series data will often leave many parameters poorly constrained. Tests over our model collection are consistent with this suggestion. This difficulty with collective fits may seem to argue for direct parameter measurements, but sloppiness also implies that such measurements must be formidably precise and complete to usefully constrain many model predictions. We confirm this implication in our signaling model. Our results suggest that sloppy sensitivity spectra are universal in systems biology models. The prevalence of sloppiness highlights the power of collective fits and suggests that modelers should focus on predictions rather than on parameters.Comment: Submitted to PLoS Computational Biology. Supplementary Information available in "Other Formats" bundle. Discussion slightly revised to add historical contex

Temperature dependence of the superheating field for superconductors in the high-k London limit

We study the metastability of the superheated Meissner state in type II superconductors with k >> 1 beyond Ginzburg-Landau theory, which is applicable only in the vicinity of the critical temperature. Within Eilenberger's semiclassical approximation, we use the local electrodynamic response of the superconductor to derive a generalized thermodynamic potential valid at any temperature. The stability analysis of this functional yields the temperature dependence of the superheating field. Finally, we comment on the implications of our results for superconducting cavities in particle accelerators.Comment: 7.5 pages, 2 figure