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arXiv2019

How does active participation effect consensus: Adaptive network model of opinion dynamics and influence maximizing rewiring

In this paper we study the impact of active participation -- or deliberately seeking out other agents with an aim to convince them -- on the dynamics of consensus formation. For this purpose, we propose an adaptive network model in which two processes shape opinion dynamics at interwoven time-scales: (i) agents adapt their opinions subject to influence from social network neighbours who hold opinions within a tolerance interval $δ$ and (ii) agents rewire network connections with an aim to maximize their own influence on overall system opinion. We study this system in both an endogenous setting in which all agents are subject to influence and also attempt to maximize influence, and in a setting of exogenous control, in which external agents not subject to influence adaptively attempt to maximize their influence. In both settings we find three regimes of stationary opinion configurations: (i) for low $δ$ a regime of two evenly balanced radicalized opinion clusters at the extremes of the opinion space, (ii) for intermediate $δ$ a 'winner-takes-most' regime of two unevenly sized radicalized opinion clusters, and (iii) for large $δ$ a regime in which very low spread compromise consensus states can be reached. Comparing to adaptive processes of random and deliberately spread-reducing rewiring, we demonstrate that in regime (iii) competitive influence maximization can achieve near-minimal opinion spread within near-optimal times. Further, we also show that competitive influence maximizing rewiring can reduce the impact of small influential minorities on consensus states.

physics.soc-ph↗

arXiv2019

Strong and Weak Many-Body Localizations

Many aspects of many-body localization (MBL), including dynamic classification of MBL phases, remain elusive. Here, by performing real-space renormalization group (RSRG) analysis we propose that there are two distinct types of MBL phases: "strong MBL" induced by quasiperiodic (QP) potential and "weak MBL" induced by random potential. Strong and weak MBL phases can be distinguished by their different probability distributions of thermal inclusion and entanglement entropy: exponential decay in strong MBL phases but power-law decay in weak MBL. We further discuss underlying mechanisms as well as experimental implications of having two distinct types of MBL phases. Strong MBL induced by QP potential may provide a more robust and promising platform for quantum information storage and processing.

cond-mat.dis-nn↗

arXiv2019

Modeling functional resting-state brain networks through neural message passing on the human connectome

Understanding the relationship between the structure and function of the human brain is one of the most important open questions in Neurosciences. In particular, Resting State Networks (RSN) and more specifically the Default Mode Network (DMN) of the brain, which are defined from the analysis of functional data lack a definitive justification consistent with the anatomical structure of the brain. In this work, we show that a possible connection may naturally rest on the idea that information flows in the brain through a neural message-passing dynamics between macroscopic structures, like those defined by the human connectome (HC). In our model, each brain region in the HC is assumed to have a binary behavior (active or not), the strength of interactions among them is encoded in the anatomical connectivity matrix defined by the HC, and the dynamics of the system is defined by a neural message-passing algorithm, Belief Propagation (BP), working near the critical point of the human connectome. We show that in the absence of direct external stimuli the BP algorithm converges to a spatial map of activations that is similar to the DMN. Moreover, we computed, using Susceptibility Propagation (SP), the matrix of correlations between the different regions and show that the modules defined by a clustering of this matrix resemble several Resting States Networks determined experimentally. Both results suggest that the functional DMN and RSNs can be seen as simple consequences of the anatomical structure of the brain and a neural message-passing dynamics between macroscopic regions. We then show preliminary results indicating our predictions on how functional DMN maps change when the anatomical brain network suffers structural anomalies, like in Alzheimers Disease and in lesions of the Corpus Callosum.

q-bio.NC↗

arXiv2019

Macroscopic theorem of the portfolio optimization problem with a risk-free asset

The investment risk minimization problem with budget and return constraints has been the subject of research using replica analysis but there are shortcomings in the extant literature. With respect to Tobin's separation theorem and the capital asset pricing model, it is necessary to investigate the implications of a risk-free asset and examine its influence on the optimal portfolio. Accordingly, in this work, we explore the investment risk minimization problem in the presence of a risk-free asset with budget and return constraints. Moreover, we discuss opportunity loss, the Pythagorean theorem of the Sharpe ratio, and Tobin's separation theorem.

q-fin.PM↗

arXiv2019

Cooling Rate Effects on the Structure of 45S5 Bioglass: Computational and Experimental Evidence of Si--P Avoidance

Due to its ability to bond with living tissues upon dissolution, 45S5 bioglass and related compositions are promising materials for the replacement, regeneration, and repair of hard tissues in the human body. However, the details of their atomic structure remain unclear. This is partially due to the non-equilibrium nature of glasses, as their non-crystalline structure is highly dependent on their thermal history, namely, the cooling rate used during quenching. Herein, using molecular dynamics (MD) simulations and magic angle spinning nuclear magnetic resonance (MAS-NMR) spectroscopy experiments, we investigate the structure of the nominal 45S5 bioglass composition prepared using cooling rates ranging over several orders of magnitude. We show that the simulations results are in very good agreement with experimental data, provided that they are extrapolated toward lower cooling rates achieved in experiments. These results highlight that previously reported inconsistencies between simulations and experiments stem from the difference in cooling rate, thereby addressing one of the longstanding questions on the structure of bioglass. Based on these results, we demonstrate the existence of a Si--P avoidance behavior, which may be key in controlling the bioactivity of 45S5 bioglass.

cond-mat.mtrl-sci↗

arXiv2019

Computational Phase Transition Signature in Gibbs Sampling

Gibbs sampling is fundamental to a wide range of computer algorithms. Such algorithms are set to be replaced by physics based processors$-$be it quantum or stochastic annealing devices$-$which embed problem instances and evolve a physical system into an ensemble to recover a probability distribution. At a critical constraint to variable ratio, decision problems$-$such as propositional satisfiability$-$appear to statistically exhibit an abrupt transition in required computational resources. This so called, algorithmic or computational phase transition signature, has yet-to-be observed in contemporary physics based processors. We found that the computational phase transition admits a signature in Gibbs' distributions and hence we predict and prescribe the physical observation of this effect. We simulate such an experiment, that when realized experimentally, we believe would represent a milestone in the physical theory of computation.

quant-ph↗

arXiv2019

Finding Quantum Many-Body Ground States with Artificial Neural Network

Solving ground states of quantum many-body systems has been a long-standing problem in condensed matter physics. Here, we propose a new unsupervised machine learning algorithm to find the ground state of a general quantum many-body system utilizing the benefits of artificial neural network. Without assuming the specific forms of the eigenvectors, this algorithm can find the eigenvectors in an unbiased way with well controlled accuracy. As examples, we apply this algorithm to 1D Ising and Heisenberg models, where the results match very well with exact diagonalization.

cond-mat.dis-nn↗

arXiv2019

Robust topological invariants of topological crystalline phases in the presence of impurities

Topological crystalline phases (TCPs) are topological states protected by spatial symmetries. A broad range of TCPs have been conventionally studied by formulating topological invariants (symmetry indicators) at invariant momenta in the Brillouin zone, which leaves open the question of their stability in the absence of translational invariance. In this work, we show that robust basis-independent topological invariants can be generically constructed for TCPs using projected symmetry operators. Remarkably we show that the real-space topological markers of these invariants are exponentially localized to the fixed points of the spatial symmetry. As a result, this real-space structure protects them against the presence of impurities that are located away from the fixed points. By considering all possible symmetry centers in a crystalline system we can generate a mesh of real-space topological markers that can provide a local topological distinction for TCPs. We illustrate the robustness of this mesh of invariants with 1D and 2D TCPs protected by inversion, rotational and mirror symmetries. Finally, we find that the boundary modes of these TCPs can also exhibit robust topological invariants with localized markers on the edges. We illustrate this with the gapless Majorana boundary modes of mirror-symmetric topological superconductors, and relate their integer topological edge invariant with a quantized effective edge polarization.

cond-mat.dis-nn↗

arXiv2019

Intermittency of dynamical phases in a quantum spin glass

Answering the question of existence of efficient quantum algorithms for NP-hard problems require deep theoretical understanding of the properties of the low-energy eigenstates and long-time coherent dynamics in quantum spin glasses. We discovered and described analytically the property of asymptotic orthogonality resulting in a new type of structure in quantum spin glass. Its eigen-spectrum is split into the alternating sequence of bands formed by quantum states of two distinct types ($x$ and $z$). Those of $z$-type are non-ergodic extended eigenstates (NEE) in the basis of $\{σ_z\}$ operators that inherit the structure of the classical spin glass with exponentially long decay times of Edwards Anderson order parameter at any finite value of transverse field $B_{\perp}$. Those of $x$-type form narrow bands of NEEs that conserve the integer-valued $x$-magnetization. Quantum evolution within a given band of each type is described by a Hamiltonian that belongs to either the ensemble of Preferred Basis Levi matrices ($z$-type) or Gaussian Orthogonal ensemble ($x$-type). We characterize the non-equilibrium dynamics using fractal dimension $D$ that depends on energy density (temperature) and plays a role of thermodynamic potential: $D=0$ in MBL phase, $0<D<1$ in NEE phase, $D\rightarrow 1$ in ergodic phase in infinite temperature limit. MBL states coexist with NEEs in the same range of energies even at very large $B_{\perp}$. Bands of NEE states can be used for new quantum search-like algorithms of population transfer in the low-energy part of spin-configuration space. Remarkably, the intermitted structure of the eigenspectrum emerges in quantum version of a statistically featureless Random Energy Model and is expected to exist in a class of paractically important NP-hard problems that unlike REM can be implemented on a computer with polynomial resources.

cond-mat.dis-nn↗

arXiv2019

Geometry-controlled Failure Mechanisms of Amorphous Solids on the Nanoscale

Amorphous solids, confined on the nano-scale, exhibit a wealth of novel phenomena yet to be explored. In particular, the response of such solids to a mechanical load is not well understood and, as has been demonstrated experimentally, it differs strongly from bulk samples made of the same materials. Failure patterns and mechanisms are strongly affected by the geometry of the confinement and the interplay between interfacial effects in the sample and the time scale, imposed by an external mechanical field. Here, we present the mechanism of cavity formation in a confined model glass, subjected to expansion with a constant strain rate. This system is studied for varying geometric aspect ratio and sample size. Our results show that for a given temperature and straining condition, the sample shows cavitation when the aspect ratio reaches a critical value and below this aspect ratio the sample breaks by forming a neck. The critical aspect ratio is associated with a critical curvature of the neck that depends on strain rate and temperature. If this critical curvature is exceeded, the free energy of the system is minimized by the formation of a cavity. Our study reveals a novel mechanism of cavity formation on the nanoscale. This is probably a generic mechanism for material's failure in small confined systems under mechanical load.

cond-mat.stat-mech↗

arXiv2019

Relevance of topological disorder on the directed percolation phase transition

Despite decades of research, the precise role of topological disorder in critical phenomena has yet to be fully understood. A major contribution has been the work by Barghathi and Vojta, which uses spatial correlations to explain puzzling earlier results. However, due to its reliance on coordination number fluctuations, their criterion cannot be applied to constant-coordination lattices, raising the question, for which classes of transitions this type of disorder can be a relevant perturbation. In order to cast light on this question, we investigate the non-equilibrium phase transition of the two-dimensional contact process on different types of spatial random graphs with varying or constant coordination number. Using large-scale numerical simulations, we find the disorder to be relevant, as the dynamical scaling behaviour turns out to be non-conventional, thus ruling out the directed percolation universality class. We conjecture that the relevance of topological disorder is linked to how strongly connected the lattice is. Based on this assumption, we design two analysis tools that succeed in qualitatively distinguishing relevant from non-relevant topological disorders, supporting our conjecture and possibly pointing the way to a more complete relevance criterion.

cond-mat.stat-mech↗

arXiv2019

Basis Glass States: New Insights from the Potential Energy Landscape

Using the potential energy landscape formalism we show that, in the temperature range in which the dynamics of a glass forming system is thermally activated, there exists a unique set of "basis glass states" each of which is confined to a single metabasin of the energy landscape of a glass forming system. These basis glass states tile the entire configuration space of the system, exhibit only secondary relaxation and are solid-like. Any macroscopic state of the system (whether liquid or glass) can be represented as a superposition of basis glass states and can be described by a probability distribution over these states. During cooling of a liquid from a high temperature, the probability distribution freezes at sufficiently low temperatures describing the process of liquid to glass transition. The time evolution of the probability distribution towards the equilibrium distribution during subsequent aging describes the primary relaxation of a glass.

cond-mat.dis-nn↗

arXiv2019

Scale Invariant Entanglement Negativity at the Many-Body Localization Transition

The exact nature of the many-body localization transition remains an open question. An aspect which has been posited in various studies is the emergence of scale invariance around this point, however the direct observation of this phenomenon is still absent. Here we achieve this by studying the logarithmic negativity and mutual information between disjoint blocks of varying size across the many-body localization transition. The two length scales, block sizes and the distance between them, provide a clear quantitative probe of scale invariance across different length scales. We find that at the transition point, the logarithmic negativity obeys a scale invariant exponential decay with respect to the ratio of block separation to size, whereas the mutual information obeys a polynomial decay. The observed scale invariance of the quantum correlations in a microscopic model opens the direction to probe the fractal structure in critical eigenstates using tensor network techniques and provide constraints on the theory of the many-body localization transition.

cond-mat.dis-nn↗

arXiv2019

Atomic Vibrations in Glasses

In glasses, atomic disorder combined with atomic connectivity makes understanding of the nature of the vibrations much more complex than in crystals or molecules. With a simple model, however, it is possible to show how disorder generates quasi-local modes on optic branches as well as on acoustic branches at low-frequency. The latter modes, possibly hybridizing with low-lying optic modes in real glasses, lead to the excess, low-frequency excitations known as {\it boson-peak modes}, which are lacking in crystals. The spatially quasi-localized vibrations also explain anomalies in thermal conductivity and the end of the acoustic branches, two other specific features of glasses. Together with the quasi-localization of the modes at the nanometric scale, structural disorder lifts the crystalline or molecular spectroscopic selection rules and makes interpretation of experiments difficult. Nevertheless, vibrations in simple glasses such as vitreous silica or vitreous boron oxide are nowadays rather well described. But a comprehensive understanding of the boson peak modes remains a highly debated issue as illustrated by three archetypal glass systems, vitreous SiO$_2$ and B$_2$O$_3$ and amorphous silicon.

cond-mat.dis-nn↗

arXiv2021

Pattern recognition in Deep Boltzmann machines

We consider a multi-layer Sherrington-Kirkpatrick spin-glass as a model for deep restricted Boltzmann machines and we solve for its quenched free energy, in the thermodynamic limit and allowing for a first step of replica symmetry breaking. This result is accomplished rigorously exploiting interpolating techniques and recovering the expression already known for the replica-symmetry case. Further, we drop the restriction constraint by introducing intra-layer connections among spins and we show that the resulting system can be mapped into a modular Hopfield network, which is also addressed rigorously via interpolating techniques up to the first step of replica symmetry breaking.

cond-mat.dis-nn↗

arXiv2021

Spacetime Neural Network for High Dimensional Quantum Dynamics

We develop a spacetime neural network method with second order optimization for solving quantum dynamics from the high dimensional Schrödinger equation. In contrast to the standard iterative first order optimization and the time-dependent variational principle, our approach utilizes the implicit mid-point method and generates the solution for all spatial and temporal values simultaneously after optimization. We demonstrate the method in the Schrödinger equation with a self-normalized autoregressive spacetime neural network construction. Future explorations for solving different high dimensional differential equations are discussed.

cond-mat.dis-nn↗

arXiv2021

Magnetic field driven dielectric relaxation in non-magnetic composite medium: a low temperature study

The frequency dependence of dielectric constant for composites of polyaniline (PANI) and multi-walled carbon nanotube (MWCNT) with different degree of functionalization is studied at low temperature (down to 4.2 K) and magnetic field (up to 3 Tesla) applied both in parallel and perpendicular direction of ac electric field. A relaxation phenomenon is observed in all the MWCNT/PANI composites by applying magnetic field in both the directions, below 10$^3$ Hz. However, PANI does not show any relaxation peak with applied magnetic field in either direction. The relaxation peak frequency does not depend on the strength of magnetic field but it varies with temperature and degree of functionalization of MWCNT in composites. This relaxation phenomenon occurs due to the inhomogeneity of the medium of two highly mismatched conductive materials at low temperatures. We have tried to explain our results in the light of Parish and Littlewood theory about magnetocapacitance in nonmagnetic composite.

cond-mat.dis-nn↗

arXiv2021

The emergence of a concept in shallow neural networks

We consider restricted Boltzmann machine (RBMs) trained over an unstructured dataset made of blurred copies of definite but unavailable ``archetypes'' and we show that there exists a critical sample size beyond which the RBM can learn archetypes, namely the machine can successfully play as a generative model or as a classifier, according to the operational routine. In general, assessing a critical sample size (possibly in relation to the quality of the dataset) is still an open problem in machine learning. Here, restricting to the random theory, where shallow networks suffice and the grand-mother cell scenario is correct, we leverage the formal equivalence between RBMs and Hopfield networks, to obtain a phase diagram for both the neural architectures which highlights regions, in the space of the control parameters (i.e., number of archetypes, number of neurons, size and quality of the training set), where learning can be accomplished. Our investigations are led by analytical methods based on the statistical-mechanics of disordered systems and results are further corroborated by extensive Monte Carlo simulations.

cond-mat.dis-nn↗