arXiv subjects

Danh-Tai Hoang

Publications and source records attributed to Danh-Tai Hoang.

16 recordsLinked to original sources

arXiv2024

A Local Counter-Regulatory Motif Modulates the Global Phase of Hormonal Oscillations

Counter-regulatory elements maintain dynamic equilibrium ubiquitously in living systems. The most prominent example, which is critical to mammalian survival, is that of pancreatic α and β cells producing glucagon and insulin for glucose homeostasis. These cells are not found in a single gland but are dispersed in multiple micro-organs known as the islets of Langerhans. Within an islet, these two reciprocal cell types interact with each other and with an additional cell type: the δ cell. By testing all possible motifs governing the interactions of these three cell types, we found that a unique set of positive/negative intra-islet interactions between different islet cell types functions not only to reduce the superficially wasteful zero-sum action of glucagon and insulin but also to enhance/suppress the synchronization of hormone secretions between islets under high/normal glucose conditions. This anti-symmetric interaction motif confers effective controllability for network (de)synchronization.

q-bio.TO↗

arXiv2023

Spin Transport in Magnetically Ordered Systems: Ferromagnets, Antiferromagnets and Frustrated Systems

In this review, we outline the important results on the resistivity encountered by an electron in magnetically ordered materials. The mechanism of the collision between the electron and the lattice spins is shown. Experiments on the spin resistivity in various magnetic materials as well as theoretical background are recalled. We focus on our works since 15 years using principally Monte Carlo simulations. In these works, we have studied the spin resistivity in various kinds of magnetic systems ranging from ferromagnets and antiferromagnets to frustrated spin systems. It is found that the spin resistivity shows a broad peak at the transition temperature in systems with a second-order phase transition, while it undergoes a discontinuous jump at the transition temperature of a first-order transition. New results on the hexagonal-close-packed (HCP) antiferromagnet are also shown in extended details for the Ising case in both the frustrated and non-frustrated parameter regions.

cond-mat.stat-mech↗

arXiv2019

Inverse Ising inference from high-temperature re-weighting of observations

Maximum Likelihood Estimation (MLE) is the bread and butter of system inference for stochastic systems. In some generality, MLE will converge to the correct model in the infinite data limit. In the context of physical approaches to system inference, such as Boltzmann machines, MLE requires the arduous computation of partition functions summing over all configurations, both observed and unobserved. We present here a conceptually and computationally transparent data-driven approach to system inference that is based on the simple question: How should the Boltzmann weights of observed configurations be modified to make the probability distribution of observed configurations close to a flat distribution? This algorithm gives accurate inference by using only observed configurations for systems with a large number of degrees of freedom where other approaches are intractable.

stat.ML↗

arXiv2019

Data-driven inference of hidden nodes in networks

The explosion of activity in finding interactions in complex systems is driven by availability of copious observations of complex natural systems. However, such systems, e.g. the human brain, are rarely completely observable. Interaction network inference must then contend with hidden variables affecting the behavior of the observed parts of the system. We present a novel data-driven approach for model inference with hidden variables. From configurations of observed variables, we identify the observed-to-observed, hidden-to-observed, observed-to-hidden, and hidden-to-hidden interactions, the configurations of hidden variables, and the number of hidden variables. We demonstrate the performance of our method by simulating a kinetic Ising model, and show that our method outperforms existing methods. Turning to real data, we infer the hidden nodes in a neuronal network in the salamander retina and a stock market network. We show that predictive modeling with hidden variables is significantly more accurate than that without hidden variables. Finally, an important hidden variable problem is to find the number of clusters in a dataset. We apply our method to classify MNIST handwritten digits. We find that there are about 60 clusters which are roughly equally distributed amongst the digits.

physics.data-an↗

arXiv2018

Causality inference in stochastic systems from neurons to currencies: Profiting from small sample size

Success in modeling complex phenomena such as human perception hinges critically on the availability of data and computational power. Significant progress has been made in modeling such phenomena using probabilistic methods, particularly in image analysis and speech recognition. Maximum Likelihood Estimation (MLE) combined with Bayesian model selection is the basis of much of this progress, as MLE converges to the true model with copious data. In the sciences, large enough datasets are rarae aves, so alternatives to MLE must be developed for small sample size. We introduce a data-driven statistical physics approach to model inference based on minimizing a free energy of data and show superior model recovery for small sample sizes. We demonstrate coupling strength inference in non-equilibrium kinetic Ising models, including in the difficult large coupling variability regime, and show scaling to systems of arbitrary size. As applications, we infer a functional connectivity network in the salamander retina and a currency exchange rate network from time-series data of neuronal spiking and currency exchange rates, respectively. Accurate small sample size inference is critical for devising a profitable currency hedging strategy.

physics.data-an↗

arXiv2015

Design Principles of Pancreatic Islets: Glucose-dependent Coordination of Hormone Pulses

Pancreatic islets are functional units involved in glucose homeostasis. The multicellular system comprises three main cell types; $β$ and $α$ cells reciprocally decrease and increase blood glucose by producing insulin and glucagon pulses, while the role of $δ$ cells is less clear. Although their spatial organization and the paracrine/autocrine interactions between them have been extensively studied, the functional implications of the design principles are still lacking. In this study, we formulated a mathematical model that integrates the pulsatility of hormone secretion and the interactions and organization of islet cells and examined the effects of different cellular compositions and organizations in mouse and human islets. A common feature of both species was that islet cells produced synchronous hormone pulses under low- and high- glucose conditions, while they produced asynchronous hormone pulses under normal glucose conditions. However, the synchronous coordination of insulin and glucagon pulses at low glucose was more pronounced in human islets that had more $α$ cells. When $β$ cells were selectively removed to mimic diabetic conditions, the anti-synchronicity of insulin and glucagon pulses was deteriorated at high glucose, but it could be partially recovered when the re-aggregation of remaining cells was considered. Finally, the third cell type, $δ$ cells, which introduced additional complexity in the multicellular system, prevented the excessive synchronization of hormone pulses. Our computational study suggests that controllable synchronization is a design principle of pancreatic islets.

q-bio.TO↗

arXiv2015

Minimal Perceptrons for Memorizing Complex Patterns

Feedforward neural networks have been investigated to understand learning and memory, as well as applied to numerous practical problems in pattern classification. It is a rule of thumb that more complex tasks require larger networks. However, the design of optimal network architectures for specific tasks is still an unsolved fundamental problem. In this study, we consider three-layered neural networks for memorizing binary patterns. We developed a new complexity measure of binary patterns, and estimated the minimal network size for memorizing them as a function of their complexity. We formulated the minimal network size for regular, random, and complex patterns. In particular, the minimal size for complex patterns, which are neither ordered nor disordered, was predicted by measuring their Hamming distances from known ordered patterns. Our predictions agreed with simulations based on the back-propagation algorithm.

q-bio.NC↗

arXiv2015

Emergent Universality in Nonequilibrium Processes of Critical Systems

We examine the Jarzynski equality for a quenching process across the critical point of second-order phase transitions, where absolute irreversibility and the effect of finite-sampling of the initial equilibrium distribution arise on an equal footing. We consider the Ising model as a prototypical example for spontaneous symmetry breaking and take into account the finite sampling issue by introducing a tolerance parameter. For a given tolerance parameter, the deviation from the Jarzynski equality depends onthe reduced coupling constant and the system size. In this work, we show that the deviation from the Jarzynski equality exhibits a universal scaling behavior inherited from the critical scaling laws of second-order phase transitions.

cond-mat.stat-mech↗

arXiv2013

Magnetic Properties of Two-dimensional Nanodots: Ground State and Phase Transition

We study the effect of perpendicular single-ion anisotropy, $-As_{\text{z}}^2$, on the ground-state structure and finite-temperature properties of a two-dimensional magnetic nanodot in presence of a dipolar interaction of strength $D$. By a simulated annealing Monte Carlo method, we show that in the ground state a vortex core perpendicular to the nanodot plane emerges already in the range of moderate anisotropy values above a certain threshold level. In the giant-anisotropy regime the vortex structure is superseded by a stripe domain structure with stripes of alternate domains perpendicular to the surface of the sample. We have also observed an intermediate stage between the vortex and stripe structures, with satellite regions of tilted nonzero perpendicular magnetization around the core. At finite temperatures, at small $A$, we show by Monte Carlo simulations that there is a transition from the the in-plane vortex phase to the disordered phase characterized by a peak in the specific heat and the vanishing vortex order parameter. At stronger $A$, we observe a discontinuous transition with a large latent heat from the in-plane vortex phase to perpendicular stripe ordering phase before a total disordering at higher temperatures. In the regime of perpendicular stripe domains, namely with giant $A$, there is no phase transition at finite $T$: the stripe domains are progressively disordered with increasing $T$. Finite-size effects are shown and discussed.

cond-mat.stat-mech↗

arXiv2013

Theory and Simulation of Magnetic Materials: Physics at Phase Frontiers

The combination of theory and simulation is necessary in the investigation of properties of complex systems where each method alone cannot do the task properly. Theory needs simulation to test ideas and to check approximations. Simulation needs theory for modeling and for understanding results coming out from computers. In this review, we give recent examples to illustrate this necessary combination in a few domains of interest such as frustrated spin systems, surface magnetism, spin transport and melting. Frustrated spin systems have been intensively studied for more than 30 years. Surface effects in magnetic materials have been widely investigated also in the last three decades. These fields are closely related to each other and their spectacular development is due to numerous applications. We confine ourselves to theoretical developments and numerical simulations on these subjects with emphasis on spectacular effects occurring at frontiers of different phases.

cond-mat.stat-mech↗

arXiv2013

Phase Transition in Dimer Liquids

We study the phase transition in a system composed of dimers interacting with each other via a nearest-neighbor (NN) exchange $J$ and competing interactions taken from a truncated dipolar coupling. Each dimer occupies a link between two nearest sites of a simple cubic lattice. We suppose that dimers are self-avoiding and can have only three orientations which coincide with the $x$, $y$ or $z$ direction. The interaction $J$ is attractive if the two dimers are parallel with each other at the NN distance, zero otherwise. The truncated dipolar interaction is characterized by two parameters: its amplitude $D$ and the cutoff distance $r_c$. Using the steepest-descent method, we determine the ground-state (GS) configuration as functions of $D$ and $r_c$. We then use Monte Carlo simulations to investigate the nature of the low-temperature phase and to determine characteristics of the phase transition from the ordered phase to the disordered phase at high temperatures at a given dimer concentration. We show that as the temperature increases, dimers remain in the compact state and the transition from the low-$T$ compact phase to the disordered phase where dimers occupy the whole space is of second order when $D$ is small, but it becomes of first order for large enough $D$, for both polarized and non polarized dimers. This transition has a resemblance with the unfolding polymer transition. The effect of $r_c$ is discussed.

cond-mat.stat-mech↗

arXiv2012

Re-orientation Transition in Molecular Thin Films: Potts Model with Dipolar Interaction

We study the low-temperature behavior and the phase transition of a thin film by Monte Carlo simulation. The thin film has a simple cubic lattice structure where each site is occupied by a Potts parameter which indicates the molecular orientation of the site. We take only three molecular orientations in this paper which correspond to the 3-state Potts model. The Hamiltonian of the system includes: (i) the exchange interaction $J_{ij}$ between nearest-neighbor sites $i$ and $j$ (ii) the long-range dipolar interaction of amplitude $D$ truncated at a cutoff distance $r_c$ (iii) a single-ion perpendicular anisotropy of amplitude $A$. We allow $J_{ij} =J_s$ between surface spins, and $J_{ij}=J$ otherwise. We show that the ground state depends on the the ratio $D/A$ and $r_c$. For a single layer, for a given $A$, there is a critical value $D_c$ below (above) which the ground-state (GS) configuration of molecular axes is perpendicular (parallel) to the film surface. When the temperature $T$ is increased, a re-orientation transition occurs near $D_c$: the low-$T$ in-plane ordering undergoes a transition to the perpendicular ordering at a finite $T$, below the transition to the paramagnetic phase. The same phenomenon is observed in the case of a film with a thickness. We show that the surface phase transition can occur below or above the bulk transition depending on the ratio $J_s/J$. Surface and bulk order parameters as well as other physical quantities are shown and discussed.

cond-mat.stat-mech↗

arXiv2011

Hexagonal-Close-Packed Lattice: Phase Transition and Spin Transport

We study the ground state (GS) and the phase transition in a hexagonal-close-packed lattice with both XY and Ising models by using extensive Monte Carlo simulation. We suppose the in-plane interaction $J_1$ and inter-plane interaction $J_2$, both antiferromagnetic. The system is frustrated with two kinds of GS configuration below and above a critical value of $η=J_1/J_2$ ($η_c$). For the Ising case, one has $η_c=0.5$ which separates in-plane ferromagnetic and antiferromagnetic states, while for the XY case $η_c=1/3$ separates the collinear and non collinear spin configurations. The phase transition is shown to be of first (second) order for $η> (<) η_c$. The spin resistivity is calculated for the Ising case. It shows a rounded maximum at the magnetic transition in the second-order region, and a discontinuity in the first-order region of $η$.

cond-mat.stat-mech↗

arXiv2011

Effect of Dipolar Interaction in Molecular Crystals

We investigate in this paper the ground state and the nature of the transition from an orientational ordered phase at low temperature to the disordered state at high temperature in a molecular crystal. Our model is a Potts model which takes into account the exchange interaction $J$ between nearest-neighbor molecules and a dipolar interaction between molecular axes in three dimensions. The dipolar interaction is characterized by two parameters: its amplitude $D$ and the cutoff distance $r_c$. If the molecular axis at a lattice site has three orientations, say the $x$, $y$ or $z$ axes, then when D=0, the system is equivalent to the 3-state Potts model: the transition to the disordered phase is known to be of first order. When $D\neq 0$, the ground-state configuration is shown to be composed of two independent interpenetrating layered subsystems which form a sandwich whose periodicity depends on $D$ and $r_c$. We show by extensive Monte Carlo simulation with a histogram method that the phase transition remains of first order at relatively large values of $r_c$.

cond-mat.stat-mech↗

arXiv2011

Spin transport in magnetically ordered systems: effect of the lattice relaxation time

Spin resistivity $R$ has been shown to result mainly from the scattering of itinerant spins with magnetic impurities and lattice spins. $R$ is proportional to the spin-spin correlation so that its behavior is very complicated near and at the magnetic phase transition of the lattice spins. For the time being there are many new experimental data on the spin resistivity going from semiconductors to superconductors. Depending on materials, various behaviors have been observed. There is however no theory so far which gives a unified mechanism for spin resistivity in magnetic materials. Recently, we have showed Monte Carlo results for different systems. We found that the spin resistivity is very different from one material to another. In this paper, we show for the first time how the dynamic relaxation time of the lattice spins affects the resistivity of itinerant spins observed in Monte Carlo simulation.

cond-mat.stat-mech↗

arXiv2010

Spin Resistivity in the Frustrated $J_1-J_2$ Model

We study in this paper the resistivity encountered by Ising itinerant spins traveling in the so-called $J_1-J_2$ frustrated simple cubic Ising lattice. For the lattice, we take into account the interactions between nearest-neighbors and next-nearest-neighbors, $J_1$ and $J_2$ respectively. Itinerant spins interact with lattice spins via a distance-dependent interaction. We also take into account an interaction between itinerant spins. The lattice is frustrated in a range of $J_2$ in which we show that it undergoes a very strong first-order transition. Using Monte Carlo simulation, we calculate the resistivity $ρ$ of the itinerant spins and show that the first-order transition of the lattice causes a discontinuity of $ρ$.

cond-mat.stat-mech↗