SearcharxivSearch

arXiv subjects

Seong-Gyu Yang

Publications and source records attributed to Seong-Gyu Yang.

9 recordsLinked to original sources

Multifractal Scaling in Hi-C Maps

The three-dimensional organization of the genome exhibits rich, scale-dependent structure, as revealed by both chromosome contact maps (e.g., Hi-C maps) and chromatin density measured by microscopy. Recent studies have reported multifractal scaling in these data. Yet, the origin of this scaling behavior remains unclear: existing efforts describe it through postulated models. Here, we show that the multifractal structure of Hi-C maps is a direct consequence of the power-law contact probability $P(s)$, which is itself an empirical observable measured from Hi-C maps. Starting from $P(s)$ with a single exponent $γ$, we analytically derive the mass exponent $τ(q)$, which characterizes how the $q$-th moment of contact density scales with box size $l$ used to coarse-grain the genomic coordinate. This multifractal behavior reflects the geometric competition between intra- and inter-segment contacts. We find that the slope of $τ(q)$ at large $q$ is given by $2 -γ$ when $γ<1$, and by $1$ when $γ\geq 1$. We further show that this behavior is robust to noise and consistent across diverse organisms, indicating that it is a universal feature of chromatin organization. We extend our analysis into double-exponent $P(s)$, and show the $l$ dependence in multifractal behavior. Taken together, these results provide a physical explanation for multifractal scaling and establish a direct link between the multifractality in Hi-C maps and polymer contact statistics, with the large-$q$ slope of $τ(q)$ mapping onto a known polymer contact exponent.

cond-mat.stat-mech

Beyond average: heterogeneous first-passage dynamics in many-particle systems with resetting

We study how stochastic resetting affects first-passage processes in systems of many interacting particles. While resetting is well understood for single-particle dynamics, its consequences for collective behavior remain less clear. We consider a protocol in which all surviving particles are reset to the position of the most extreme one, motivated by problems in artificial selection and avoidance. Using stochastic simulations of particles diffusing in a confining potential with an absorbing boundary, we examine two notions of arrival: when the first particle reaches the boundary and the point at which half of the particles do. We find that resetting produces broad distributions of arrival times with heavy tails and extended plateaus that span several orders of magnitude. As the resetting rate increases, the mean arrival time grows and diverges beyond a threshold. Trajectory-level analysis also reveals strong heterogeneity, with very short and very long absorption times. These results show that collective resetting lacks a single characteristic time scale and that the definition of arrival is crucial for understanding and controlling such systems.

cond-mat.stat-mech

General Theory for Group Resetting with Application to Avoidance

We present a general theoretical framework for group resetting dynamics in a potential landscape. While traditional resetting models typically focus on a single particle, we consider a group of particles whose collective dynamics govern the resetting. We extend existing resetting theories to cover extreme-value group resetting. This has applications from bacterial evolution under antibiotic pressure to swarm-search optimization. Using renewal theory, we derive a Fokker-Planck equation for the spatial distribution of the group's center of mass, treated as an effective particle. This formalism yields analytical expressions for key observables such as the stationary mean position and variance. We also study a group avoidance problem, where the particles must avoid an undesirable region. Such problems have recently been studied in contexts such as preventing critically high water levels in dams and controlling excessive financial leverage. Our framework offers new insight into how resetting can optimize group-level search and avoidance strategies.

cond-mat.stat-mech

Interplay between intraspecific suppression and environment in shaping biodiversity

Understanding the mechanisms that sustain high biodiversity remains a central challenge. MacArthur's classical consumer-resource model (MCRM) suggests that consumer diversity is limited by the number of available resources, yet empirical observations often exceed this bound. To address this, we extend the generalized consumer-resource model by incorporating intraspecific suppression and analyze its effects using the dynamical mean-field theory. Our results show that intraspecific suppression promotes biodiversity by preventing the emergence of dominant species and enabling more species to coexist, particularly in resource-rich environments. Furthermore, our results provide analytical bounds on relative diversity, demonstrating that the number of coexisting consumer species can exceed the number of resource kinds. This highlights the critical role of intraspecific suppression and environmental factors in promoting coexistence.

q-bio.PE

Equilibrium-preserving Laplacian renormalization group

Diffusion over networks has recently been used to define spatiotemporal scales and extend Kadanoff block spins of Euclidean space to supernodes of networks in the Laplacian renormalization group (LRG). Yet, its ad hoc coarse-graining procedure remains underdeveloped and unvalidated, limiting its broader applicability. Here we rigorously formulate an LRG preserving the equilibrium state, offering a principled coarse-graining procedure. We construct the renormalized Laplacian matrix preserving dominant spectral properties using a proper, quasi-complete basis transformation and the renormalized adjacency matrix preserving mean connectivity from equilibrium-state flows among supernodes. Applying recursively this equilibrium-preserving LRG to various hypergraphs, we find that in hypertrees with low spectral dimensions vertex degree and hyperedge cardinality distributions flow toward Poissonian forms, while in hypergraphs lacking a finite spectral dimension they broaden toward power-law forms when starting from Poissonian ones, revealing how informational, structural, and dynamical scale-invariances are interrelated.

cond-mat.stat-mech

Target search on networks-within-networks with applications to protein-DNA interactions

We present a novel framework for understanding node target search in systems organized as hierarchical networks-within-networks. Our work generalizes traditional search models on complex networks, where the mean-first passage time is typically inversely proportional to the node degree. However, real-world search processes often span multiple network layers, such as moving from an external environment into a local network, and then navigating several internal states. This multilayered complexity appears in scenarios such as international travel networks, tracking email spammers, and the dynamics of protein-DNA interactions in cells. Our theory addresses these complex systems by modeling them as a three-layer multiplex network: an external source layer, an intermediate spatial layer, and an internal state layer. We derive general closed-form solutions for the steady-state flux through a target node, which serves as a proxy for inverse mean-first passage time. Our results reveal a universal relationship between search efficiency and network-specific parameters. This work extends the current understanding of multiplex networks by focusing on systems with hierarchically connected layers. Our findings have broad implications for fields ranging from epidemiology to cellular biology and provide a more comprehensive understanding of search dynamics in complex, multilayered environments.

physics.bio-ph

Power-grid stability predictions using transferable machine learning

Complex network analyses have provided clues to improve power-grid stability with the help of numerical models. The high computational cost of numerical simulations, however, has inhibited the approach, especially when it deals with the dynamic properties of power grids such as frequency synchronization. In this study, we investigate machine learning techniques to estimate the stability of power-grid synchronization. We test three different machine learning algorithms -- random forest, support vector machine, and artificial neural network -- training them with two different types of synthetic power grids consisting of homogeneous and heterogeneous input-power distribution, respectively. We find that the three machine learning models better predict the synchronization stability of power-grid nodes when they are trained with the heterogeneous input-power distribution than the homogeneous one. With the real-world power grids of Great Britain, Spain, France, and Germany, we also demonstrate that the machine learning algorithms trained on synthetic power grids are transferable to the stability prediction of the real-world power grids, which implies the prospective applicability of machine learning techniques on power-grid studies.

physics.soc-ph

Benford's Law and First Letter of Word

A universal First-Letter Law (FLL) is derived and described. It predicts the percentages of first letters for words in novels. The FLL is akin to Benford's law (BL) of first digits, which predicts the percentages of first digits in a data collection of numbers. Both are universal in the sense that FLL only depends on the numbers of letters in the alphabet, whereas BL only depends on the number of digits in the base of the number system. The existence of these types of universal laws appears counter-intuitive. Nonetheless both describe data very well. Relations to some earlier works are given. FLL predicts that an English author on the average starts about 16 out of 100 words with the English letter `t'. This is corroborated by data, yet an author can freely write anything. Fuller implications and the applicability of FLL remain for the future.

cs.CL

Formation of a Topological Monopole Lattice and its Dynamics in Three-dimensional Chiral Magnets

Topologically protected swirl of the magnetic texture known as the Skyrmion has become ubiqui- tous in both metallic and insulating chiral magnets. Meanwhile the existence of its three-dimensional analogue, known as the magnetic monopole, has been suggested by various indirect experimental sig- natures in MnGe compound. Theoretically, Ginzburg-Landau arguments in favor of the formation of a three-dimensional crystal of monopoles and anti-monopoles have been put forward, however no microscopic model Hamiltonian was shown to support such a phase. Here we present strong numerical evidence from Monte Carlo simulations for the formation of a rock-salt crystal structure of monopoles and anti-monopoles in short-period chiral magnets. Real-time simulation of the spin dynamics suggests there is only one collective mode in the monopole crystal state in the frequency range of several GHz for the material parameters of MnGe.

cond-mat.str-el