Interplay of network structure and talent configuration on wealth dynamics.

The economic success of individuals is often determined by a combination of talent, luck, and assistance from others. We introduce an agent-based model that simultaneously considers talent, luck, and social interaction. This model allows us to explore how network structure (how agents interact) and talent distribution among agents affect the dynamics of capital accumulation through analytical and numerical methods.

Hidden multiscale organization and robustness of real multiplex networks.

Hidden geometry enables the investigation of complex networks at different scales. Extending this framework to multiplex networks, we uncover a different kind of mesoscopic organization in real multiplex systems, named clan, a group of nodes that preserve local geometric arrangements across layers. Furthermore, we reveal the intimate relationship between the unfolding of clan structure and mutual percolation against targeted attacks, leading to an ambivalent role of clans: making a system fragile yet less prone to complete shattering.

Spontaneous emergence of rudimentary music detectors in deep neural networks.

Music exists in almost every society, has universal acoustic features, and is processed by distinct neural circuits in humans even with no experience of musical training. However, it remains unclear how these innate characteristics emerge and what functions they serve. Here, using an artificial deep neural network that models the auditory information processing of the brain, we show that units tuned to music can spontaneously emerge by learning natural sound detection, even without learning music.

Unexpected advantages of exploitation for target searches in complex networks.

Exploitation universally emerges in various decision-making contexts, e.g., animals foraging, web surfing, the evolution of scientists' research topics, and our daily lives.

Unraveling hidden interactions in complex systems with deep learning.

Rich phenomena from complex systems have long intrigued researchers, and yet modeling system micro-dynamics and inferring the forms of interaction remain challenging for conventional data-driven approaches, being generally established by scientists with human ingenuity. In this study, we propose AgentNet, a model-free data-driven framework consisting of deep neural networks to reveal and analyze the hidden interactions in complex systems from observed data alone. AgentNet utilizes a graph attention network with novel variable-wise attention to model the interaction between individual agents, and employs various encoders and decoders that can be selectively applied to any desired system.

Impact of environmental changes on the dynamics of temporal networks.

Dynamics of complex social systems has often been described in the framework of temporal networks, where links are considered to exist only at the moment of interaction between nodes. Such interaction patterns are not only driven by internal interaction mechanisms, but also affected by environmental changes. To investigate the impact of the environmental changes on the dynamics of temporal networks, we analyze several face-to-face interaction datasets using the multiscale entropy (MSE) method to find that the observed temporal correlations can be categorized according to the environmental similarity of datasets such as classes and break times in schools.

Inertial effects on the Brownian gyrator.

The recent interest into the Brownian gyrator has been confined chiefly to the analysis of Brownian dynamics both in theory and experiment despite the applicability of general cases with definite mass. Considering mass explicitly in the solution of the Fokker-Planck equation and Langevin dynamics simulations, we investigate how inertia can change the dynamics and energetics of the Brownian gyrator. In the Langevin model, the inertia reduces the nonequilibrium effects by diminishing the declination of the probability density function and the mean of a specific angular momentum, j_{θ}, as a measure of rotation.

Quantumness and thermodynamic uncertainty relation of the finite-time Otto cycle.

To reveal the role of the quantumness in the Otto cycle and to discuss the validity of the thermodynamic uncertainty relation (TUR) in the cycle, we study the quantum Otto cycle and its classical counterpart. In particular, we calculate exactly the mean values and relative error of thermodynamic quantities. In the quasistatic limit, quantumness reduces the productivity and precision of the Otto cycle compared to that in the absence of quantumness, whereas in the finite-time mode, it can increase the cycle's productivity and precision.

Encoding Multiple Virtual Signals in DNA Barcodes with Single-Molecule FRET.

DNA barcoding provides a way to label a myriad of different biological molecules using the extreme programmability in DNA sequence synthesis. Fluorescence imaging is presumably the most easy-to-access method for DNA barcoding, yet large spectral overlaps between fluorescence dyes severely limit the numbers of barcodes that can be detected simultaneously. We here demonstrate the use of single-molecule fluorescence resonance energy transfer (FRET) to encode virtual signals in DNA barcodes using conventional two-color fluorescence microscopy.

Learning Entropy Production via Neural Networks.

This Letter presents a neural estimator for entropy production (NEEP), that estimates entropy production (EP) from trajectories of relevant variables without detailed information on the system dynamics. For steady state, we rigorously prove that the estimator, which can be built up from different choices of deep neural networks, provides stochastic EP by optimizing the objective function proposed here. We verify the NEEP with the stochastic processes of the bead spring and discrete flashing ratchet models and also demonstrate that our method is applicable to high-dimensional data and can provide coarse-grained EP for Markov systems with unobservable states.

Dissecting landscape art history with information theory.

Painting has played a major role in human expression, evolving subject to a complex interplay of representational conventions, social interactions, and a process of historization. From individual qualitative work of art historians emerges a metanarrative that remains difficult to evaluate in its validity regarding emergent macroscopic and underlying microscopic dynamics. The full scope of granular data, the summary statistics, and consequently, also their bias simply lie beyond the cognitive limit of individual qualitative human scholarship.

Finite-time quantum Otto engine: Surpassing the quasistatic efficiency due to friction.

In finite-time quantum heat engines, some work is consumed to drive a working fluid accompanying coherence, which is called "friction." To understand the role of friction in quantum thermodynamics, we present a couple of finite-time quantum Otto cycles with two different baths: Agarwal versus Lindbladian. We solve them exactly and compare the performance of the Agarwal engine with that of the Lindbladian engine.

Watching helical membrane proteins fold reveals a common N-to-C-terminal folding pathway.

To understand membrane protein biogenesis, we need to explore folding within a bilayer context. Here, we describe a single-molecule force microscopy technique that monitors the folding of helical membrane proteins in vesicle and bicelle environments. After completely unfolding the protein at high force, we lower the force to initiate folding while transmembrane helices are aligned in a zigzag manner within the bilayer, thereby imposing minimal constraints on folding.

Early onset of structural inequality in the formation of collaborative knowledge in all Wikimedia projects.

The Wikimedia project, including Wikipedia, is one of the largest communal data sets and has served as a representative medium to convey collective knowledge in the twenty-first century. Researchers have believed that the analysis of these collaborative digital data sets provides a unique window into the processes of collaborative knowledge formation; yet, in reality, most previous studies have usually focused on its narrow subsets. Here, by analysing all 863 Wikimedia projects (various types and in different languages), we find evidence for a universal growth pattern in communal data formation.

Role of hubs in the synergistic spread of behavior.

The spread of behavior in a society has two major features: the synergy of multiple spreaders and the dominance of hubs. While strong synergy is known to induce mixed-order transitions (MOTs) at percolation, the effects of hubs on the phenomena are yet to be clarified. By analytically solving the generalized epidemic process on random scale-free networks with the power-law degree distribution p_{k}∼k^{-α}, we clarify how the dominance of hubs in social networks affects the conditions for MOTs.

Heterogeneity in chromatic distance in images and characterization of massive painting data set.

Painting is an art form that has long functioned as a major channel for the creative expression and communication of humans, its evolution taking place under an interplay with the science, technology, and social environments of the times. Therefore, understanding the process based on comprehensive data could shed light on how humans acted and manifested creatively under changing conditions. Yet, there exist few systematic frameworks that characterize the process for painting, which would require robust statistical methods for defining painting characteristics and identifying human's creative developments, and data of high quality and sufficient quantity.

Dynamic topologies of activity-driven temporal networks with memory.

We propose dynamic scaling in temporal networks with heterogeneous activities and memory and provide a comprehensive picture for the dynamic topologies of such networks, in terms of the modified activity-driven network model [H. Kim et al., Eur.

Jamming and condensation in one-dimensional driven flow.

We revisit the slow-bond (SB) problem of the one-dimensional (1D) totally asymmetric simple exclusion process (TASEP) with modified hopping rates. In the original SB problem, it turns out that a local defect is always relevant to the system as jamming, so that phase separation occurs in the 1D TASEP. However, crossover scaling behaviors are also observed as finite-size effects.

Effects of a local defect on one-dimensional nonlinear surface growth.

The slow-bond problem is a long-standing question about the minimal strength ε_{c} of a local defect with global effects on the Kardar-Parisi-Zhang (KPZ) universality class. A consensus on the issue has been delayed due to the discrepancy between various analytical predictions claiming ε_{c}=0 and numerical observations claiming ε_{c}>0. We revisit the problem via finite-size scaling analyses of the slow-bond effects, which are tested for different boundary conditions through extensive Monte Carlo simulations.

Universality classes of the generalized epidemic process on random networks.

We present a self-contained discussion of the universality classes of the generalized epidemic process (GEP) on Poisson random networks, which is a simple model of social contagions with cooperative effects. These effects lead to rich phase transitional behaviors that include continuous and discontinuous transitions with tricriticality in between. With the help of a comprehensive finite-size scaling theory, we numerically confirm static and dynamic scaling behaviors of the GEP near continuous phase transitions and at tricriticality, which verifies the field-theoretical results of previous studies.

Intellectual interchanges in the history of the massive online open-editing encyclopedia, Wikipedia.

Wikipedia is a free Internet encyclopedia with an enormous amount of content. This encyclopedia is written by volunteers with various backgrounds in a collective fashion; anyone can access and edit most of the articles. This open-editing nature may give us prejudice that Wikipedia is an unstable and unreliable source; yet many studies suggest that Wikipedia is even more accurate and self-consistent than traditional encyclopedias.

Chromosome-like organization of an asymmetrical ring polymer confined in a cylindrical space.

To what extent does a confined polymer show chromosome-like organization? Using molecular dynamics simulations, we study a model Escherichia coli (E. coli) chromosome: an asymmetrical ring polymer, formed by small monomers on one side and big monomers on the other confined in a concentric-shell or simple cylinder with closed ends. The ring polymer is organized in the way observed for the E.

Anatomy of scientific evolution.

The quest for historically impactful science and technology provides invaluable insight into the innovation dynamics of human society, yet many studies are limited to qualitative and small-scale approaches. Here, we investigate scientific evolution through systematic analysis of a massive corpus of digitized English texts between 1800 and 2008. Our analysis reveals great predictability for long-prevailing scientific concepts based on the levels of their prior usage.

Effects of junctional correlations in the totally asymmetric simple exclusion process on random regular networks.

We investigate the totally asymmetric simple exclusion process on closed and directed random regular networks, which is a simple model of active transport in the one-dimensional segments coupled by junctions. By a pair mean-field theory and detailed numerical analyses, it is found that the correlations at junctions induce two notable deviations from the simple mean-field theory, which neglects these correlations: (1) the narrower range of particle density for phase coexistence and (2) the algebraic decay of density profile with exponent 1/2 even outside the maximal-current phase. We show that these anomalies are attributable to the effective slow bonds formed by the network junctions.

A polymer in a crowded and confined space: effects of crowder size and poly-dispersity.

DNA compaction in a bacterial cell is in part carried out by entropic (depletion) forces induced by "free" proteins or crowding particles in the cytoplasm. Indeed, recent in vitro experiments highlight these effects by showing that they alone can condense the E. coli chromosome to its in vivo size.