Sungmin Hwang

The first passage time (FPT) for random walks is a key indicator of how fast information diffuses in a given system. Despite the role of FPT as a fundamental feature in transport phenomena, its behavior, particularly in heterogeneous networks, is not yet fully understood. Here, we study, both analytically and numerically, the scaling behavior of the FPT distribution to a given target node, averaged over all starting nodes. We find that random walks arrive quickly at a local hub, and therefore, t

We successfully developed a 3D electrical circuit model consisting of resistances and intrinsic diodes to analyze the current spreading effect in an InGaN/GaN multiple-quantum-well light-emitting diode. Each circuit element was formulated by physical parameters such as structural dimensions or material properties. We obtained a good agreement between the measured 2D light intensity distribution emitted from the surface of a fabricated device and that calculated with our model. With our design to

Spectral dimensions have been widely used to understand transport properties on regular and fractal lattices. However, they have received little attention with regard to complex networks such as scale-free and small-world networks. Here, we study the spectral dimension and the return-to-origin probability of random walks on hierarchical scale-free networks, which can be either fractal or nonfractal depending on the weight of the shortcuts. Applying the renormalization-group (RG) approach to a Ga

A neuromorphic system is composed of hardware-based artificial neurons and synaptic devices, designed to improve the efficiency of neural computations inspired by energy-efficient and parallel operations of the biological nervous system. A synaptic device-based array can compute vector-matrix multiplication (VMM) with given input voltage signals, as a non-volatile memory device stores the weight information of the neural network in the form of conductance or capacitance. However, unlike software

Exploring the World Wide Web has become one of the key issues in information science, specifically in view of its application to the PageRank-like algorithms used in search engines. The random walk approach has been employed to study such a problem. The probability of return to the origin (RTO) of random walks is inversely related to how information can be accessed during random surfing. We find analytically that the RTO probability for a given starting node shows a crossover from a slow to a fa

We perform a system-level simulation of hardware spiking neural network (SNN) consisting of silicon-based synaptic transistors and integrate-and-fire (I&F) neuron circuits. Using electrical models of the synaptic device and I&F neuron circuit, a three-layer fully connected SNN in hardware is presented for MNIST pattern recognition by means of ex situ training. Right-justified rate coding is employed as an information encoding method, and negative weight values are implemented by a pair of the sy

Abstract In his celebrated model of adaptation, Fisher assumed a smooth phenotype fitness map with one optimum. This assumption is at odds with the rugged..... Fisher’s geometric model was originally introduced to argue that complex adaptations must occur in small steps because of pleiotropic constraints. When supplemented with the assumption of additivity of mutational effects on phenotypic traits, it provides a simple mechanism for the emergence of genotypic epistasis from the nonlinear mappin

Mutually connected components (MCCs) play an important role as a measure of resilience in the study of interdependent networks. Despite their importance, an efficient algorithm to obtain the statistics of all MCCs during the removal of links has thus far been absent. Here, using a well-known fully dynamic graph algorithm, we propose an efficient algorithm to accomplish this task. We show that the time complexity of this algorithm is approximately O(N(1.2)) for random graphs, which is more effici

In this work, we present a hardware neural network with capacitor-based synaptic devices. A capacitor-based synaptic device was developed using a MOS capacitor structure with a charge trapping layer. Due to the flat band voltage shift by charge trapping and its non-linear <inline-formula> <tex-math notation="LaTeX">${C} - {V}$ </tex-math></inline-formula> characteristics, multilevel weight values could be implemented by the charge occurring when charging and discharging the capacitor. The vector

The effect of intracellular acidification and subsequent pH recovery in sensory neurons has not been well characterized. We have studied the mechanisms underlying Ca(2+)-induced acidification and subsequent recovery of intracellular pH (pH(i)) in rat trigeminal ganglion neurons and report their effects on neuronal excitability. Glutamate (500 μM) and capsaicin (1 μM) increased intracellular Ca(2+) concentration ([Ca(2+)](i)) with a following decrease in pH(i). The recovery of [Ca(2+)](i) to the

The progress of artificial intelligence and the development of large-scale neural networks have significantly increased computational costs and energy consumption. To address these challenges, researchers are exploring low-power neural network implementation approaches and neuromorphic computing systems are being highlighted as potential candidates. Specifically, the development of high-density and reliable synaptic devices, which are the key elements of neuromorphic systems, is of particular in

ABSTRACT Thiamine biosynthesis is commonly regulated by a riboswitch mechanism; however, the enzymatic steps and regulation of this pathway in archaea are poorly understood. Haloferax volcanii , one of the representative archaea, uses a eukaryote-like Thi4 (thiamine thiazole synthase) for the production of the thiazole ring and condenses this ring with a pyrimidine moiety synthesized by an apparent bacterium-like ThiC (2-methyl-4-amino-5-hydroxymethylpyrimidine [HMP] phosphate synthase) branch.

Spiking neural networks (SNNs) have attracted many researchers’ interests due to its biological plausibility and event-driven characteristic. In particular, recently, many studies on high-performance SNNs comparable to the conventional analog-valued neural networks (ANNs) have been reported by converting weights trained from ANNs into SNNs. However, unlike ANNs, SNNs have an inherent latency that is required to reach the best performance because of differences in operations of neuron. In SNNs, n

Spiking neural networks (SNNs) are considered as the third generation of artificial neural networks, having the potential to improve the energy efficiency of conventional computing systems. Although the firing rate of a spiking neuron is an approximation of rectified linear unit (ReLU) activation in an analog-valued neural network (ANN), there remain many challenges to be overcome owing to differences in operation between ANNs and SNNs. Unlike actual biological and biophysical processes, various