Generalized gravity model for human migration

The gravity model (GM) analogous to Newton's law of universal gravitation has successfully described the flow between different spatial regions, such as human migration, traffic flows, international economic trades, etc. This simple but powerful approach relies only on the 'mass' factor represented by the scale of the regions and the 'geometrical' factor represented by the geographical distance. However, when the population has a subpopulation structure distinguished by different attributes, the estimation of the flow solely from the coarse-grained geographical factors in the GM causes the loss of differential geographical information for each attribute. To exploit the full information contained in the geographical information of subpopulation structure, we generalize the GM for population flow by explicitly harnessing the subpopulation properties characterized by both attributes and geography. As a concrete example, we examine the marriage patterns between the bride and the groom clans of Korea in the past. By exploiting more refined geographical and clan information, our generalized GM properly describes the real data, a part of which could not be explained by the conventional GM. Therefore, we would like to emphasize the necessity of using our generalized version of the GM, when the information on such nongeographical subpopulation structures is available.Comment: 14 pages, 6 figures, 2 table

String Sound Synthesizer on GPU-accelerated Finite Difference Scheme

This paper introduces a nonlinear string sound synthesizer, based on a finite difference simulation of the dynamic behavior of strings under various excitations. The presented synthesizer features a versatile string simulation engine capable of stochastic parameterization, encompassing fundamental frequency modulation, stiffness, tension, frequency-dependent loss, and excitation control. This open-source physical model simulator not only benefits the audio signal processing community but also contributes to the burgeoning field of neural network-based audio synthesis by serving as a novel dataset construction tool. Implemented in PyTorch, this synthesizer offers flexibility, facilitating both CPU and GPU utilization, thereby enhancing its applicability as a simulator. GPU utilization expedites computation by parallelizing operations across spatial and batch dimensions, further enhancing its utility as a data generator.Comment: To be appeared in ICASSP 202

Data-Reserved Periodic Diffusion LMS With Low Communication Cost Over Networks

In this paper, we analyze diffusion strategies in which all nodes attempt to estimate a common vector parameter for achieving distributed estimation in adaptive networks. Under diffusion strategies, each node essentially needs to share processed data with predefined neighbors. Although the use of internode communication has contributed significantly to improving convergence performance based on diffusion, such communications consume a huge quantity of power in data transmission. In developing low-power consumption diffusion strategies, it is very important to reduce the communication cost without significant degradation of convergence performance. For that purpose, we propose a data-reserved periodic diffusion least-mean-squares (LMS) algorithm in which each node updates and transmits an estimate periodically while reserving its measurement data even during non-update time. By applying these reserved data in an adaptation step at update time, the proposed algorithm mitigates the decline in convergence speed incurred by most conventional periodic schemes. For a period p, the total cost of communication is reduced to a factor of 1/p relative to the conventional adapt-then-combine (ATC) diffusion LMS algorithm. The loss of combination steps in this process leads naturally to a slight increase in the steady-state error as the period p increases, as is theoretically confirmed through mathematical analysis. We also prove an interesting property of the proposed algorithm, namely, that it suffers less degradation of the steady-state error than the conventional diffusion in a noisy communication environment. Experimental results show that the proposed algorithm outperforms related conventional algorithms and, in particular, outperforms ATC diffusion LMS over a network with noisy links.11Ysciescopu

Yersinia Phages and Food Safety

One of the human- and animal-pathogenic species in genus Yersinia is Yersinia enterocolitica, a food-borne zoonotic pathogen that causes enteric infections, mesenteric lymphadenitis, and sometimes sequelae such as reactive arthritis and erythema nodosum. Y. enterocolitica is able to proliferate at 4 °C, making it dangerous if contaminated food products are stored under refrigeration. The most common source of Y. enterocolitica is raw pork meat. Microbiological detection of the bacteria from food products is hampered by its slow growth rate as other bacteria overgrow it. Bacteriophages can be exploited in several ways to increase food safety with regards to contamination by Y. enterocolitica. For example, Yersinia phages could be useful in keeping the contamination of food products under control, or, alternatively, the specificity of the phages could be exploited in developing rapid and sensitive diagnostic tools for the identification of the bacteria in food products. In this review, we will discuss the present state of the research on these topics

Yersinia Phages and Food Safety

One of the human- and animal-pathogenic species in genus Yersinia is Yersinia enterocolitica, a food-borne zoonotic pathogen that causes enteric infections, mesenteric lymphadenitis, and sometimes sequelae such as reactive arthritis and erythema nodosum. Y. enterocolitica is able to proliferate at 4 °C, making it dangerous if contaminated food products are stored under refrigeration. The most common source of Y. enterocolitica is raw pork meat. Microbiological detection of the bacteria from food products is hampered by its slow growth rate as other bacteria overgrow it. Bacteriophages can be exploited in several ways to increase food safety with regards to contamination by Y. enterocolitica. For example, Yersinia phages could be useful in keeping the contamination of food products under control, or, alternatively, the specificity of the phages could be exploited in developing rapid and sensitive diagnostic tools for the identification of the bacteria in food products. In this review, we will discuss the present state of the research on these topics

T4-like Bacteriophages Isolated from Pig Stools Infect Yersinia pseudotuberculosis and Yersinia pestis Using LPS and OmpF as Receptors

The Yersinia bacteriophages fPS-2, fPS-65, and fPS-90, isolated from pig stools, have long contractile tails and elongated heads, and they belong to genus Tequatroviruses in the order Caudovirales. The phages exhibited relatively wide host ranges among Yersinia pseudotuberculosis and related species. One-step growth curve experiments revealed that the phages have latent periods of 50-80 min with burst sizes of 44-65 virions per infected cell. The phage genomes consist of circularly permuted dsDNA of 169,060, 167,058, and 167,132 bp in size, respectively, with a G + C content 35.3%. The number of predicted genes range from 267 to 271. The phage genomes are 84-92% identical to each other and ca 85% identical to phage T4. The phage receptors were identified by whole genome sequencing of spontaneous phage-resistant mutants. The phage-resistant strains had mutations in the ompF, galU, hldD, or hldE genes. OmpF is a porin, and the other genes encode lipopolysaccharide (LPS) biosynthetic enzymes. The ompF, galU, and hldE mutants were successfully complemented in trans with respective wild-type genes. The host recognition was assigned to long tail fiber tip protein Gp38, analogous to that of T-even phages such as Salmonella phage S16, specifically to the distal beta-helices connecting loops.Peer reviewe