Experimental evidence for vibrational resonance and enhanced signal transmission in Chua's circuit

We consider a single Chua's circuit and a system of a unidirectionally coupled n-Chua's circuits driven by a biharmonic signal with two widely different frequencies \omega and \Omega, where \Omega >> \omega. We show experimental evidence for vibrational resonance in the single Chua's circuit and undamped signal propagation of a low-frequency signal in the system of n-coupled Chua's circuits where only the first circuit is driven by the biharmonic signal. In the single circuit, we illustrate the mechanism of vibrational resonance and the influence of the biharmonic signal parameters on the resonance. In the n(= 75)-coupled Chua's circuits enhanced propagation of low-frequency signal is found to occur for a wide range of values of the amplitude of the high-frequency input signal and coupling parameter. The response amplitude of the ith circuit increases with i and attains a saturation. Moreover, the unidirectional coupling is found to act as a low-pass filter.Comment: 15 pages, 12 figures, Accepted for Publication in International Journal of Bifurcation and Chao

The VIMOS VLT Deep Survey. The different assembly history of passive and star-forming L_B >= L*_B galaxies in the group environment at z < 1

We use the VIMOS VLT Deep Survey to study the close environment of galaxies in groups at 0.2 = L*_B galaxies (Me_B = M_B + 1.1z <= -20) are identified with Me_B <= -18.25 and within a relative distance 5h^-1 kpc <= rp <= 100h^-1 kpc and relative velocity Delta v <= 500 km/s . The richness N of a group is defined as the number of Me_B <= -18.25 galaxies belonging to that group. We split our principal sample into red, passive galaxies with NUV - r >= 4.25 and blue, star-forming galaxies with NUV - r < 4.25. We find that blue galaxies with a close companion are primarily located in poor groups, while the red ones are in rich groups. The number of close neighbours per red galaxy increases with N, with n_red being proportional to 0.11N, while that of blue galaxies does not depend on N and is roughly constant. In addition, these trends are found to be independent of redshift, and only the average n_blue evolves, decreasing with cosmic time. Our results support the following assembly history of L_B >= L*_B galaxies in the group environment: red, massive galaxies were formed in or accreted by the dark matter halo of the group at early times (z >= 1), therefore their number of neighbours provides a fossil record of the stellar mass assembly of groups, traced by their richness N. On the other hand, blue, less massive galaxies have recently been accreted by the group potential and are still in their parent dark matter halo, having the same number of neighbours irrespective of N. As time goes by, these blue galaxies settle in the group potential and turn red and/or fainter, thus becoming satellite galaxies in the group. With a toy quenching model, we estimate an infall rate of field galaxies into the group environment of R_infall = 0.9 - 1.5 x 10^-4 Mpc^-3 Gyr^-1 at z ~ 0.7.Comment: Astronomy and Astrophysics, in press. 11 pages, 11 figures, 4 tables. Minor changes with respect to the first versio

Overview of the CLEF 2023 SimpleText Lab:Automatic Simplification of Scientific Texts

There is universal consensus on the importance of objective scientific information, yet the general public tends to avoid scientific literature due to access restrictions, its complex language or their lack of prior background knowledge. Academic text simplification promises to remove some of these barriers, by improving the accessibility of scientific text and promoting science literacy. This paper presents an overview of the CLEF 2023 SimpleText track addressing the challenges of text simplification approaches in the context of promoting scientific information access, by providing appropriate data and benchmarks, and creating a community of IR and NLP researchers working together to resolve one of the greatest challenges of today. The track provides a corpus of scientific literature abstracts and popular science requests. It features three tasks. First, content selection (what is in, or out?) challenges systems to select passages to include in a simplified summary in response to a query. Second, complexity spotting (what is unclear?) given a passage and a query, aims to rank terms/concepts that are required to be explained for understanding this passage (definitions, context, applications). Third, text simplification (rewrite this!) given a query, asks to simplify passages from scientific abstracts while preserving the main content.</p