Scale-free switching of polarization in the layered ferroelectric material CuInP2_2S6_6

Using first-principles calculations we model the out-of-plane switching of local dipoles in CuInP$_2$S$_6$ (CIPS) that are largely induced by Cu off-centering. Previously, a coherent switching of polarization via a quadruple-well potential was proposed for these materials. In the super-cells we considered, we find multiple structures with similar energies but with different local polar order. Our results suggest that the individual dipoles are weakly coupled in-plane and under an electric field at very low temperatures these dipoles in CIPS should undergo incoherent disordered switching. The barrier for switching is determined by the single Cu-ion switching barrier. This in turn suggests a scale-free polarization with a switching barrier of $\sim$ 203.6-258.0 meV, a factor of five smaller than that of HfO$_2$ (1380 meV) a prototypical scale-free ferroelectric. The mechanism of polarization switching in CIPS is mediated by the switching of each weakly interacting dipole rather than the macroscopic polarization itself as previously hypothesized. These findings reconcile prior observations of a quadruple well with sloping hysteresis loops, large ionic conductivity even at 250~K well below the Curie temperature (315~K), and a significant wake-up effects where the macroscopic polarization is slow to order and set-in under an applied electric field. We also find that computed piezoelectric response and the polarization show a linear dependence on the local dipolar order. This is consistent with having scale-free polarization and other polarization-dependent properties and opens doors for engineering tunable metastability by-design in CIPS (and related family of materials) for neuromorphic applications

Evidence of estrogenic mixture effects on the reproductive performance of fish

The official published version can be obtained from the link below - Copyright @ 2007 American Chemical SocietyRecent research into the effects of mixtures of estrogenic chemicals has revealed the capacity for similarly acting chemicals to act in combination, according to the principles of concentration addition. This means that, collectively, they may pose a significant environmental risk, even when each component is present at a low and individually ineffective concentration. The aim of this study was to investigate the ecological significance of mixture effects at low-effect concentrations by assessing the combined effect of estrogenic chemicals on the reproductive performance of fish. Pairs of fathead minnows were exposed to five estrogenic chemicals. Endpoints analyzed included fecundity, the expression of male secondary sexual characteristics, somatic indices, and vitellogenin induction. In the first phase of the study, a concentration-response analysis was performed to investigate the relative sensitivity of these endpoints. In the second phase, mixture effects at low-effect concentrations were explored by exposing fish to each of the mixture components, individually and in combination. Data from these experiments provide evidence of mixture effects on fitness and fecundity, demonstrating the capacity for chemicals to act together to affect reproductive performance, even when each component is present belowthe threshold of detectable effects. This has important implications for hazard assessment and contributes to our understanding of mixture effects at increasing levels of biological complexity.This work was funded by the European Commission, under contract EVK1-2001-00091

On the Necessary Memory to Compute the Plurality in Multi-Agent Systems

We consider the Relative-Majority Problem (also known as Plurality), in which, given a multi-agent system where each agent is initially provided an input value out of a set of $k$ possible ones, each agent is required to eventually compute the input value with the highest frequency in the initial configuration. We consider the problem in the general Population Protocols model in which, given an underlying undirected connected graph whose nodes represent the agents, edges are selected by a globally fair scheduler. The state complexity that is required for solving the Plurality Problem (i.e., the minimum number of memory states that each agent needs to have in order to solve the problem), has been a long-standing open problem. The best protocol so far for the general multi-valued case requires polynomial memory: Salehkaleybar et al. (2015) devised a protocol that solves the problem by employing $O(k 2^k)$ states per agent, and they conjectured their upper bound to be optimal. On the other hand, under the strong assumption that agents initially agree on a total ordering of the initial input values, Gasieniec et al. (2017), provided an elegant logarithmic-memory plurality protocol. In this work, we refute Salehkaleybar et al.'s conjecture, by providing a plurality protocol which employs $O(k^{11})$ states per agent. Central to our result is an ordering protocol which allows to leverage on the plurality protocol by Gasieniec et al., of independent interest. We also provide a $\Omega(k^2)$-state lower bound on the necessary memory to solve the problem, proving that the Plurality Problem cannot be solved within the mere memory necessary to encode the output.Comment: 14 pages, accepted at CIAC 201

Learning from the past and considering the future of chemicals in the environment

Copyright © 2020 The Authors, some rights reserved. Knowledge of the hazards and associated risks from chemicals discharged to the environment has grown considerably over the past 40 years. This improving awareness stems from advances in our ability to measure chemicals at low environmental concentrations, recognition of a range of effects on organisms, and a worldwide growth in expertise. Environmental scientists and companies have learned from the experiences of the past; in theory, the next generation of chemicals will cause less acute toxicity and be less environmentally persistent and bioaccumulative. However, researchers still struggle to establish whether the nonlethal effects associated with some modern chemicals and substances will have serious consequences for wildlife. Obtaining the resources to address issues associated with chemicals in the environment remains a challenge.NERC Environmental Bioinformatics Centre: NE/S000100/1

The ACE Project: a synopsis of in vivo studies to predict estrogenic mixture effects in freshwater and marine fish

Society of Environmental Toxicology and Chemistry - SETAC Europe 15th Annual Meeting, Lille, France, May 2005.This work is part of the ACE project (ACE, EVK1-CT-2001-100) which aim is to investigate multi-component mixtures of estrogenic compounds in aquatic ecosystems. Here we present a synopsis of in vivo data related with the joint estrogenic action of five estrogenic compounds (17ß-estradiol, ethynylestradiol, nonylphenol, octylphenol and bisphenol-A) on vitellogenesis in fathead minnow (Pimephales promelas) and sea bass (Dicentrarchus labrax). The studies were conducted with freshwater adult males and marine juveniles under flow through exposure conditions for two weeks. In the first step, fish were exposed to the five compounds individually in order to generate concentration- response curves. Therefore mixture effects were predicted on the basis of the potency of each compound by using the model of concentration addition (CA). Finally, the compounds were tested as a mixture at equipotent concentrations, and the observed mixture effects were compared to the predictions. The mixture studies showed an good agreement between observed and predicted effects and provided evidence that CA can be used as a predictive tool for the effect assessment of mixtures of (xeno)estrogens in freshwater or marine ecosystems. The differences/limitations of running in vivo mixture studies with freshwater and marine species will be discussed.Comissão Europeia (CE) - ACE project - ACE, EVK1-CT-2001-100

Collective Animal Behavior from Bayesian Estimation and Probability Matching

Animals living in groups make movement decisions that depend, among other factors, on social interactions with other group members. Our present understanding of social rules in animal collectives is based on empirical fits to observations and we lack first-principles approaches that allow their derivation. Here we show that patterns of collective decisions can be derived from the basic ability of animals to make probabilistic estimations in the presence of uncertainty. We build a decision-making model with two stages: Bayesian estimation and probabilistic matching.
In the first stage, each animal makes a Bayesian estimation of which behavior is best to perform taking into account personal information about the environment and social information collected by observing the behaviors of other animals. In the probability matching stage, each animal chooses a behavior with a probability given by the Bayesian estimation that this behavior is the most appropriate one. This model derives very simple rules of interaction in animal collectives that depend only on two types of reliability parameters, one that each animal assigns to the other animals and another given by the quality of the non-social information. We test our model by obtaining theoretically a rich set of observed collective patterns of decisions in three-spined sticklebacks, Gasterosteus aculeatus, a shoaling fish species. The quantitative link shown between probabilistic estimation and collective rules of behavior allows a better contact with other fields such as foraging, mate selection, neurobiology and psychology, and gives predictions for experiments directly testing the relationship between estimation and collective behavior

Multi-scale Inference of Interaction Rules in Animal Groups Using Bayesian Model Selection

Inference of interaction rules of animals moving in groups usually relies on an analysis of large scale system behaviour. Models are tuned through repeated simulation until they match the observed behaviour. More recent work has used the fine scale motions of animals to validate and fit the rules of interaction of animals in groups. Here, we use a Bayesian methodology to compare a variety of models to the collective motion of glass prawns (Paratya australiensis). We show that these exhibit a stereotypical ‘phase transition’, whereby an increase in density leads to the onset of collective motion in one direction. We fit models to this data, which range from: a mean-field model where all prawns interact globally; to a spatial Markovian model where prawns are self-propelled particles influenced only by the current positions and directions of their neighbours; up to non-Markovian models where prawns have ‘memory’ of previous interactions, integrating their experiences over time when deciding to change behaviour. We show that the mean-field model fits the large scale behaviour of the system, but does not capture fine scale rules of interaction, which are primarily mediated by physical contact. Conversely, the Markovian self-propelled particle model captures the fine scale rules of interaction but fails to reproduce global dynamics. The most sophisticated model, the non-Markovian model, provides a good match to the data at both the fine scale and in terms of reproducing global dynamics. We conclude that prawns' movements are influenced by not just the current direction of nearby conspecifics, but also those encountered in the recent past. Given the simplicity of prawns as a study system our research suggests that self-propelled particle models of collective motion should, if they are to be realistic at multiple biological scales, include memory of previous interactions and other non-Markovian effects