Challenges in understanding meiosis: fostering metaconceptual awareness among university biology students

In this study, firstly, university biology students’ conceptual understanding and potential misconceptions concerning meiosis were studied. Secondly, an easily applicable drawing task was used to foster students’ metaconceptual awareness which would help them to reach conceptual change. A quasi-experimental design with a non-equivalent control group was conducted. The students (N = 82) were divided into experimental and control groups. The control groups attended traditional teaching, i.e. lectures with practicals, whilst the experimental groups had an additional activating task before practicals. In the activating task, the students drew the selected phases of meiosis and marked given concepts of meiosis in the drawing. The drawings were scored and the solutions were discussed in detail with the students. After the activating task, the traditional practicals were held for both groups. After a week, both experimental and control groups were given the same task. The results show that students in the experimental group understood meiosis significantly better than the control group, who had more misconceptions after the instruction compared to the experimental group. Thus, fostering students’ metaconceptual awareness is crucial and relatively easy to apply, also in higher education.</p

Gaussian states provide universal and versatile quantum reservoir computing

We establish the potential of continuous-variable Gaussian states in performing reservoir computing with linear dynamical systems in classical and quantum regimes. Reservoir computing is a machine learning approach to time series processing. It exploits the computational power, high-dimensional state space and memory of generic complex systems to achieve its goal, giving it considerable engineering freedom compared to conventional computing or recurrent neural networks. We prove that universal reservoir computing can be achieved without nonlinear terms in the Hamiltonian or non-Gaussian resources. We find that encoding the input time series into Gaussian states is both a source and a means to tune the nonlinearity of the overall input-output map. We further show that reservoir computing can in principle be powered by quantum fluctuations, such as squeezed vacuum, instead of classical intense fields. Our results introduce a new research paradigm for quantum reservoir computing and the engineering of Gaussian quantum states, pushing both fields into a new direction.Comment: 13 pages, 4 figure

Opportunities in Quantum Reservoir Computing and Extreme Learning Machines

Quantum reservoir computing (QRC) and quantum extreme learning machines (QELM) are two emerging approaches that have demonstrated their potential both in classical and quantum machine learning tasks. They exploit the quantumness of physical systems combined with an easy training strategy, achieving an excellent performance. The increasing interest in these unconventional computing approaches is fueled by the availability of diverse quantum platforms suitable for implementation and the theoretical progresses in the study of complex quantum systems. In this review article, recent proposals and first experiments displaying a broad range of possibilities are reviewed when quantum inputs, quantum physical substrates and quantum tasks are considered. The main focus is the performance of these approaches, on the advantages with respect to classical counterparts and opportunities

Analytical Evidence of Nonlinearity in Qubits and Continuous-Variable Quantum Reservoir Computing

The natural dynamics of complex networks can be harnessed for information processing purposes. A paradigmatic example are artificial neural networks used for machine learning. In this context, quantum reservoir computing (QRC) constitutes a natural extension of the use of classical recurrent neural networks using quantum resources for temporal information processing. Here, we explore the fundamental properties of QRC systems based on qubits and continuous variables. We provide analytical results that illustrate how nonlinearity enters the input–output map in these QRC implementations. We find that the input encoding through state initialization can serve to control the type of nonlinearity as well as the dependence on the history of the input sequences to be processed.</p

Opportunities in Quantum Reservoir Computing and Extreme Learning Machines

Quantum reservoir computing and quantum extreme learning machines are two emerging approaches that have demonstrated their potential both in classical and quantum machine learning tasks. They exploit the quantumness of physical systems combined with an easy training strategy, achieving an excellent performance. The increasing interest in these unconventional computing approaches is fueled by the availability of diverse quantum platforms suitable for implementation and the theoretical progresses in the study of complex quantum systems. In this review article, recent proposals and first experiments displaying a broad range of possibilities are reviewed when quantum inputs, quantum physical substrates and quantum tasks are considered. The main focus is the performance of these approaches, on the advantages with respect to classical counterparts and opportunities

Network Geometry and Complexity

(28 pages, 11 figures)Higher order networks are able to characterize data as different as functional brain networks, protein interaction networks and social networks beyond the framework of pairwise interactions. Most notably higher order networks include simplicial complexes formed not only by nodes and links but also by triangles, tetrahedra, etc. More in general, higher-order networks can be cell-complexes formed by gluing convex polytopes along their faces. Interestingly, higher order networks have a natural geometric interpretation and therefore constitute a natural way to explore the discrete network geometry of complex networks. Here we investigate the rich interplay between emergent network geometry of higher order networks and their complexity in the framework of a non-equilibrium model called Network Geometry with Flavor. This model, originally proposed for capturing the evolution of simplicial complexes, is here extended to cell-complexes formed by subsequently gluing different copies of an arbitrary regular polytope. We reveal the interplay between complexity and geometry of the higher order networks generated by the model by studying the emergent community structure and the degree distribution as a function of the regular polytope forming its building blocks. Additionally, we discuss the underlying hyperbolic nature of the emergent geometry and we relate the spectral dimension of the higher-order network to the dimension and nature of its building blocks

How to make a sex chromosome

Sex chromosomes can evolve once recombination is halted between a homologous pair of chromosomes. Owing to detailed studies using key model systems, we have a nuanced understanding and a rich review literature of what happens to sex chromosomes once recombination is arrested. However, three broad questions remain unanswered. First, why do sex chromosomes stop recombining in the first place? Second, how is recombination halted? Finally, why does the spread of recombination suppression, and therefore the rate of sex chromosome divergence, vary so substantially across clades? In this review, we consider each of these three questions in turn to address fundamental questions in the field, summarize our current understanding, and highlight important areas for future work

Dynamical Phase Transitions in Quantum Reservoir Computing

Trabajo presentado en la 24th Annual Conference on Quantum Information Processing (QIP 21), celebrada online del 1 al 5 de febrero de 2021

Dynamical Phase Transitions in Quantum Reservoir Computing

Trabajo presentado en la Conference on Complex Systems (CCS), celebrada en Lyon del 25 al 29 de octubre de 2021