The development of temperament and character during adolescence: The processes and phases of change

AbstractWe studied the pattern of personality development in a longitudinal population-based sample of 752 American adolescents. Personality was assessed reliably with the Junior Temperament and Character Inventory at 12, 14, and 16 years of age. The rank-order stability of Junior Temperament and Character Inventory traits from age 12 to 16 was moderate (r = .35). Hierarchical linear modeling of between-group variance due to gender and within-group variance due to age indicated that harm avoidance and persistence decreased whereas self-directedness and cooperativeness increased from age 12 to 16. Novelty seeking, reward dependence, and self-transcendence increased from age 12 to 14 and then decreased. This biphasic pattern suggests that prior to age 14 teens became more emancipated from adult authorities while identifying more with the emergent norms of their peers, and after age 14 their created identity was internalized. Girls were more self-directed and cooperative than boys and maintained this advantage from age 12 to 16. Dependability of temperament at age 16 was mainly predicted by the same traits at earlier ages. In contrast, maturity of character at age 16 was predicted by both temperament and character at earlier ages. We conclude that character develops rapidly in adolescence to self-regulate temperament in accord with personally valued goals shaped by peers.</jats:p

Topological Wilson-loop area law manifested using a superposition of loops

We introduce a new topological effect involving interference of two meson loops, manifesting a path-independent topological area dependence. The effect also draws a connection between quark confinement, Wilson-loops and topological interference effects. Although this is only a gedanken experiment in the context of particle physics, such an experiment may be realized and used as a tool to test confinement effects and phase transitions in quantum simulation of dynamic gauge theories.Comment: Superceding arXiv:1206.2021v1 [quant-ph

Preprint arXiv: 2211.00023 Submitted on 31 Oct 2022

Tensor network states, and in particular Projected Entangled Pair States(PEPS) have been a strong ansatz for the variational study of complicatedquantum many-body systems, thanks to their built-in entanglement entropy arealaw. In this work, we use a special kind of PEPS - Gauged Gaussian FermionicPEPS (GGFPEPS) to find the ground state of $2+1d$ dimensional pure$\mathbb{Z}_2$ lattice gauge theories for a wide range of coupling constants.We do so by combining PEPS methods with Monte-Carlo computations, allowing forefficient contraction of the PEPS and computation of correlation functions.Previously, such numerical computations involved the calculation of thePfaffian of a matrix scaling with the system size, forming a severe bottleneck;in this work we show how to overcome this problem. This paves the way forapplying the method we propose and benchmark here to other gauge groups, higherdimensions, and models with fermionic matter, in an efficient,sign-problem-free way

Multiresolution community detection for megascale networks by information-based replica correlations

We use a Potts model community detection algorithm to accurately and quantitatively evaluate the hierarchical or multiresolution structure of a graph. Our multiresolution algorithm calculates correlations among multiple copies ("replicas") of the same graph over a range of resolutions. Significant multiresolution structures are identified by strongly correlated replicas. The average normalized mutual information, the variation of information, and other measures in principle give a quantitative estimate of the "best" resolutions and indicate the relative strength of the structures in the graph. Because the method is based on information comparisons, it can in principle be used with any community detection model that can examine multiple resolutions. Our approach may be extended to other optimization problems. As a local measure, our Potts model avoids the "resolution limit" that affects other popular models. With this model, our community detection algorithm has an accuracy that ranks among the best of currently available methods. Using it, we can examine graphs over 40 million nodes and more than one billion edges. We further report that the multiresolution variant of our algorithm can solve systems of at least 200000 nodes and 10 million edges on a single processor with exceptionally high accuracy. For typical cases, we find a super-linear scaling, O(L^{1.3}) for community detection and O(L^{1.3} log N) for the multiresolution algorithm where L is the number of edges and N is the number of nodes in the system.Comment: 19 pages, 14 figures, published version with minor change

Investigating Dataset Distinctiveness

Just as a human might struggle to interpret another human’s handwriting, a computer vision program might fail when asked to perform one task in two different domains. To be more specific, visualize a self-driving car as a human driver who had only ever driven on clear, sunny days, during daylight hours. This driver – the self-driving car – would inevitably face a significant challenge when asked to drive when it is violently raining or foggy during the night, putting the safety of its passengers in danger. An extensive understanding of the data we use to teach computer vision models – such as those that will be driving our cars in the years to come – is absolutely necessary as these sorts of complex systems find their way into everyday human life. This study works to develop a comprehensive meaning of the style of a dataset, or the quantitative difference between cursive lettering and print lettering, with respect to the image data used in the field of computer vision. We accomplished this by asking a machine learning model to predict which commonly used dataset a particular image belongs to, based on detailed features of the images. If the model performed well when classifying an image based on which dataset it belongs to, that dataset was considered distinct. We then developed a linear relationship between this distinctiveness metric and a model’s ability to learn from one dataset and test on another, so as to have a better understanding of how a computer vision system will perform in a given context, before it is trained

Variational quantum simulation of U(1) lattice gauge theories with qudit systems

Lattice gauge theories are fundamental to various fields, including particle physics, condensed matter, and quantum information theory. Recent progress in the control of quantum systems allows for studying Abelian lattice gauge theories in table-top experiments. However, several challenges remain, such as implementing dynamical fermions in higher spatial dimensions and magnetic field terms. Here, we map D-dimensional U(1) Abelian lattice gauge theories onto qudit systems with local interactions for arbitrary D. We propose a variational quantum simulation scheme for the qudit system with a local Hamiltonian, that can be implemented on a universal qudit quantum device as the one developed in [Nat. Phys. 18, 1053-1057 (2022)]. We describe how to implement the variational imaginary-time evolution protocol for ground state preparation as well as the variational real-time evolution protocol to simulate non-equilibrium physics on universal qudit quantum computers, supplemented with numerical simulations. Our proposal can serve as a way of simulating lattice gauge theories, particularly in higher spatial dimensions, with minimal resources, regarding both system sizes and gate count