Patterns and driving forces of dimensionality-dependent charge density waves in 2H-type transition metal dichalcogenides

Two-dimensional (2D) materials have become a fertile playground for the exploration and manipulation of novel collective electronic states. Recent experiments have unveiled a variety of robust 2D orders in highly-crystalline materials ranging from magnetism to ferroelectricity and from superconductivity to charge density wave (CDW) instability. The latter, in particular, appears in diverse patterns even within the same family of materials with isoelectronic species. Furthermore, how they evolve with dimensionality has so far remained elusive. Here we propose a general framework that provides a unfied picture of CDW ordering in the 2H polytype of four isoelectronic transition metal dichalcogenides 2H-MX$_2$ (M=Nb, Ta and X=S, Se). We first show experimentally that whilst NbSe$_2$ exhibits a strongly enhanced CDW order in the 2D limit, the opposite trend exists for TaSe$_2$ and TaS$_2$, with CDW being entirely absent in NbS$_2$ from its bulk to the monolayer. Such distinct behaviours are then demonstrated to be the result of a subtle, yet profound, competition between three factors: ionic charge transfer, electron-phonon coupling, and the spreading extension of the electronic wave functions. Despite its simplicity, our approach can, in essence, be applied to other quasi-2D materials to account for their CDW response at different thicknesses, thereby shedding new light on this intriguing quantum phenomenon and its underlying mechanisms

Dissipative quantum dynamics, phase transitions and non-Hermitian random matrices

We explore the connections between dissipative quantum phase transitions and non-Hermitian random matrix theory. For this, we work in the framework of the dissipative Dicke model which is archetypal of symmetry-breaking phase transitions in open quantum systems. We establish that the Liouvillian describing the quantum dynamics exhibits distinct spectral features of integrable and chaotic character on the two sides of the critical point. We follow the distribution of the spacings of the complex Liouvillian eigenvalues across the critical point. In the normal and superradiant phases, the distributions are $2D$ Poisson and that of the Ginibre Unitary random matrix ensemble, respectively. Our results are corroborated by computing a recently introduced complex-plane generalization of the consecutive level-spacing ratio distribution. Our approach can be readily adapted for classifying the nature of quantum dynamics across dissipative critical points in other open quantum systems.Comment: 5 pages, 4 figures, and supplementary materia

Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience

This essay is presented with two principal objectives in mind: first, to document the prevalence of fractals at all levels of the nervous system, giving credence to the notion of their functional relevance; and second, to draw attention to the as yet still unresolved issues of the detailed relationships among power law scaling, self-similarity, and self-organized criticality. As regards criticality, I will document that it has become a pivotal reference point in Neurodynamics. Furthermore, I will emphasize the not yet fully appreciated significance of allometric control processes. For dynamic fractals, I will assemble reasons for attributing to them the capacity to adapt task execution to contextual changes across a range of scales. The final Section consists of general reflections on the implications of the reviewed data, and identifies what appear to be issues of fundamental importance for future research in the rapidly evolving topic of this review

Densification and Structural Transitions in Networks that Grow by Node Copying

We introduce a growing network model---the copying model---in which a new node attaches to a randomly selected target node and, in addition, independently to each of the neighbors of the target with copying probability $p$. When $p<\frac{1}{2}$, this algorithm generates sparse networks, in which the average node degree is finite. A power-law degree distribution also arises, with a non-universal exponent whose value is determined by a transcendental equation in $p$. In the sparse regime, the network is "normal", e.g., the relative fluctuations in the number of links are asymptotically negligible. For $p\geq \frac{1}{2}$, the emergent networks are dense (the average degree increases with the number of nodes $N$) and they exhibit intriguing structural behaviors. In particular, the $N$-dependence of the number of $m$-cliques (complete subgraphs of $m$ nodes) undergoes $m-1$ transitions from normal to progressively more anomalous behavior at a $m$-dependent critical values of $p$. Different realizations of the network, which start from the same initial state, exhibit macroscopic fluctuations in the thermodynamic limit---absence of self averaging. When linking to second neighbors of the target node can occur, the number of links asymptotically grows as $N^2$ as $N\to\infty$, so that the network is effectively complete as $N\to \infty$.Comment: 15 pages, 12 figure

Introduction to Complex Systems, Sustainability and Innovation

The technological innovations have always proved the impossible possible. Humans have all the time obliterated barriers and set records with astounding regularity. However, there are issues springing up in terms of complexity and sustainability in this context, which we were ignoring for long. Today, in every walk of life, we encounter complex systems, whether it is the Internet, communication systems, electrical power grids, or the financial markets. Due to its unpredictable behavior, any creative change in a complex system poses a threat of systemic risks. This is because an innovation is always introducing something new, introducing a change, possibly to solve an existing problem, the effect of which is nonlinear. Failure to predict the future states of the system due to the nonlinear nature makes any system unsustainable. This necessitates the need for any development to be sustainable by meeting the needs of people today without destroying the potential of future generations to meet their needs. This chapter, which studies systems that are complex due to intricateness in their connectivity, gives insights into their ways of emergence and the nonlinear cause and effects pattern the complex systems use to follow, effectively paving way for sustainable innovation