Thermodynamics of noncommutative quantum Kerr black holes

Thermodynamic formalism for rotating black holes, characterized by noncommutative and quantum corrections, is constructed. From a fundamental thermodynamic relation, equations of state and thermodynamic response functions are explicitly given and the effect of noncommutativity and quantum correction is discussed. It is shown that the well known divergence exhibited in specific heat is not removed by any of these corrections. However, regions of thermodynamic stability are affected by noncommutativity, increasing the available states for which some thermodynamic stability conditions are satisfied.Comment: 16 pages, 9 figure

Dependence of the drag over super hydrophobic and liquid infused surfaces on the textured surface and Weber number

Direct Numerical Simulations of a turbulent channel flow have been performed. The lower wall of the channel is made of staggered cubes with a second fluid locked in the cavities. Two viscosity ratios have been considered, m=μ1/μ2=0.02 and 0.4 (the subscript 1 indicates the fluid in the cavities and 2 the overlying fluid) mimicking the viscosity ratio in super–hydrophobic surfaces (SHS) and liquid infused surfaces (LIS) respectively. A first set of simulations with a slippery interface has been performed and results agree well with those in literature for perfect slip conditions and Stokes approximations. To assess how the dynamics of the interface affects the drag, a second set of DNS has been carried out at We=40 and 400 corresponding to We+≃10−3 and 10−2. The deformation of the interface is fully coupled to the Navier-Stokes equation and tracked in time using a Level Set Method. Two gas fractions, GF=0.5 and 0.875, have been considered to assess how the spacing between the cubes affects the deformation of the interface and therefore the drag. For the dimensions of the substrate here considered, under the ideal assumption of flat interface, staggered cubes with GF=0.875 provide about 20% drag reduction for We=0. However, a rapid degradation of the performances is observed when the dynamics of the interface is considered, and the same geometry increases the drag of about 40% with respect to a smooth wall. On the other hand, the detrimental effect of the dynamics of the interface is much weaker for GF=0.5 because of the reduced pitch between the cubes

On the universality of the scaling of fluctuations in traffic on complex networks

We study the scaling of fluctuations with the mean of traffic in complex networks using a model where the arrival and departure of "packets" follow exponential distributions, and the processing capability of nodes is either unlimited or finite. The model presents a wide variety of exponents between 1/2 and 1 for this scaling, revealing their dependence on the few parameters considered, and questioning the existence of universality classes. We also report the experimental scaling of the fluctuations in the Internet for the Abilene backbone network. We found scaling exponents between 0.71 and 0.86 that do not fit with the exponent 1/2 reported in the literature.Comment: 4 pages, 4 figure

Beyond mean-field bistability in driven-dissipative lattices: bunching-antibunching transition and quantum simulation

In the present work we investigate the existence of multiple nonequilibrium steady states in a coherently driven XY lattice of dissipative two-level systems. A commonly used mean-field ansatz, in which spatial correlations are neglected, predicts a bistable behavior with a sharp shift between low- and high-density states. In contrast one-dimensional matrix product methods reveal these effects to be artifacts of the mean-field approach, with both disappearing once correlations are taken fully into account. Instead, a bunching-antibunching transition emerges. This indicates that alternative approaches should be considered for higher spatial dimensions, where classical simulations are currently infeasible. Thus we propose a circuit QED quantum simulator implementable with current technology to enable an experimental investigation of the model considered

Central limit theorem for crossings in randomly embedded graphs

We consider the number of crossings in a random embedding of a graph, $G$, with vertices in convex position. We give explicit formulas for the mean and variance of the number of crossings as a function of various subgraph counts of $G$. Using Stein's method and size-bias coupling, we prove an upper bound on the Kolmogorov distance between the distribution of the number of crossings and a standard normal random variable. As an application, we establish central limit theorems, along with convergence rates, for the number of crossings in random matchings, path graphs, cycle graphs, and the disjoint union of triangles.Comment: 18 pages, 5 figures. This is a merger of arXiv:2104.01134 and arXiv:2205.0399

Magnetodielectric coupling of infrared phonons in single crystal Cu2_{2}OSeO3_{3}

Reflection and transmission as a function of temperature have been measured on a single crystal of the magnetoelectric ferrimagnetic compound Cu$_{2}$OSeO$_{3}$ utilizing light spanning the far infrared to the visible portions of the electromagnetic spectrum. The complex dielectric function and optical properties were obtained via Kramers-Kronig analysis and by fits to a Drude-Lortentz model. The fits of the infrared phonons show a magnetodielectric effect near the transition temperature ($T_{c}\sim 60$~K). Assignments to strong far infrared phonon modes have been made, especially those exhibiting anomalous behavior around the transition temperature

Functional Optimization in Complex Excitable Networks

We study the effect of varying wiring in excitable random networks in which connection weights change with activity to mold local resistance or facilitation due to fatigue. Dynamic attractors, corresponding to patterns of activity, are then easily destabilized according to three main modes, including one in which the activity shows chaotic hopping among the patterns. We describe phase transitions to this regime, and show a monotonous dependence of critical parameters on the heterogeneity of the wiring distribution. Such correlation between topology and functionality implies, in particular, that tasks which require unstable behavior --such as pattern recognition, family discrimination and categorization-- can be most efficiently performed on highly heterogeneous networks. It also follows a possible explanation for the abundance in nature of scale--free network topologies.Comment: 7 pages, 3 figure

How to suppress undesired synchronization

It is delightful to observe the emergence of synchronization in the blinking of fireflies to attract partners and preys. Other charming examples of synchronization can also be found in a wide range of phenomena such as, e.g., neurons firing, lasers cascades, chemical reactions, and opinion formation. However, in many situations the formation of a coherent state is not pleasant and should be mitigated. For example, the onset of synchronization can be the root of epileptic seizures, traffic congestion in communication networks, and the collapse of constructions. Here we propose the use of contrarians to suppress undesired synchronization. We perform a comparative study of different strategies, either requiring local or total knowledge of the system, and show that the most efficient one solely requires local information. Our results also reveal that, even when the distribution of neighboring interactions is narrow, significant improvement in mitigation is observed when contrarians sit at the highly connected elements. The same qualitative results are obtained for artificially generated networks as well as two real ones, namely, the Routers of the Internet and a neuronal network

Functional strengthening through synaptic scaling upon connectivity disruption in neuronal cultures

An elusive phenomenon in network neuroscience is the extent of neuronal activity remodeling upon damage. Here, we investigate the action of gradual synaptic blockade on the effective connectivity in cortical networks in vitro. We use two neuronal cultures configurations—one formed by about 130 neuronal aggregates and another one formed by about 600 individual neurons—and monitor their spontaneous activity upon progressive weakening of excitatory connectivity. We report that the effective connectivity in all cultures exhibits a first phase of transient strengthening followed by a second phase of steady deterioration. We quantify these phases by measuring GEFF, the global efficiency in processing network information. We term hyperefficiency the sudden strengthening of GEFF upon network deterioration, which increases by 20–50% depending on culture type. Relying on numerical simulations we reveal the role of synaptic scaling, an activity–dependent mechanism for synaptic plasticity, in counteracting the perturbative action, neatly reproducing the observed hyperefficiency. Our results demonstrate the importance of synaptic scaling as resilience mechanism. Author Summary Neuronal circuits exhibit homeostatic plasticity mechanisms to cope with perturbations or damage. A central mechanism is ‘synaptic scaling,’ a self-organized response in which the strength of neurons’ excitatory synapses is adjusted to compensate for activity variations. Here we present experiments in which the excitatory connectivity of in vitro cortical networks is progressively weakened through chemical action. The spontaneous activity and effective connectivity of the whole network is monitored as degradation progresses, and the capacity of the network for broad information communication is quantified through the global efficiency. We observed that the network responded to the perturbation by strengthening the effective connectivity, reaching a hyperefficient state for moderate perturbations. The study proves the importance of ‘synaptic scaling’ as a driver for functional reorganization and network-wide resilience