2,219 research outputs found
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, ,
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
. 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 CuOSeO
Reflection and transmission as a function of temperature have been measured
on a single crystal of the magnetoelectric ferrimagnetic compound
CuOSeO 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 (~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
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