Massless and massive graviton spectra in anisotropic dilatonic braneworld cosmologies

We consider a braneworld model in which an anisotropic brane is embedded in a dilatonic background. We solve the background solutions and study the behavior of the perturbations when the universe evolves from an inflationary Kasner phase to a Minkowski phase. We calculate the massless mode spectrum, and find that it does not differ from what expected in standard four-dimensional cosmological models. We then evaluate the spectrum of both light (ultrarelativistic) and heavy (nonrelativistic) massive modes, and find that, at high energies, there can be a strong enhancement of the Kaluza-Klein spectral amplitude, which can become dominant in the total spectrum. The presence of the dilaton, on the contrary, decrease the relative importance of the massive modes.Comment: 18 pages, 4 figures, Typos correction

Mpemba effect and phase transitions in the adiabatic cooling of water before freezing

An accurate experimental investigation on the Mpemba effect (that is, the freezing of initially hot water before cold one) is carried out, showing that in the adiabatic cooling of water a relevant role is played by supercooling as well as by phase transitions taking place at 6 +/- 1 oC, 3.5 +/- 0.5 oC and 1.3 +/- 0.6 oC, respectively. The last transition, occurring with a non negligible probability of 0.21, has not been detected earlier. Supported by the experimental results achieved, a thorough theoretical analysis of supercooling and such phase transitions, which are interpreted in terms of different ordering of clusters of molecules in water, is given.Comment: revtex, 4 pages, 2 figure

Domain wall dynamics in stepped magnetic nanowire with perpendicular magnetic anisotropy

Micromagnetic simulation is carried out to investigate the current-driven domain wall (DW) in a nanowire with perpendicular magnetic anisotropy (PMA). A stepped nanowire is proposed to pin DW and achieve high information storage capacity based on multi-bit per cell scheme. The DW speed is found to increase for thicker and narrower nanowires. For depinning DW from the stepped region, the current density Jdep is investigated with emphasis on device geometry and materials intrinsic properties. The Jdep could be analytically determined as a function of the nanocontriction dimension and the thickness of the nanowire. Furthermore, Jdep is found to exponential dependent on the anisotropy energy and saturation magnetization, offering thus more flexibility in adjusting the writing current for memory applications

Deep interactive evolution

This paper describes an approach that combines generative adversarial networks (GANs) with interactive evolutionary computation (IEC). While GANs can be trained to produce lifelike images, they are normally sampled randomly from the learned distribution, providing limited control over the resulting output. On the other hand, interactive evolution has shown promise in creating various artifacts such as images, music and 3D objects, but traditionally relies on a hand-designed evolvable representation of the target domain. The main insight in this paper is that a GAN trained on a specific target domain can act as a compact and robust genotype-to-phenotype mapping (i.e. most produced phenotypes do resemble valid domain artifacts). Once such a GAN is trained, the latent vector given as input to the GAN's generator network can be put under evolutionary control, allowing controllable and high-quality image generation. In this paper, we demonstrate the advantage of this novel approach through a user study in which participants were able to evolve images that strongly resemble specific target images.Comment: 16 pages, 5 figures, Published at EvoMUSART EvoStar 201

Small Sample Issues for Microarray-Based Classification

In order to study the molecular biological differences between normal and diseased tissues, it is desirable to perform classification among diseases and stages of disease using microarray-based gene-expression values. Owing to the limited number of microarrays typically used in these studies, serious issues arise with respect to the design, performance and analysis of classifiers based on microarray data. This paper reviews some fundamental issues facing small-sample classification: classification rules, constrained classifiers, error estimation and feature selection. It discusses both unconstrained and constrained classifier design from sample data, and the contributions to classifier error from constrained optimization and lack of optimality owing to design from sample data. The difficulty with estimating classifier error when confined to small samples is addressed, particularly estimating the error from training data. The impact of small samples on the ability to include more than a few variables as classifier features is explained

On the seismic analysis and design of offshore wind turbines

Offshore wind farms are a collection of offshore wind turbines (OWTs) and are currently being installed in seismically active regions. An OWT consists of a long slender tower with a top-heavy fixed mass (Nacelle) together with a heavy rotating mass (Hub and blades) and is always exposed to variable environmental wind and wave loads. For dynamic analysis, an OWT can also be seen as an inverted pendulum (with over 25%–50% of the total mass concentrated in the upper 3rd of the tower), yet it is not granted that their seismic response is dominated by the first mode. Guidelines for the design of such special structures are not explicitly mentioned in current codes of practice. The aim of this technical note is to identify the design issues and provide a rational background for the seismic analysis. Where feasible, further research work that is needed is also identified and discussed.•Considerations for seismic design.•Design return period.•Types of seismic analysis.•Selection of input motion

Is growth hormone insufficiency the missing link between obesity, male gender, age, and COVID-19 severity?

Evidence has emerged regarding an increased risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with worse prognosis in elderly male patients with obesity, and blunted growth hormone (GH) secretion represents a feature of this population subgroup. Here, a comprehensive review of the possible links between GH–insulinlike growth factor 1 axis impairment and coronavirus disease 2019 (COVID-19) severity is offered. First, unequivocal evidence suggests that immune system dysregulation represents a key element in determining SARS-CoV-2 severity, as well as the association with adult-onset GH deficiency (GHD); notably, if GH is physiologically involved in the development and maintenance of the immune system, its pharmacological replacement in GHD patients seems to positively influence their inflammatory status. In addition, the impaired fibrinolysis associated with GHD may represent a further link between GH–insulin-like growth factor 1 axis impairment and COVID-19 severity, as it has been associated with both conditions. In conclusion, several sources of evidence have supported a relationship between GHD and COVID-19, and they also shed light upon potential beneficial effects of recombinant GH treatment on COVID-19 patients

Bootstrapping Conditional GANs for Video Game Level Generation

Generative Adversarial Networks (GANs) have shown im-pressive results for image generation. However, GANs facechallenges in generating contents with certain types of con-straints, such as game levels. Specifically, it is difficult togenerate levels that have aesthetic appeal and are playable atthe same time. Additionally, because training data usually islimited, it is challenging to generate unique levels with cur-rent GANs. In this paper, we propose a new GAN architec-ture namedConditional Embedding Self-Attention Genera-tive Adversarial Network(CESAGAN) and a new bootstrap-ping training procedure. The CESAGAN is a modification ofthe self-attention GAN that incorporates an embedding fea-ture vector input to condition the training of the discriminatorand generator. This allows the network to model non-localdependency between game objects, and to count objects. Ad-ditionally, to reduce the number of levels necessary to trainthe GAN, we propose a bootstrapping mechanism in whichplayable generated levels are added to the training set. Theresults demonstrate that the new approach does not only gen-erate a larger number of levels that are playable but also gen-erates fewer duplicate levels compared to a standard GAN