Scalegenesis and fermionic dark matters in the flatland scenario

We propose an extension of the standard model with Majorana-type fermionic dark matters based on the flatland scenario where all scalar coupling constants, including scalar mass terms, vanish at the Planck scale, i.e. the scalar potential is flat above the Planck scale. This scenario could be compatible with the asymptotic safety paradigm for quantum gravity. We search the parameter space so that the model reproduces the observed values such as the Higgs mass, the electroweak vacuum and the relic abundance of dark matter. We also investigate the spin-independent elastic cross section for the Majorana fermions and a nucleon. It is shown that the Majorana fermions as dark matter candidates could be tested by dark matter direct detection experiments such as XENON, LUX and PandaX-II. We demonstrate that within the minimal setup compatible with the flatland scenario at the Planck scale or asymptotically safe quantum gravity, the extended model could have a strong predictability.Comment: 23 pages, 9 figures, Version published in EPJ

Evolutionary Distributed Control of a Biologically Inspired Modular Robot

fals

Neutrino emissions in all flavors up to the pre-bounce of massive stars and the possibility of their detections

This paper is a sequel to our previous one (Kato et al.2015), which calculated the luminosities and spectra of electron-type anti-neutrinos ($\bar{\nu}_e$'s) from the progenitors of core-collapse supernovae. Expecting that a capability to detect electron-type neutrinos ($\nu_e$'s) will increase dramatically with the emergence of liquid-argon detectors such as DUNE, we broaden the scope in this study to include all-flavors of neutrinos emitted from the pre-bounce phase. We pick up three progenitor models of an electron capture supernova (ECSN) and iron-core collapse supernovae (FeCCSNe). We find that the number luminosities reach $\sim10^{57} \mathrm{s^{-1}}$ and $\sim10^{53} \mathrm{s^{-1}}$ at maximum for $\nu_e$ and $\bar{\nu}_e$, respectively. We also estimate the numbers of detection events at terrestrial neutrino detectors including DUNE, taking flavor oscillations into account and assuming the distance to the progenitors to be 200 pc. It is demonstrated that $\bar{\nu}_e$'s from the ECSN-progenitor will be undetected at almost all detectors, whereas we will be able to observe $\gtrsim$15900 $\nu_e$'s at DUNE for the inverted mass hierarchy. From the FeCCSN-progenitors, the number of $\bar{\nu}_e$ events will be largest for JUNO, 200-900 $\bar{\nu}_e$'s, depending on the mass hierarchy whereas the number of $\nu_e$ events at DUNE is $\gtrsim$2100 for the inverted mass hierarchy. These results imply that the detection of $\bar{\nu}_e$'s is useful to distinguish FeCCSN- from ECSN-progenitors, while $\nu_e$'s will provide us with detailed information on the collapse phase regardless of the type and mass of progenitor.Comment: 22 pages, 14 figures, 4 tables, accepted to Ap

Pupillary dynamics of mice performing a Pavlovian delay conditioning task reflect reward-predictive signals

Pupils can signify various internal processes and states, such as attention, arousal, and working memory. Changes in pupil size have been associated with learning speed, prediction of future events, and deviations from the prediction in human studies. However, the detailed relationships between pupil size changes and prediction are unclear. We explored pupil size dynamics in mice performing a Pavlovian delay conditioning task. A head-fixed experimental setup combined with deep-learning-based image analysis enabled us to reduce spontaneous locomotor activity and to track the precise dynamics of pupil size of behaving mice. By setting up two experimental groups, one for which mice were able to predict reward in the Pavlovian delay conditioning task and the other for which mice were not, we demonstrated that the pupil size of mice is modulated by reward prediction and consumption, as well as body movements, but not by unpredicted reward delivery. Furthermore, we clarified that pupil size is still modulated by reward prediction even after the disruption of body movements by intraperitoneal injection of haloperidol, a dopamine D2 receptor antagonist. These results suggest that changes in pupil size reflect reward prediction signals. Thus, we provide important evidence to reconsider the neuronal circuit involved in computing reward prediction error. This integrative approach of behavioral analysis, image analysis, pupillometry, and pharmacological manipulation will pave the way for understanding the psychological and neurobiological mechanisms of reward prediction and the prediction errors essential to learning and behavior

Thin film write head field analysis using a benchmark problem

A benchmark problem has been proposed by the Storage Research Consortium (SRC) in Japan, for evaluating the applicability of computer codes to 3-D nonlinear eddy current analysis of thin film magnetic recording write head. Various codes using the finite element method are compared in terms of the write head field and the computational efficiency. The difficulty in 3-D mesh generation of thin film head is also discussed. The write head fields calculated by various codes using different meshes show fairly good agreement. The calculated write head fields are verified by measurement using a stroboscopic electron beam tomography. It is found that the calculation time strongly depends on unknown variables </p