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To understand physical properties of the interstellar medium (ISM) on various scales, we investigate it at parsec resolution on the kiloparsec scale. Here, we report on the sub-kpc scale Gas Density Histogram (GDH) of the Milky Way. The GDH is a density probability distribution function (PDF) of the gas volume density. Using this method, we are free from an identification of individual molecular clouds and their spatial structures. We use survey data of $^{12}$CO and $^{13}$CO ($J$=1-0) emission in the Galactic plane ($l = 10^{\circ}$-$50^{\circ}$) obtained as a part of the FOREST Unbiased Galactic plane Imaging survey with the Nobeyama 45m telescope (FUGIN). We make a GDH for every channel map of $2^{\circ} \times 2^{\circ}$ area, including the blank sky component, and without setting cloud boundaries. This is a different approach from previous works for molecular clouds. The GDH fits well to a single or double log-normal distribution, which we name the low-density log-normal (L-LN) and high-density log-normal (H-LN) components, respectively. The multi-log-normal components suggest that the L-LN and H-LN components originate from two different stages of structure formation in the ISM. Moreover, we find that both the volume ratios of H-LN components to total ($f_{\mathrm{H}}$) and the width of the L-LN along the gas density axis ($\sigma_{\rm{L}}$) show coherent structure in the Galactic-plane longitude-velocity diagram. It is possible that these GDH parameters are related to strong galactic shocks and other weak shocks in the Milky Way.

A major obstacle to understanding high-Tc cuprates is that superconductivity precludes observing normal-state properties at low temperatures. One prime example is the normal-state spin susceptibility: although its decrease upon cooling far above Tc typifies pseudogap behavior, its behavior at low temperatures is generally unknown. Here, our measurements in high magnetic fields expose the spin susceptibility of YBa2Cu3Oy down to low temperatures. Even though superconductivity is suppressed by the field, we uncover two thermally-activated contributions alongside a residual susceptibility at T=0 due to gapless excitations. We relate these two distinct gaps to short-range charge-density waves and to the formation of spin singlets similar to those found in certain quantum spin systems. These phenomena thus collectively contribute to the pseudogap in the spin susceptibility at low temperature, supplementing short-lived antiferromagnetism known to initiate pseudogap behavior at high temperatures. We therefore propose that the pseudogap should be regarded as a composite property.

The global drive towards carbon neutrality has led to a significant increase in the number of power plants based on renewable energy sources (RES). Concurrently, numerous households are adopting RES to generate their own energy, aiming to decrease both electricity costs and carbon footprints. To support these users, many papers have been devoted to developing optimal investment strategies for residential energy systems. However, there is still a significant gap as these studies often neglect important aspects like carbon neutrality. For this reason, in this paper, we explore the concept of net-zero energy houses (ZEHs) -- houses designed to have an annual net energy consumption around zero -- by presenting a constrained optimization problem to find the optimal number of photovoltaic panels and the optimal size of the battery system for home integration. Solving this constrained optimization problem is difficult due to its nonconvex constraints. Nevertheless, by applying a series of transformations, we reveal that it is possible to find an equivalent linear programming (LP) problem which is computationally tractable. The attainment of ZEH can be tackled by introducing a single constraint in the optimization problem. Additionally, we propose a sharing economy approach to the investment problem, offering a strategy that could potentially reduce investment costs and facilitate the attainment of ZEH more efficiently. Finally, we apply the proposed frameworks to a neighborhood in Japan as a case study, demonstrating the potential for long-term ZEH attainment. The results show that, under the right incentive, users can achieve ZEH, reduce their electricity costs and have a minimal impact on the main grid., Comment: 13 pages, 13 figures, 5 tables

Deep reinforcement learning (RL) algorithms enable the development of fully autonomous agents that can interact with the environment. Brain-computer interface (BCI) systems decipher human implicit brain signals regardless of the explicit environment. In this study, we integrated deep RL and BCI to improve beneficial human interventions in autonomous systems and the performance in decoding brain activities by considering environmental factors. Shared autonomy was allowed between the action command decoded from the electroencephalography (EEG) of the human agent and the action generated from the twin delayed DDPG (TD3) agent for a given environment. Our proposed copilot control scheme with a full blocker (Co-FB) significantly outperformed the individual EEG (EEG-NB) or TD3 control. The Co-FB model achieved a higher target approaching score, lower failure rate, and lower human workload than the EEG-NB model. The Co-FB control scheme had a higher invisible target score and level of allowed human intervention than the TD3 model. We also proposed a disparity d-index to evaluate the effect of contradicting agent decisions on the control accuracy and authority of the copilot model. We found a significant correlation between the control authority of the TD3 agent and the performance improvement of human EEG classification with respect to the d-index. We also observed that shifting control authority to the TD3 agent improved performance when BCI decoding was not optimal. These findings indicate that the copilot system can effectively handle complex environments and that BCI performance can be improved by considering environmental factors. Future work should employ continuous action space and different multi-agent approaches to evaluate copilot performance., Comment: 14 pages, 6 figures

Using a general-order ab initio many-body Green's function method, we numerically illustrate several pathological behaviors of the Feynman-Dyson diagrammatic perturbation expansion of one-particle many-body Green's functions as electron Feynman propagators. (i) The perturbation expansion of the frequency-dependent self-energy is not convergent at the exact self-energy in many frequency domains. (ii) An odd-perturbation-order self-energy has a qualitatively wrong shape and, as a result, many roots of the corresponding Dyson equation are nonphysical in that the poles may be complex or residues can exceed unity or be negative. (iii) A higher even-order self-energy consists of vertical lines at many frequencies, predicting numerous phantom poles with zero residues. (iv) Infinite partial resummations of diagrams by vertex or edge renormalization tend to exacerbate these pathologies. (v) The nonconvergence is caused by the nonanalyticity of the rational-function form of the exact Green's function at many frequencies, where the radius of convergence of its Taylor expansion is zero. This is consistent with the fact that (vi) Pad\'{e} approximants (power-series expansions of a rational function) can largely restore the correct shape and poles of the Green's function. Nevertheless, not only does the nonconvergence render higher-order Feynman-Dyson diagrammatic perturbation theory useless for many lower-lying ionization or higher-lying electron-attachment states, but it also calls into question the validity of its combined use with the ans\"{a}tze requiring the knowledge of all poles and residues. Such ans\"{a}tze include the Galitskii-Migdal identity, the self-consistent Green's function methods, and some models of the algebraic diagrammatic construction.