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Neutrinos are known to undergo flavor conversion among their three flavors. In the theoretical modeling of core-collapse supernova (CCSN), there has been a great deal of attention to recent discoveries of a new type of neutrino flavor conversions, namely collisional flavor instability (CFI), in which the instability is induced by the flavor-dependent decoherence due to the disparity of neutrino-matter interactions among flavors. In this paper, we study how the appearance of on-shell muons and associated neutrino-matter interactions can impact CFIs based on linear stability analysis of flavor conversions. Some striking results emerge from the present study. First, we analytically show that breaking beta- and pair equilibrium is a necessary condition to trigger CFIs. This also indicates that CFIs with on-shell muons could appear in $e \tau$ and $\mu \tau$ neutrino mixing sectors in very high-density region ($\gtrsim 10^{13} {\rm g/cm^{3}}$), exhibiting a possibility of large impacts of CFIs on CCSN. Second, resonance-like CFIs, having a much higher growth rate than normal CFIs, can be triggered by muons. The resonance point of CFIs is different between $e \tau$ and $\mu \tau$ sectors; the former (latter) occurs at $\mu_{e (\mu)} = \mu_{n} - \mu_{p}$, where $\mu_{i}$ denotes the chemical potential of $i$ constitute ($n$ and $p$ represent neutrons and protons, respectively). Our result suggests that the non-linear evolution of CFI with on-shell muons would induce flavor conversions with the complex interplay among all three different neutrino-mixing sectors.

Recently, Ainsworth et al. showed that using weight matching (WM) to minimize the $L_2$ distance in a permutation search of model parameters effectively identifies permutations that satisfy linear mode connectivity (LMC), in which the loss along a linear path between two independently trained models with different seeds remains nearly constant. This paper provides a theoretical analysis of LMC using WM, which is crucial for understanding stochastic gradient descent's effectiveness and its application in areas like model merging. We first experimentally and theoretically show that permutations found by WM do not significantly reduce the $L_2$ distance between two models and the occurrence of LMC is not merely due to distance reduction by WM in itself. We then provide theoretical insights showing that permutations can change the directions of the singular vectors, but not the singular values, of the weight matrices in each layer. This finding shows that permutations found by WM mainly align the directions of singular vectors associated with large singular values across models. This alignment brings the singular vectors with large singular values, which determine the model functionality, closer between pre-merged and post-merged models, so that the post-merged model retains functionality similar to the pre-merged models, making it easy to satisfy LMC. Finally, we analyze the difference between WM and straight-through estimator (STE), a dataset-dependent permutation search method, and show that WM outperforms STE, especially when merging three or more models., Comment: 26 pages

Neutrinos are known to undergo flavor conversion processes among the three flavors. The fast flavor conversion (FFC) has been the central piece of flavor conversions taking place in core-collapse supernovae (CCSNe) due to its shorter timescale to the completion of flavor conversion compared to other types of flavor conversion. Although the ordinary collisions between neutrinos and matter were once thought to decohere neutrinos and thus damp flavor conversions, it was recently realized that they can also induce the flavor conversion. The linear analysis showed that the so-called collisional flavor instability or CFI occurs in the absence of FFC. In this paper, we investigate if CFI takes place in of the post-bounce core of CCSNe, using the results of spherically symmetric Boltzmann simulations of CCSNe for four progenitor models with different masses. We also provide a necessary (but not sufficient) condition of matter properties for the occurrence of CFI in optically thick and semi-transparent regions; baryon mass density ($\rho$), electron fraction ($Y_e$), and the degeneracy of electron-type neutrinos ($\eta_{\nu_e}$) need to be $10^{10} {\rm g/cm^3} \lesssim \rho \lesssim 10^{12} {\rm g/cm^3}$, $Y_e\lesssim 0.4$, and $\eta_{\nu_e} \lesssim 0.5$, respectively. This condition allows us to easily locate the place of possible CFI occurence without detailed stability analyses, which is useful for analyzing CFI in CCSN models phenomenologically, Comment: accepted in PRD

This study presents a new framework for vehicle motion planning and control based on the automatic generation of model predictive controllers (MPCs) named MPC Builder. In this framework, several components necessary for MPC, such as prediction models, constraints, and cost functions, are prepared in advance. The MPC Builder then generates various MPCs online in a unified manner according to traffic situations. This scheme enabled us to represent various driving tasks with less design effort than typical switched MPC systems. The proposed framework was implemented considering the continuation/generalized minimum residual (C/GMRES) method optimization solver, which can reduce computational costs. Finally, numerical experiments on multiple driving scenarios were presented., Comment: 8 pages, 9 figures

This paper proposes Scyclone, a high-quality voice conversion (VC) technique without parallel data training. Scyclone improves speech naturalness and speaker similarity of the converted speech by introducing CycleGAN-based spectrogram conversion with a simplified WaveRNN-based vocoder. In Scyclone, a linear spectrogram is used as the conversion features instead of vocoder parameters, which avoids quality degradation due to extraction errors in fundamental frequency and voiced/unvoiced parameters. The spectrogram of source and target speakers are modeled by modified CycleGAN networks, and the waveform is reconstructed using the simplified WaveRNN with a single Gaussian probability density function. The subjective experiments with completely unpaired training data show that Scyclone is significantly better than CycleGAN-VC2, one of the existing state-of-the-art parallel-data-free VC techniques.