The electric dipole moment in a model for neutrino mass, dark matter and baryon asymmetry of the Universe

The electric dipole moment is examined in a three-loop neutrino mass model with dark matter originally proposed in Aoki et al. [Phys. Rev. Lett.102 (2009) 051805]. The model contains a CP-violating phase in the Higgs potential which plays an important role in electroweak baryogenesis and is thus expected to explain the baryon asymmetry of the Universe simultaneously. However, such a CP-violating phase is severely constrained by the measurements of the electron electric dipole moment (eEDM), and a suppression mechanism for the eEDM is necessary to explain the observed baryon asymmetry while avoiding the constraint. In this paper, we examine neutrino mass, lepton-flavor-violating processes, dark matter, and the eEDM in the model. We show that the eEDM can be suppressed by destructive interference between the CP-violating phases in the Higgs sector and the dark sector with large CP-violating phases. We propose some benchmark scenarios including O(1) CP-violating phases where tiny neutrino mass and dark matter can be explained while avoiding all current experimental and theoretical constraints. These CP-violating phases are expected to be large enough to generate the observed baryon asymmetry in the electroweak baryogenesis scenario.

Improved Flat Frequency Response of Conical Shellular Metamaterial-Enabled Flat Panel Loudspeaker

A piezoelectric flat panel loudspeaker operates by utilizing a piezoelectric film actuator to vibrate a diaphragm, offering advantages in miniaturization, embedding, and spatial efficiency. However, achieving high sound pressure levels (SPL) at low frequencies and maintaining a flat frequency response remains challenging. This study presents a Conical Shellular Sandwich Diaphragm (CSSD), derived from conical shellular metamaterials, which combines lightweight properties with high stiffness. The CSSD enhances low-frequency SPL and improves frequency response flatness through structural optimization, eliminating the need for complex systems. Finite element analysis identifies optimal geometric parameters for the CSSD unit cell, resulting in an 11.5 dB increase in low-frequency SPL and a 53% reduction in peak-dip deviation across the 200 Hz-20 kHz range, compared to a conventional Flat Panel Diaphragm (FPD). These enhancements stem from a reduction in the CSSD's effective mass (meff) to one-hundredth of an equivalent-volume FPD and an increase in effective bulk modulus (keff) relative to structures with the same mass. Experimental tests of 3D-printed CSSD and FPD prototypes integrated with piezoelectric actuators match the simulation results. This study demonstrates the potential of mechanical metamaterials to address design limitations in flat panel loudspeakers, enabling improved sound quality and simpler configurations for commercial use.

A comparative study of ANN-based forward dynamics and inverse dynamics in human gait analysis

This study investigates the similarities and differences in the analysis of human walking motion between the traditional inverse dynamics method and the forward dynamics method that employs an Artificial Neural Network (ANN)-based controller. Nine healthy male subjects walked at their preferred speeds while motion capture and ground reaction force data were collected. Inverse kinematics and dynamics analyses were conducted using OpenSim. The ANN-based gait controller was trained via deep reinforcement learning using a two-stage curriculum in forward dynamics simulations. It was first trained for kinematic tracking and then further optimized to minimize torque, power, torque difference, and ground reaction force fluctuations. The ANN-based controller reproduced joint kinematics with a root-mean-square (RMS) difference of less than 2.7 degrees compared to inverse kinematics in OpenSim. The controller preserved accurate gait kinematics despite reducing joint torques and power. Joint torque profiles showed RMS differences of 0.20-0.23 Nm/kg, comparable to results obtained through optimization-based residual force minimization. Joint power analysis revealed that inverse dynamics in OpenSim underestimated total energy consumption by 0.74 W/kg compared to forward dynamics. This discrepancy was primarily due to residual forces and torques, which accounted for 19.9% of total mechanical power. When residuals were included, the difference in total power between the two methods was reduced to 4.1%. These findings indicate that ANN-based forward dynamics modeling can accurately reproduce human gait while allowing mechanical energy estimation without residual forces. The controller's adaptability allows for analyzing gait variations under different conditions, with potential applications in rehabilitation and assistive robotics.

Looking but Not Focusing: Defining Gaze-Based Indices of Attention Lapses and Classifying Attentional States

Identifying objective markers of attentional states is critical, particularly in real-world scenarios where attentional lapses have serious consequences. In this study, we identified gaze-based indices of attentional lapses and validated them by examining their impact on the performance of classification models. We designed a virtual reality visual search task that encouraged active eye movements to define dynamic gaze-based metrics of different attentional states (zone in/out). The results revealed significant differences in both reactive ocular features, such as first fixation and saccade onset latency, and global ocular features, such as saccade amplitude, depending on the attentional state. Moreover, the performance of the classification models improved significantly when trained only on the proven gaze-based and behavioral indices rather than all available features, with the highest prediction accuracy of 79.3%. We highlight the importance of the preliminary studies before model training and provide generalizable gaze-based indices of attentional states for practical applications

Evolution of nano-sized precipitates in δ-ferrite of high-Cu CF8M with thermal aging and their stability against reversion heat treatment

The aim of this study was to investigate the evolution of thermal aging-induced nano-sized precipitates in delta-ferrite of high-Cu CF8M cast austenitic stainless steel (CASS) and the stability of these precipitates against reversion heat treatment (R-HT). The CF8M was subjected to thermal aging at 400 degrees C for up to 20,000 h. Initially, thermal aging stimulated spinodal decomposition of delta-ferrite accompanied by the formation of Cu-rich clusters, G-phase and omega (omega) phase precipitates. The spinodal decomposition and secondary phase precipitates further evolved with the increase in thermal aging duration to 20,000 h. The Cu-rich clusters remained either as coherent BCC Cu precipitate adjacent to the G-phase or as part of G-phase and did not transformed to other structures, even after long-term thermal aging as well as after R-HT at 550 degrees C for 1 h. The R-HT of aged CF8M lead to the dissolution of spinodal decomposition and desegregation of Ni in delta-ferrite. However, despite the desegregation of Ni, G-phase and omega-phase precipitates exhibited minimal dissolution after R-HT demonstrating greater thermal stability. The size, number density and volume fraction of G-phase and omega-phase precipitates in delta-ferrite were determined for both aged and R-HTed conditions. These microstructural changes in delta-ferrite of CF8M could be correlated with the hardening of delta-ferrite after thermal aging and the subsequent reduction in hardness after R-HT.

Does SGD really happen in tiny subspaces?

Understanding the training dynamics of deep neural networks is challenging due to their high-dimensional nature and intricate loss landscapes. Recent studies have revealed that, along the training trajectory, the gradient approximately aligns with a low-rank top eigenspace of the training loss Hessian, referred to as the dominant subspace. Given this alignment, this paper explores whether neural networks can be trained within the dominant subspace, which, if feasible, could lead to more efficient training methods. Our primary observation is that when the SGD update is projected onto the dominant subspace, the training loss does not decrease further. This suggests that the observed alignment between the gradient and the dominant subspace is spurious. Surprisingly, projecting out the dominant subspace proves to be just as effective as the original update, despite removing the majority of the original update component. We observe similar behavior across practical setups, including the large learning rate regime (also known as Edge of Stability), Sharpness-Aware Minimization, momentum, and adaptive optimizers. We discuss the main causes and implications of this spurious alignment, shedding light on the dynamics of neural network training

Simulating quantum light in lossy microring resonators driven by strong pulses

In this work, we present a quantum theory for pulsed photon pair generation in a single ring resonator. Our approach combines the Heisenberg picture input-output formalism with the Ikeda mapping from classical nonlinear optics. In doing so, we address the high-gain regime by incorporating nonperturbative effects, including self-phase modulation, cross-phase modulation, and time-ordering, which are roots for significantly different behaviors in the low-gain regime. We also account for optical losses by introducing an auxiliary waveguide, allowing for a more accurate representation of experimentally viable scenarios. Numerical simulations reveal that nonperturbative effects significantly distort transfer functions, making desirable operations challenging without careful optimization. We show that appropriate detuning of the pump frequency can mitigate these issues, leading to enhanced brightness and higher spectral purity in the high-gain regime. We further investigate the performance of a single ring resonator as a two-mode squeezer by analyzing various performance metrics under experimentally relevant optical loss conditions.