Doppler effect in TianQin time-delay interferometry

The current design of space-based gravitational wave detectors utilizes heterodyne laser interferometry in inter-satellite science measurements. Frequency variations of the heterodyne beatnotes are predominantly caused by the Doppler effect from relative satellite motion along lines of sight. Generally considered to be outside the measurement band, this Doppler frequency shift appears to have been overlooked in numerical simulations of time-delay interferometry (TDI). However, the potential impact on the implementation of TDI should be assessed. The issue is particularly relevant to TianQin that features geocentric orbits, because of strong gravity disturbances from the Earth-Moon system at frequencies $<1\times 10^{-4}$ Hz. In this proof-of-principle study, based on high-precision orbital data obtained from detailed gravity field modeling, we incorporate the Doppler shift in the generation of TianQin's beatnote phase signals. To remove the large-scale Doppler phase drift at frequencies $<1\times 10^{-4}$ Hz, we develop a high-performance high-pass filter and consider two possible processing sequences, i.e., applying the filter before or after TDI combinations. Our simulation results favor the former and demonstrate successful removal of the low-frequency gravity disturbances for TianQin without degrading the TDI performance, assuming 10 m pseudo-ranging uncertainty. The filtering scheme can be used in developing the initial noise-reduction pipeline for TianQin.Comment: 10 pages, 11 figures, revised to match the version accepted by Phys. Rev.

Theoretically Principled Federated Learning for Balancing Privacy and Utility

We propose a general learning framework for the protection mechanisms that protects privacy via distorting model parameters, which facilitates the trade-off between privacy and utility. The algorithm is applicable to arbitrary privacy measurements that maps from the distortion to a real value. It can achieve personalized utility-privacy trade-off for each model parameter, on each client, at each communication round in federated learning. Such adaptive and fine-grained protection can improve the effectiveness of privacy-preserved federated learning. Theoretically, we show that gap between the utility loss of the protection hyperparameter output by our algorithm and that of the optimal protection hyperparameter is sub-linear in the total number of iterations. The sublinearity of our algorithm indicates that the average gap between the performance of our algorithm and that of the optimal performance goes to zero when the number of iterations goes to infinity. Further, we provide the convergence rate of our proposed algorithm. We conduct empirical results on benchmark datasets to verify that our method achieves better utility than the baseline methods under the same privacy budget

Effect of Football Shoe Collar Type on Ankle Biomechanics and Dynamic Stability During Anterior and Lateral Single-Leg Jump Landings

In this study, we investigated the effects of football shoes with different collar heights on ankle biomechanics and dynamic postural stability. Fifteen healthy college football players performed anterior and lateral single-leg jump landings when wearing high collar, elastic collar, or low collar football shoes. The kinematics of lower limbs and ground reaction forces were collected by simultaneously using a stereo-photogrammetric system with markers (Vicon) and a force plate (Kistler). During the anterior single-leg jump landing, a high collar shoe resulted in a significantly smaller ankle dorsiflexion range of motion (ROM), compared to both elastic (p = 0.031, dz = 0.511) and low collar (p = 0.043, dz = 0.446) types, while also presenting lower total ankle sagittal ROM, compared to the low collar type (p = 0.023, dz = 0.756). Ankle joint stiffness was significantly greater for the high collar, compared to the elastic collar (p = 0.003, dz = 0.629) and low collar (p = 0.030, dz = 1.040). Medial-lateral stability was significantly improved with the high collar, compared to the low collar (p = 0.001, dz = 1.232). During the lateral single-leg jump landing, ankle inversion ROM (p = 0.028, dz = 0.615) and total ankle frontal ROM (p = 0.019, dz = 0.873) were significantly smaller for the high collar, compared to the elastic collar. The high collar also resulted in a significantly smaller total ankle sagittal ROM, compared to the low collar (p = 0.001, dz = 0.634). Therefore, the high collar shoe should be effective in decreasing the amount of ROM and increasing the dynamic stability, leading to high ankle joint stiffness due to differences in design and material characteristics of the collar types

Efficient ASIC Architectures for Low Latency Niederreiter Decryption

Post-quantum cryptography addresses the increasing threat that quantum computing poses to modern communication systems. Among the available quantum-resistant systems, the Niederreiter cryptosystem is positioned as a conservative choice with strong security guarantees. As a code-based cryptosystem, the Niederreiter system enables high performance operations and is thus ideally suited for applications such as the acceleration of server workloads. However, until now, no ASIC architecture is available for low latency computation of Niederreiter operations. Therefore, the present work targets the design, implementation and optimization of tailored archi- tectures for low latency Niederreiter decryption. Two architectures utilizing different decoding algorithms are proposed and implemented using a 22nm FDSOI CMOS technology node. One of these optimized architectures improves the decryption latency by 27% compared to a state-of-the-art reference and requires at the same time only 25% of the area

Try with Simpler -- An Evaluation of Improved Principal Component Analysis in Log-based Anomaly Detection

The rapid growth of deep learning (DL) has spurred interest in enhancing log-based anomaly detection. This approach aims to extract meaning from log events (log message templates) and develop advanced DL models for anomaly detection. However, these DL methods face challenges like heavy reliance on training data, labels, and computational resources due to model complexity. In contrast, traditional machine learning and data mining techniques are less data-dependent and more efficient but less effective than DL. To make log-based anomaly detection more practical, the goal is to enhance traditional techniques to match DL's effectiveness. Previous research in a different domain (linking questions on Stack Overflow) suggests that optimized traditional techniques can rival state-of-the-art DL methods. Drawing inspiration from this concept, we conducted an empirical study. We optimized the unsupervised PCA (Principal Component Analysis), a traditional technique, by incorporating lightweight semantic-based log representation. This addresses the issue of unseen log events in training data, enhancing log representation. Our study compared seven log-based anomaly detection methods, including four DL-based, two traditional, and the optimized PCA technique, using public and industrial datasets. Results indicate that the optimized unsupervised PCA technique achieves similar effectiveness to advanced supervised/semi-supervised DL methods while being more stable with limited training data and resource-efficient. This demonstrates the adaptability and strength of traditional techniques through small yet impactful adaptations

Selective quantum Zeno effect of ultracold atom-molecule scattering in dynamic magnetic fields

We demonstrated that final states of ultracold scattering between atom and molecule can be selectively produced using dynamic magnetic fields of multiple frequencies. The mechanism of the dynamic magnetic field control is based on a generalized quantum Zeno effect for the selected scattering channels. In particular, we use an atom-molecule spin flip scattering to show that the transition to the selected final spin projection of the molecule in the inelastic scattering can be suppressed by dynamic modulation of coupling between the Floquet engineered initial and final states

Plasma noise in TianQin time delay interferometry

TianQin is a proposed geocentric space-based gravitational wave observatory mission, which requires time-delay interferometry (TDI) to cancel laser frequency noise. With high demands for precision, solar-wind plasma environment at $\sim 10^5$ km above the Earth may constitute a non-negligible noise source to laser interferometric measurements between satellites, as charged particles perturb the refractivity along light paths. In this paper, we first assess the plasma noises along single links from space-weather models and numerical orbits, and analyze the time and frequency domain characteristics. Particularly, to capture the plasma noise in the entire measurement band of $10^{-4} - 1$ Hz, we have performed additional space-weather magnetohydrodynamic simulations in finer spatial and temporal resolutions and utilized Kolmogorov spectra in high-frequency data generation. Then we evaluate the residual plasma noises of the first- and second-generation TDI combinations. Both analytical and numerical estimations have shown that under normal solar conditions the plasma noise after TDI is less than the secondary noise requirement. Moreover, TDI is shown to exhibit moderate suppression on the plasma noise below $\sim 10^{-2}$ Hz due to noise correlation between different arms, when compared with the secondary noise before and after TDI.Comment: 12 pages, 15 figures, accepted by Phys. Rev.