Pietro Farina | Searcharxiv

arXiv2025

Gravitational signatures beyond Newton: exploring hierarchical three-body dynamics

Hierarchical three-body systems offer a compelling framework to explore the subtle interplay between Newtonian and relativistic gravitational effects in astrophysical environments. In this work, we investigate post-Newtonian corrections to the periastron shift within such systems, focusing on the impact of orbital eccentricity. Modeling the secondary body's influence as a quadrupolar perturbation, we compare Newtonian, Schwarzschild, and post-Newtonian quadrupolar contributions to orbital precession. Our analysis demonstrates that Newtonian quadrupolar effects could be observable, for a long monitoring time, in the orbit of the S87 star around Sagittarius A* if an intermediate-mass black hole is present, under the assumptions of our model. Additionally, post-Newtonian quadrupolar corrections may influence the dynamics of small Solar System bodies in the presence of massive companions. Although the predicted effects are minute and require long monitoring periods to be measurable, our analysis clarifies how relativistic corrections enter the dynamics of the third body and outlines the conditions under which future observations could reveal them.

gr-qc↗

arXiv2024

Memory-efficient Energy-adaptive Inference of Pre-Trained Models on Batteryless Embedded Systems

Batteryless systems frequently face power failures, requiring extra runtime buffers to maintain inference progress and leaving only a memory space for storing ultra-tiny deep neural networks (DNNs). Besides, making these models responsive to stochastic energy harvesting dynamics during inference requires a balance between inference accuracy, latency, and energy overhead. Recent works on compression mostly focus on time and memory, but often ignore energy dynamics or significantly reduce the accuracy of pre-trained DNNs. Existing energy-adaptive inference works modify the architecture of pre-trained models and have significant memory overhead. Thus, energy-adaptive and accurate inference of pre-trained DNNs on batteryless devices with extreme memory constraints is more challenging than traditional microcontrollers. We combat these issues by proposing FreeML, a framework to optimize pre-trained DNN models for memory-efficient and energy-adaptive inference on batteryless systems. FreeML comprises (1) a novel compression technique to reduce the model footprint and runtime memory requirements simultaneously, making them executable on extremely memory-constrained batteryless platforms; and (2) the first early exit mechanism that uses a single exit branch for all exit points to terminate inference at any time, making models energy-adaptive with minimal memory overhead. Our experiments showed that FreeML reduces the model sizes by up to $95 \times$, supports adaptive inference with a $2.03-19.65 \times$ less memory overhead, and provides significant time and energy benefits with only a negligible accuracy drop compared to the state-of-the-art.

cs.LG↗