Related papers: Tune Evaluation From Phased BPM Turn-By-Turn Data
In this paper we study how to shape temporal pulses to switch a bistable system between its stable steady states. Our motivation for pulse-based control comes from applications in synthetic biology, where it is generally difficult to…
The dipole mode synchrotron frequency is a basic beam parameter; it and a few similarly basic quantities measured at small time intervals serve to characterize the longitudinal beam dynamics throughout the acceleration cycle. The effective…
This article presents a method that uses turn-by-turn beam position data and k-modulation data to measure the calibration factors of beam position monitors in high energy accelerators. In this method, new algorithms have been developed to…
Dynamic spectroscopies in Scanning Probe Microscopy (SPM) are critical for probing material properties, such as force interactions, mechanical properties, polarization switching, and electrochemical reactions and ionic dynamics. However,…
The response of nonlinear resonators to multifrequency driving reveals rich dynamics beyond conventional single-tone theory. We study a Duffing resonator under bichromatic excitation and identify a competition between the two drives,…
This study introduces an advanced transient stability assessment (TSA) method for power systems, addressing the challenges of sample class imbalance and data noise through a novel CatBoost algorithm framework. By implementing a Gradient…
The extraction bucket position in the Fermilab Booster is controlled with a cogging process that involves the comparison of the Booster RF count and the Recycler Ring revolution marker. A one RF bucket jitter in the extraction bucket…
The Fermilab Linac experiences longitudinal beam phase drift, leading to increased particle loss, conventionally corrected through labor-intensive manual RF adjustments. This project explores machine learning-based automation for drift…
Pressure is the cleanest way to tune heavy fermion systems to a quantum phase transition in order to study the rich physics and competing phases, and the comparison between ytterbium and cerium systems is particularly fruitful. We briefly…
In this paper, we present an algorithm for learning time-correlated measurement covariances for application in batch state estimation. We parameterize the inverse measurement covariance matrix to be block-banded, which conveniently…
At submicron manufacturing technology nodes process variations affect circuit performance significantly. This trend leads to a large timing margin and thus overdesign to maintain yield. To combat this pessimism, post-silicon clock tuning…
The timing system of SPring-8 booster synchrotron generates various timing signals concerning beam injection, acceleration from 1 GeV to 8 GeV and ejection. We have improved the timing system of the synchrotron giving it better stability…
We propose a transformer-based rhythm quantization model that incorporates beat and downbeat information to quantize MIDI performances into metrically-aligned, human-readable scores. We propose a beat-based preprocessing method that…
This paper presents a comparative analysis on two artificial neural networks (with different architectures) for the task of tempo estimation. For this purpose, it also proposes the modeling, training and evaluation of a B-RNN (Bidirectional…
The growth in the number of low-cost narrow band radios such as Bluetooth low energy (BLE) enabled applications such as asset tracking, human behavior monitoring, and keyless entry. The accurate range estimation is a must in such…
This paper investigates the accuracy of bootstrap-based bias correction of persistence measures for long memory fractionally integrated processes. The bootstrap method is based on the semi-parametric sieve approach, with the dynamics in the…
Effectively finetuning pretrained language models (PLMs) is critical for their success in downstream tasks. However, PLMs may have risks in overfitting the pretraining tasks and data, which usually have gap with the target downstream tasks.…
Machine-learning interatomic potentials are widely used as computationally efficient surrogates for density functional theory in atomistic simulations, enabling large-scale, long-time modeling of materials systems. We investigate how…
In Patil et. al 2024a, we developed a multitaper power spectrum estimation method, mtNUFFT, for analyzing time-series with quasi-regular spacing, and showed that it not only improves upon the statistical issues of the Lomb-Scargle…
Phase estimation is known to be a robust method for single-qubit gate calibration in quantum computers, while Bayesian estimation is widely used in devising optimal methods for learning in quantum systems. We present Bayesian phase…