Transition Complexity of Incomplete DFAs

In this paper, we consider the transition complexity of regular languages based on the incomplete deterministic finite automata. A number of results on Boolean operations have been obtained. It is shown that the transition complexity results for union and complementation are very different from the state complexity results for the same operations. However, for intersection, the transition complexity result is similar to that of state complexity.Comment: In Proceedings DCFS 2010, arXiv:1008.127

SDEMG: Score-based Diffusion Model for Surface Electromyographic Signal Denoising

Surface electromyography (sEMG) recordings can be influenced by electrocardiogram (ECG) signals when the muscle being monitored is close to the heart. Several existing methods use signal-processing-based approaches, such as high-pass filter and template subtraction, while some derive mapping functions to restore clean sEMG signals from noisy sEMG (sEMG with ECG interference). Recently, the score-based diffusion model, a renowned generative model, has been introduced to generate high-quality and accurate samples with noisy input data. In this study, we proposed a novel approach, termed SDEMG, as a score-based diffusion model for sEMG signal denoising. To evaluate the proposed SDEMG approach, we conduct experiments to reduce noise in sEMG signals, employing data from an openly accessible source, the Non-Invasive Adaptive Prosthetics database, along with ECG signals from the MIT-BIH Normal Sinus Rhythm Database. The experiment result indicates that SDEMG outperformed comparative methods and produced high-quality sEMG samples. The source code of SDEMG the framework is available at: https://github.com/tonyliu0910/SDEMGComment: This paper is accepted by ICASSP 202

Next-to-leading order QCD effect of W′W' on top quark Forward-Backward Asymmetry

We present the calculations of the complete next-to-leading order (NLO) QCD corrections to the total cross section, invariant mass distribution and the forward-backward asymmetry ($\rm A_{FB}$) of top quark pair production mediated by $W'$ boson. Our results show that in the best fit point in the parameter space allowed by data at the Tevatron, the NLO corrections change the new physics contributions to the total cross section slightly, but increase the $\rm A_{FB}$ in the large invariant mass region by about 9%. Moreover, we evaluate the total cross section and charge asymmetry ($\rm{A}_{\rm{C}}$) of top pair production at the LHC, and find that both total cross section and $A_{\rm C}$ can be used to distinguish NP from SM with the integrated luminosity increasing.Comment: 24 pages, 9 figures, 1 tabl

The Research on the Detection of Noteworthy Symptom Descriptions

The advance of mobile devices and communication technologies enable patients to communicate with their doctors in a more convenient way. We have developed an App that allows patients to record their symptoms and submit them to their doctors. Physicians can keep track of patients’ conditions by looking at the self-report messages. Nevertheless, physicians are usually busy and may be overwhelmed by the large amount of incoming messages. As a result, critical messages may not receive immediate attentions, and patient care is compromised. It is imperative to identify the messages that require physicians’ attention, called noteworthy messages. In this research, we propose an approach that applies text-mining technologies to identify medical symptoms conveyed in the messages and their associated sentiment orientation, as well as other factors. Noteworthy messages are subsequently characterized by symptom sentiment and symptom change features. We then construct a prediction model to identify messages that are noteworthy to the physicians. We show from our experiments using data collected from a teaching hospital in Taiwan that the different features have different degrees of impact on the performance of the prediction model, and our proposed approach can effectively identify noteworthy messages