Advancing Android Activity Recognition Service with Markov Smoother: Practical Solutions

Common use of smartphones is a compelling reason for performing activity recognition with on-board sensors as it is more practical than other approaches, such as wearable sensors and augmented environments. Many solutions have been proposed by academia, but practical use is limited to experimental settings. Ad hoc solutions exist with different degrees in recognition accuracy and efficiency. To ease the development of activity recognition for the mobile application eco-system, Google released an activity recognition service on their Android platform. In this paper, we present a systematic evaluation of this activity recognition service and share the lesson learnt. Through our experiments, we identified scenarios in which the recognition accuracy was barely acceptable. We analyze the cause of the inaccuracy and propose four practical and light-weight solutions to significantly improve the recognition accuracy and efficiency. Our evaluation confirmed the improvement. As a contribution, we released the proposed solutions as open-source projects for developers who want to incorporate activity recognition into their applications

The Role of lncRNA-Mediated Pyroptosis in Cardiovascular Diseases

Cardiovascular disease (CVD) is the leading cause of death worldwide. Pyroptosis is a unique kind of programmed cell death that varies from apoptosis and necrosis morphologically, mechanistically, and pathophysiologically. Long non-coding RNAs (LncRNAs) are thought to be promising biomarkers and therapeutic targets for the diagnosis and treatment of a variety of diseases, including cardiovascular disease. Recent research has demonstrated that lncRNA-mediated pyroptosis has significance in CVD and that pyroptosis-related lncRNAs may be potential targets for the prevention and treatment of specific CVDs such as diabetic cardiomyopathy (DCM), atherosclerosis (AS), and myocardial infarction (MI). In this paper, we collected previous research on lncRNA-mediated pyroptosis and investigated its pathophysiological significance in several cardiovascular illnesses. Interestingly, certain cardiovascular disease models and therapeutic medications are also under the control of lncRNa-mediated pyroptosis regulation, which may aid in the identification of new diagnostic and therapy targets. The discovery of pyroptosis-related lncRNAs is critical for understanding the etiology of CVD and may lead to novel targets and strategies for prevention and therapy

Entanglement distribution maximization over one-side Gaussian noisy channel

The optimization of entanglement evolution for two-mode Gaussian pure states under one-side Gaussian map is studied. Even there isn't complete information about the one-side Gaussian noisy channel, one can still maximize the entanglement distribution by testing the channel with only two specific states

Steady-state edge burst: From free-particle systems to interaction-induced phenomena

The interplay between the non-Hermitian skin effect and the imaginary gap of lossy lattices results in the edge burst, a boundary-induced dynamical phenomenon in which an exceptionally large portion of particle loss occurs at the edge. Here, we find that this intriguing non-Hermitian dynamical phenomenon can be exactly mapped into the steady-state density distribution of a corresponding open quantum system. Consequently, the bulk-edge scaling relation of loss probability in the edge burst maps to that of steady-state density. Furthermore, we introduce a many-body open-system model in which the two-body loss generates an interaction-induced non-Hermitian skin effect. Using the positive-$P$ method, we demonstrate the validity of the scaling relation for steady-state correlators. These results provide a unique perspective on the interaction-induced many-body non-Hermitian skin effect. Our predictions are testable in state-of-the-art experimental platforms.Comment: 13 pages, 6 figure