Relation between space-time inversion and particle-antiparticle symmetry and the microscopic essence of special relativity

After analyzing the implication of investigations on the C, P and T transformations since 1956, we propose that there is a basic symmetry in particle physics. The combined space-time inversion is equivalent to particle-antiparticle transformation, denoted by ${\cal PT=C}$. It is shown that the relativistic quantum mechanics and quantum field theory do contain this invariance explicitly or implicitly. In particular, (a) the appearance of negative energy or negative probability density in single particle theory -- corresponding to the fact of existence of antiparticle, (b) spin- statistics connection, (c) CPT theorem, (d) the Feynman propagator are linked together via this symmetry. Furthermore, we try to derive the main results of special relativity, especially, (e) the mass-energy relation, (f) the Lorentz transformation by this one ``relativistic'' postulate and some ``nonrelativistic'' knowledge.Comment: 29 pages, Latex, 1 figur

Long-term Blood Pressure Prediction with Deep Recurrent Neural Networks

Existing methods for arterial blood pressure (BP) estimation directly map the input physiological signals to output BP values without explicitly modeling the underlying temporal dependencies in BP dynamics. As a result, these models suffer from accuracy decay over a long time and thus require frequent calibration. In this work, we address this issue by formulating BP estimation as a sequence prediction problem in which both the input and target are temporal sequences. We propose a novel deep recurrent neural network (RNN) consisting of multilayered Long Short-Term Memory (LSTM) networks, which are incorporated with (1) a bidirectional structure to access larger-scale context information of input sequence, and (2) residual connections to allow gradients in deep RNN to propagate more effectively. The proposed deep RNN model was tested on a static BP dataset, and it achieved root mean square error (RMSE) of 3.90 and 2.66 mmHg for systolic BP (SBP) and diastolic BP (DBP) prediction respectively, surpassing the accuracy of traditional BP prediction models. On a multi-day BP dataset, the deep RNN achieved RMSE of 3.84, 5.25, 5.80 and 5.81 mmHg for the 1st day, 2nd day, 4th day and 6th month after the 1st day SBP prediction, and 1.80, 4.78, 5.0, 5.21 mmHg for corresponding DBP prediction, respectively, which outperforms all previous models with notable improvement. The experimental results suggest that modeling the temporal dependencies in BP dynamics significantly improves the long-term BP prediction accuracy.Comment: To appear in IEEE BHI 201

Use staged reading to improve sentences reading skill in Chinese language teaching: qualitative research management

This study was to examine the effectiveness in improving the skills of reading sentences. Skills in reading sentences focus on teaching and learning Chinese. In the study, the samples were identified as non-Chinese students. They are among those who use or learn Chinese as a second language. The method used in this study is reading stages. The methodology of this study is qualitative. The study was conducted in one of Sekolah Jenis Kebangsaan Cina (SJKC) or Chinese National Type School in Lawas, Sarawak, Malaysia. The samples selected were 4 students who are studying in year one based on their results in the summative evaluation on the Chinese Language subject at this point. The research data will be collected and analyzed through observation, interviews, record reflections of researcher and also the supporting documents. Through the data obtained from the 3 cycles of studies, students can recognize available samples and pronounce of 5 simple words, 5 average difficult words and 5 difficult words with correct pronunciation. Samples students can also read at least two simple sentences, 1 average difficult sentence and 1 difficult sentence correctly, fluently and with the correct intonation

Energy Efficient Uplink Transmissions in LoRa Networks

LoRa has been recognized as one of the most promising low-power wide-area (LPWA) techniques. Since LoRa devices are usually powered by batteries, energy efficiency (EE) is an essential consideration. In this paper, we investigate the energy efficient resource allocation in LoRa networks to maximize the system EE (SEE) and the minimal EE (MEE) of LoRa users, respectively. Specifically, our objective is to maximize the corresponding EE by jointly exploiting user scheduling, spreading factor (SF) assignment, and transmit power allocations. To solve them efficiently, we first propose a suboptimal algorithm, including the low-complexity user scheduling scheme based on matching theory and the heuristic SF assignment approach for LoRa users scheduled on the same channel. Then, to deal with the power allocation, an optimal algorithm is proposed to maximize the SEE. To maximize the MEE of LoRa users assigned to the same channel, an iterative power allocation algorithm based on the generalized fractional programming and sequential convex programming is proposed. Numerical results show that the proposed user scheduling algorithm achieves near-optimal EE performance, and the proposed power allocation algorithms outperform the benchmarks. © 2020 IEEE