Decoding by Sampling: A Randomized Lattice Algorithm for Bounded Distance Decoding

Despite its reduced complexity, lattice reduction-aided decoding exhibits a widening gap to maximum-likelihood (ML) performance as the dimension increases. To improve its performance, this paper presents randomized lattice decoding based on Klein's sampling technique, which is a randomized version of Babai's nearest plane algorithm (i.e., successive interference cancelation (SIC)). To find the closest lattice point, Klein's algorithm is used to sample some lattice points and the closest among those samples is chosen. Lattice reduction increases the probability of finding the closest lattice point, and only needs to be run once during pre-processing. Further, the sampling can operate very efficiently in parallel. The technical contribution of this paper is two-fold: we analyze and optimize the decoding radius of sampling decoding resulting in better error performance than Klein's original algorithm, and propose a very efficient implementation of random rounding. Of particular interest is that a fixed gain in the decoding radius compared to Babai's decoding can be achieved at polynomial complexity. The proposed decoder is useful for moderate dimensions where sphere decoding becomes computationally intensive, while lattice reduction-aided decoding starts to suffer considerable loss. Simulation results demonstrate near-ML performance is achieved by a moderate number of samples, even if the dimension is as high as 32

Examining teachers’ influence on MOOCs learners’ continuance learning intention: The mediating effects of perceived usefulness and satisfaction

Although Massive Open Online Courses (MOOCs) have attracted extensive attention among educational stakeholders, the issue of the high dropout rate has yet to be solved. The current study aimed to unpack teacher influence on MOOCs learners’ continuance learning intention, and to examine the mediating roles of students’ perceived usefulness and satisfaction. Quantitative data were collected from 166 Chinese university students located in 18 provinces. Results indicated that teacher influence is significantly associated with learners’ continuous learning intention, and when considering perceived usefulness and satisfaction, this relationship did not achieve significance but was mediated by students’ perceived usefulness and satisfaction, in addition, teacher influence did not exert a direct and significant impact on students’ satisfaction. The serial mediation model explained 65.8% of the variance of students’ continuance intention. This study uncovered the important role of teacher influence on students’ continuance learning intention in the Chinese MOOCs learning context. Results provided suggestions to policymakers, MOOCs platform and lecturers to promote MOOCs and design useful courses so as to engage students to learn continuously

Patient-specific fetal radiation dosimetry for pregnant patients undergoing abdominal and pelvic CT imaging

Background: Accurate estimation of fetal radiation dose is crucial for risk-benefit analysis of radiological imaging, while the radiation dosimetry studies based on individual pregnant patient are highly desired. Purpose: To use Monte Carlo calculations for estimation of fetal radiation dose from abdominal and pelvic computed tomography (CT) examinations for a population of patients with a range of variations in patients’ anatomy, abdominal circumference, gestational age (GA), fetal depth (FD), and fetal development. Methods: Forty-four patient-specific pregnant female models were constructed based on CT imaging data of pregnant patients, with gestational ages ranging from 8 to 35 weeks. The simulation of abdominal and pelvic helical CT examinations was performed on three validated commercial scanner systems to calculate organ-level fetal radiation dose. Results: The absorbed radiation dose to the fetus ranged between 0.97 and 2.24 mGy, with an average of 1.63 ± 0.33 mGy. The CTDIvol-normalized fetal dose ranged between 0.56 and 1.30, with an average of 0.94 ± 0.25. The normalized fetal organ dose showed significant correlations with gestational age, maternal abdominal circumference (MAC), and fetal depth. The use of ATCM technique increased the fetal radiation dose in some patients. Conclusion: A technique enabling the calculation of organ-level radiation dose to the fetus was developed from models of actual anatomy representing a range of gestational age, maternal size, and fetal position. The developed maternal and fetal models provide a basis for reliable and accurate radiation dose estimation to fetal organs.</p