Online Reinforcement Learning of X-Haul Content Delivery Mode in Fog Radio Access Networks

We consider a Fog Radio Access Network (F-RAN) with a Base Band Unit (BBU) in the cloud and multiple cache-enabled enhanced Remote Radio Heads (eRRHs). The system aims at delivering contents on demand with minimal average latency from a time-varying library of popular contents. Information about uncached requested files can be transferred from the cloud to the eRRHs by following either backhaul or fronthaul modes. The backhaul mode transfers fractions of the requested files, while the fronthaul mode transmits quantized baseband samples as in Cloud-RAN (C-RAN). The backhaul mode allows the caches of the eRRHs to be updated, which may lower future delivery latencies. In contrast, the fronthaul mode enables cooperative C-RAN transmissions that may reduce the current delivery latency. Taking into account the trade-off between current and future delivery performance, this paper proposes an adaptive selection method between the two delivery modes to minimize the long-term delivery latency. Assuming an unknown and time-varying popularity model, the method is based on model-free Reinforcement Learning (RL). Numerical results confirm the effectiveness of the proposed RL scheme.Comment: 5 pages, 2 figure

Online Reinforcement Learning of X-Haul Content Delivery Mode in Fog Radio Access Networks

We consider a Fog Radio Access Network (F-RAN) with a Base Band Unit (BBU) in the cloud and multiple cache-enabled enhanced Remote Radio Heads (eRRHs). The system aims at delivering contents on demand with minimal average latency from a time-varying library of popular contents. Information about uncached requested files can be transferred from the cloud to the eRRHs by following either backhaul or fronthaul modes. The backhaul mode transfers fractions of the requested files, while the fronthaul mode transmits quantized baseband samples as in Cloud-RAN (C-RAN). The backhaul mode allows the caches of the eRRHs to be updated, which may lower future delivery latencies. In contrast, the fronthaul mode enables cooperative C-RAN transmissions that may reduce the current delivery latency. Taking into account the trade-off between current and future delivery performance, this paper proposes an adaptive selection method between the two delivery modes to minimize the long-term delivery latency. Assuming an unknown and time-varying popularity model, the method is based on model-free Reinforcement Learning (RL). Numerical results confirm the effectiveness of the proposed RL scheme.Comment: 12 pages, 2 figure

Elevation of serum lactate dehydrogenase in patients with pectus excavatum

INTRODUCTION: Pectus excavatum is the most common congenital chest wall deformity and the depression of the anterior chest wall, which compresses the internal organs. The aim of the present study is to investigate the effects of pectus excavatum on blood laboratory findings. MATERIAL AND METHODS: From March 2011 to December 2011, 71 patients with pectus excavatum who visited Seoul Saint Mary Hospital for Nuss procedure were reviewed and analyzed. The blood samples were routinely taken at the day before surgery and pectus bar removal was usually performed in 2 to 3 years after Nuss procedure. To investigate the effects on blood laboratory findings, preoperative routine blood laboratory data and postoperative changes of abnormal laboratory data were analyzed. RESULTS: Only lactate dehydrogenase (LDH), one of 26 separate routine laboratory tests, was abnormal and significantly elevated than normal value (age <10, p = 0.008; age ≥10, p < 0.001). However, there was no significant correlation between LDH levels and severities of pectus excavatum. The symmetric subgroup had significantly higher LDH level than the asymmetric subgroup (p <0.001) and there was a significant decrease of LDH level after correction of deformity (p = 0.017). CONCLUSION: In conclusion, only LDH, one of the routine laboratory tests, was significantly elevated than normal value, which was thought to be caused by etiologies of pectus excavatum and the compression of the internal organs. Further studies on LDH including isoenzyme studies in patients with pectus excavatum will be needed, and these studies will provide a deeper and wider comprehension of pectus excavatum

EFFECTS OF GENDER AND FOOT POSITION ON ACCELERATION PATTERN OF KNEE AND HIP JOINT DURING DEEP SQUAT

The purpose of this study was to investigate the effect of gender and foot position on the acceleration patterns of the knee and hip joints during deep squat. Twenty-two male and 10 female collegiate students participated in this study. All the participants performed a deep squat two times in neutral foot position (NFP), with the foot rotated externally by 15° (ERFP). A wireless triaxial accelerometer was attached on the right-side knee and hip joints of each participant. Acceleration data generated in the anterior-posterior (AP), medio-lateral (ML), and superior-inferior (SI) directions during deep squat were collected through the attached acceleration sensor (2000Hz). Statistical analysis was performed using SPSS 24.0, and mixed analysis of variance (p \u3c 0.05) was used to identify the interaction and main effects of gender and foot positions. The acceleration patterns of the knee joint during deep squat according to gender indicated differences between the AP and ML directions. The acceleration motion of the hip joint under the ERFP condition indicated a difference in the SI direction