Comyco: Quality-Aware Adaptive Video Streaming via Imitation Learning

Learning-based Adaptive Bit Rate~(ABR) method, aiming to learn outstanding strategies without any presumptions, has become one of the research hotspots for adaptive streaming. However, it typically suffers from several issues, i.e., low sample efficiency and lack of awareness of the video quality information. In this paper, we propose Comyco, a video quality-aware ABR approach that enormously improves the learning-based methods by tackling the above issues. Comyco trains the policy via imitating expert trajectories given by the instant solver, which can not only avoid redundant exploration but also make better use of the collected samples. Meanwhile, Comyco attempts to pick the chunk with higher perceptual video qualities rather than video bitrates. To achieve this, we construct Comyco's neural network architecture, video datasets and QoE metrics with video quality features. Using trace-driven and real-world experiments, we demonstrate significant improvements of Comyco's sample efficiency in comparison to prior work, with 1700x improvements in terms of the number of samples required and 16x improvements on training time required. Moreover, results illustrate that Comyco outperforms previously proposed methods, with the improvements on average QoE of 7.5% - 16.79%. Especially, Comyco also surpasses state-of-the-art approach Pensieve by 7.37% on average video quality under the same rebuffering time.Comment: ACM Multimedia 201

Relative Assessment of Biochemical Constituents and Antioxidant Potential of Fermented Wheat Grains Using Bacillus subtilis KCTC 13241

High antioxidant levels in food are gradually becoming popular because of enhanced risk of oxidative stress in humans. Bread wheat is rich in vital antioxidants, but a major portion of its bioactive compounds are not available to humans. This study was conducted with the aim to fulfill the antioxidants and nutrients gap between the available and potential levels of wheat grains through fermentation by Bacillus subtilis KCTC 13241. In this experiment, the whole wheat grains were used by keeping in consideration the importance of minerals and to measure an increase in their availability after fermentation. The antioxidants and nutritional potential of different wheat varieties was determined by DPPH (2,2-diphenyl-1-picryl- hydrazyl) and ABTS (3-ethyl-benzothiazo- line-6-sulfonic acid) radical scavenging assays as well as by the concentration of amino acids, flavonoids, minerals, carbohydrates and phenolic compounds. Different wheat varieties were showed different free radical scavenging potential after fermentation, which was significantly higher with respect to their corresponding unfermented wheat varieties. The highest nutritional and free radical scavenging potential was found in a fermented wheat variety, named Namhae, and this combination is highly useful for cereal-based food industries

Loss differentiation: Moving onto high-speed wireless LANs

Abstract—A fundamental problem in 802.11 wireless networks is to accurately determine the cause of packet losses. This becomes increasingly important as wireless data rates scale to Gbps, where lack of loss differentiation leads to higher loss in throughput. Recent and upcoming high-speed WLAN standards, such as 802.11n and 802.11ac, use frame aggregation and block acknowledgements for achieving efficient communication. This paper presents BLMon, a framework for loss differentiation, that uses loss patterns within aggregate frames and aggregate frame retries to achieve accurate and low overhead loss differentiation. Towards this end, we carry out a detailed measurement study on a real testbed to ascertain the differences in loss patterns due to noise, collisions, and hidden nodes. We then devise metrics to quantitatively capture these differences. Finally, we design BLMon, which collectively uses these metrics to infer the cause of loss without requiring any out-of-band communication, protocol changes, or customized hardware support. BLMon can be readily deployed on commodity devices using only driver-level changes at the sender-side. We implement BLMon in the ath9k driver and using real testbed experiments, show that it can provide up to 5 improvement in throughput. I

Efficacy of Carbon Nanodots and Manganese Ferrite (MnFe₂O₄) Nanoparticles in Stimulating Growth and Antioxidant Activity in Drought-Stressed Maize Inbred Lines

Despite being the third most-consumed crop, maize (Zea mays L.) is highly vulnerable to drought stress. The predominant secondary metabolite in plants is phenolic acids, which scavenge reactive oxygen species to minimize oxidative stress under drought stress. Herein, the effect of carbon nanodots (CND) and manganese ferrite (MnFe2O4) nanoparticles (NP) on the drought stress tolerance of maize has been studied. The experimental results revealed that the highest leaf blade length (54.0 cm) and width (3.9 cm), root length (45.2 cm), stem diameter (11.1 mm), root fresh weight (7.0 g), leaf relative water content (84.8%) and chlorogenic (8.7 µg/mL), caffeic (3.0 µg/mL) and syringic acid (1.0 µg/mL) contents were demonstrated by CND-treated (10 mg L−1) inbred lines (GP5, HW19, HCW2, 17YS6032, HCW3, HCW4, HW7, HCW2, and 16S8068-9, respectively). However, the highest shoot length (71.5 cm), leaf moisture content (83.9%), shoot fresh weight (12.5 g), chlorophyll content (47.3), and DPPH free radical scavenging activity (34.1%) were observed in MnFe2O4 NP-treated (300 mg L−1) HF12, HW15, 11BS8016-7, HW15, HW12, and KW7 lines, respectively. The results indicate that CND and MnFe2O4 NP can mitigate drought stress effects on different accessions of the given population, as corroborated by improvements in growth and physio-biochemical traits among several inbred lines of maize

Association Mapping for Evaluation of Population Structure, Genetic Diversity, and Physiochemical Traits in Drought-Stressed Maize Germplasm Using SSR Markers

Globally, maize is one of the most consumed crops along with rice and wheat. However, maize is sensitive to different abiotic stress factors, such as drought, which have a significant impact on its production. The aims of this study were to investigate (1) genetic variation among 41 maize-inbred lines and the relationships among them and (2) significant marker–trait associations (SMTAs) between 7 selected physiochemical traits and 200 simple sequence repeat (SSR) markers to examine the genetics of these traits. A total of 1023 alleles were identified among the 41 maize-inbred lines using the 200 SSR loci, with a mean of 5.1 alleles per locus. The average major allele frequency, gene diversity, and polymorphism information content were 0.498, 0.627, and 0.579, respectively. The population structure analysis based on the 200 SSR loci divided the maize germplasm into two primary groups with an admixed group. Moreover, this study identified, respectively, 85 SMTAs and 31 SMTAs using a general linear model (Q GLM) and a mixed linear model (Q  + K MLM) with statistically significant (p < 0.05 and <0.01) associations with the seven physiochemical traits (caffeic acid content, chlorogenic acid content, gallic acid content, ferulic acid content, 2,2-diphenyl-1-picrylhydrazyl free radical scavenging activity, leaf relative moisture content, total phenolic content). These SSR markers were highly correlated with one or more of the seven physiochemical traits. This study provides insights into the genetics of the 41 maize-inbred lines and their seven physiochemical traits and will be of assistance to breeders in the marker-assisted selection of maize for breeding programs