Improved quality of online education using prioritized multi-agent reinforcement learning for video traffic scheduling

The recent global pandemic has transformed the way education is delivered, increasing the importance of videobased online learning. However, this puts a significant pressure on the underlying communication networks and the limited available bandwidth needs to be intelligently allocated to support a much higher transmission load, including video-based services. In this context, this paper proposes a Machine Learning (ML)-based solution that dynamically prioritizes content viewers with heterogeneous video services to increase their Quality of Service (QoS) and perceived Quality of Experience (QoE). The proposed approach makes use of the novel Prioritized Multi- Agent Reinforcement Learning solution (PriMARL) to decide the prioritization order of the video-based services based on networking conditions. However, the performance in terms of QoS and QoE provisioning to learners with different profiles and networking conditions depends on the type of scheduler employed in the frequency domain to conduct the scheduling and the radio resource allocation. To decide the best approach to be followed, we employ the proposed PriMARL solution with different types of scheduling rules and compare them with other state-of-theart solutions in terms of throughput, delay, packet loss, Peak Signal-to-Noise Ratio (PSNR), and Mean Opinion Score (MOS) for different traffic loads and characteristics. We show that the proposed solution achieves the best user QoE results

A machine learning resource allocation solution to improve video quality in remote education

The current global pandemic crisis has unquestionably disrupted the higher education sector, forcing educational institutions to rapidly embrace technology-enhanced learning. However, the COVID-19 containment measures that forced people to work or stay at home, have determined a significant increase in the Internet traffic that puts tremendous pressure on the underlying network infrastructure. This affects negatively content delivery and consequently user perceived quality, especially for video-based services. Focusing on this problem, this paper proposes a machine learning-based resource allocation solution that improves the quality of video services for increased number of viewers. The solution is deployed and tested in an educational context, demonstrating its benefit in terms of major quality of service parameters for various video content, in comparison with existing state of the art. Moreover, a discussion on how the technology is helping to mitigate the effects of massively increasing internet traffic on the video quality in an educational context is also presented

Topic Propagation in Conversational Search

In a conversational context, a user expresses her multi-faceted information need as a sequence of natural-language questions, i.e., utterances. Starting from a given topic, the conversation evolves through user utterances and system replies. The retrieval of documents relevant to a given utterance in a conversation is challenging due to ambiguity of natural language and to the difficulty of detecting possible topic shifts and semantic relationships among utterances. We adopt the 2019 TREC Conversational Assistant Track (CAsT) framework to experiment with a modular architecture performing: (i) topic-aware utterance rewriting, (ii) retrieval of candidate passages for the rewritten utterances, and (iii) neural-based re-ranking of candidate passages. We present a comprehensive experimental evaluation of the architecture assessed in terms of traditional IR metrics at small cutoffs. Experimental results show the effectiveness of our techniques that achieve an improvement of up to $0.28$ (+93%) for P@1 and $0.19$ (+89.9%) for nDCG@3 w.r.t. the CAsT baseline

RELATIONSHIP BETWEEN THE MAIN QUALITY PARAMETRES AND ANTOCYANIC CONTENT DURING GRAPES RIPENING IN DRĂNIC VITICULTURAL AREA

The first link technological development of any type of wine and particularly red wine, is to determine the optimal timing of harvest. The parameters taken into account in determining this time, weight of 100 berries, glucides content and total acidity of the grapes, provide information on potential anthocyanin of the species, thus imposing a new criterion for assessing the maturity of red grapes and certain maturity phenolic. To do this for three years at the 3 varieties of red grapes studied in the area of wine shingle, and made determinations by the entry in the ripening until harvest of the main parameters that define the quality of red grapes weight of berries, the contents of glucides, acidity and anthocyans

AROMA PROFILE OF YOUNG WINES FROM TAMAIOASA ROMANEASCA VARIETY (VITIS VINIFERA)GROWN IN DRĂGĂŞANI VENEYARD – ROMANIA

Aroma profile of three different wines from Tamaioasa romaneasca variety and the clones of this variety were analyzed in order to characterize and differentiate them. There were found a total of 16 volatile compounds. Monoterpenes, alcohols, ethyl esters and aldehydes were determined by gas chromatography-mass spectrometry (GC-MS). The results showed that Tamaioasă romaneasca 104 Dg wine had the highest concentration of volatile compounds. Monoterpenes, linalool, terpineol, geraniol and nerol were detected in all three wines but the most aromatic wine was all Tamaioasa romaneasca 104 Dg

The GATA1s isoform is normally down-regulated during terminal haematopoietic differentiation and over-expression leads to failure to repress MYB, CCND2 and SKI during erythroid differentiation of K562 cells

Background: Although GATA1 is one of the most extensively studied haematopoietic transcription factors little is currently known about the physiological functions of its naturally occurring isoforms GATA1s and GATA1FL in humans—particularly whether the isoforms have distinct roles in different lineages and whether they have non-redundant roles in haematopoietic differentiation. As well as being of general interest to understanding of haematopoiesis, GATA1 isoform biology is important for children with Down syndrome associated acute megakaryoblastic leukaemia (DS-AMKL) where GATA1FL mutations are an essential driver for disease pathogenesis. <p/>Methods: Human primary cells and cell lines were analyzed using GATA1 isoform specific PCR. K562 cells expressing GATA1s or GATA1FL transgenes were used to model the effects of the two isoforms on in vitro haematopoietic differentiation. <p/>Results: We found no evidence for lineage specific use of GATA1 isoforms; however GATA1s transcripts, but not GATA1FL transcripts, are down-regulated during in vitro induction of terminal megakaryocytic and erythroid differentiation in the cell line K562. In addition, transgenic K562-GATA1s and K562-GATA1FL cells have distinct gene expression profiles both in steady state and during terminal erythroid differentiation, with GATA1s expression characterised by lack of repression of MYB, CCND2 and SKI. <p/>Conclusions: These findings support the theory that the GATA1s isoform plays a role in the maintenance of proliferative multipotent megakaryocyte-erythroid precursor cells and must be down-regulated prior to terminal differentiation. In addition our data suggest that SKI may be a potential therapeutic target for the treatment of children with DS-AMKL

Dynamics of Quasi-ordered Structure in a Regio-regulated pi-Conjugated Polymer:Poly(4-methylthiazole-2,5-diyl)

Dynamics of regio-regulated Poly(4-methylthiazole-2,5-diyl) [HH-P4MeTz] was inves tigated by solid-state 1H, 2D, 13C NMR spectroscopies, and differential scanning calorimetry(DSC) measurements. DSC, 2D quadrupolar echo NMR, 13C cross-polarization and magic-angle spinning(CPMAS) NMR, and 2D spin-echo(2DSE) CPMAS NMR spectroscopy suggest existence of a quasi-ordered phase in which backbone twists take place with weakened pi-stackings. Two-dimensional exchange 2D NMR(2DEX) detected slow dynamics with a rate of an order of 10^2Hz for the CD_3 group in d_3-HH-P4MeTz at 288K. The frequency dependence of proton longitudinal relaxation rate at 288K shows a omega^-1/2 dependence, which is due to the one-dimensional diffusion-like motion of backbone conformational modulation waves. The diffusion rate was estimated as 3+/-2 GHz, which was approximately 10^7 times larger than that estimated by 2DEX NMR measurements. These results suggest that there exists anomalous dispersion of modulation waves in HH-P4MeTz. The one-dimensional group velocity of the wave packet is responsible for the behavior of proton longitudinal relaxation time. On the other hand, the 2DEX NMR is sensitive to phase velocity of the nutation of methyl groups that is associated with backbone twists. From proton T_1 and T_2 measurements, the activation energy was estimated as 2.9 and 3.4 kcal/mol, respectively. These were in agreement with 3.0 kcal/mol determined by Moller-Plesset(MP2) molecular orbital(MO) calculation. We also performed chemical shielding calculation of the methyl-carbon in order to understand chemical shift tensor behavior, leading to the fact that a quasi-ordered phase coexist with the crystalline phase.Comment: 14 pages, 11 figures, to appear in Phys.Rev.

(So) Big Data and the transformation of the city

The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the “City of Citizens” thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality

Control of intestinal stem cell function and proliferation by mitochondrial pyruvate metabolism.

Most differentiated cells convert glucose to pyruvate in the cytosol through glycolysis, followed by pyruvate oxidation in the mitochondria. These processes are linked by the mitochondrial pyruvate carrier (MPC), which is required for efficient mitochondrial pyruvate uptake. In contrast, proliferative cells, including many cancer and stem cells, perform glycolysis robustly but limit fractional mitochondrial pyruvate oxidation. We sought to understand the role this transition from glycolysis to pyruvate oxidation plays in stem cell maintenance and differentiation. Loss of the MPC in Lgr5-EGFP-positive stem cells, or treatment of intestinal organoids with an MPC inhibitor, increases proliferation and expands the stem cell compartment. Similarly, genetic deletion of the MPC in Drosophila intestinal stem cells also increases proliferation, whereas MPC overexpression suppresses stem cell proliferation. These data demonstrate that limiting mitochondrial pyruvate metabolism is necessary and sufficient to maintain the proliferation of intestinal stem cells