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

Unsupervised Paraphrasing via Deep Reinforcement Learning

Paraphrasing is expressing the meaning of an input sentence in different wording while maintaining fluency (i.e., grammatical and syntactical correctness). Most existing work on paraphrasing use supervised models that are limited to specific domains (e.g., image captions). Such models can neither be straightforwardly transferred to other domains nor generalize well, and creating labeled training data for new domains is expensive and laborious. The need for paraphrasing across different domains and the scarcity of labeled training data in many such domains call for exploring unsupervised paraphrase generation methods. We propose Progressive Unsupervised Paraphrasing (PUP): a novel unsupervised paraphrase generation method based on deep reinforcement learning (DRL). PUP uses a variational autoencoder (trained using a non-parallel corpus) to generate a seed paraphrase that warm-starts the DRL model. Then, PUP progressively tunes the seed paraphrase guided by our novel reward function which combines semantic adequacy, language fluency, and expression diversity measures to quantify the quality of the generated paraphrases in each iteration without needing parallel sentences. Our extensive experimental evaluation shows that PUP outperforms unsupervised state-of-the-art paraphrasing techniques in terms of both automatic metrics and user studies on four real datasets. We also show that PUP outperforms domain-adapted supervised algorithms on several datasets. Our evaluation also shows that PUP achieves a great trade-off between semantic similarity and diversity of expression

Successful Leptogenesis in SO(10) Unification with a Left-Right Symmetric Seesaw Mechanism

We study thermal leptogenesis in a broad class of supersymmetric SO(10) models with a left-right symmetric seesaw mechanism, taking into account flavour effects and the contribution of the next-to-lightest right-handed neutrino supermultiplet. Assuming M_D = M_u and a normal hierarchy of light neutrino masses, we show that four out of the eight right-handed neutrino mass spectra reconstructed from low-energy neutrino data can lead to successful leptogenesis with a reheating temperature in the (10^9 - 10^10) GeV range. In the remaining four solutions, leptogenesis is dominated by N_2 decays, as in the type I seesaw case. We find that some of these spectra can generate the observed baryon asymmetry for reheating temperatures above 10^10 GeV, in contrast to the type I case. Together with flavour effects, an accurate description of charged fermion masses turns out to be a crucial ingredient in the analysis.Comment: 32 pages, 23 figures. v2: 2 comments [below Eq. (53) and at the end of the conclusions] and 1 reference added, typos corrected. Version to be published in Nucl. Phys.

The UV Luminosity Function of Star-forming Galaxies via Dropout Selection at Redshifts z ~ 7 and 8 from the 2012 Ultra Deep Field Campaign

We present a catalog of high-redshift star-forming galaxies selected to lie within the redshift range z ≃ 7-8 using the Ultra Deep Field 2012 (UDF12), the deepest near-infrared (near-IR) exposures yet taken with the Hubble Space Telescope (HST). As a result of the increased near-IR exposure time compared to previous HST imaging in this field, we probe ~0.65 (0.25) mag fainter in absolute UV magnitude, at z ~ 7 (8), which increases confidence in a measurement of the faint end slope of the galaxy luminosity function. Through a 0.7 mag deeper limit in the key F105W filter that encompasses or lies just longward of the Lyman break, we also achieve a much-refined color-color selection that balances high redshift completeness and a low expected contamination fraction. We improve the number of dropout-selected UDF sources to 47 at z ~ 7 and 27 at z ~ 8. Incorporating brighter archival and ground-based samples, we measure the z ≃ 7 UV luminosity function to an absolute magnitude limit of M_(UV) = –17 and find a faint end Schechter slope of ɑ =-1.87^(+0.18)_(-0.17). Using a similar color-color selection at z ≃ 8 that takes our newly added imaging in the F140W filter into account, and incorporating archival data from the HIPPIES and BoRG campaigns, we provide a robust estimate of the faint end slope at z ≃ 8, ɑ =-1.94^(+0.21)_(-0.24). We briefly discuss our results in the context of earlier work and that derived using the same UDF12 data but with an independent photometric redshift technique

CO2–Hydroquinone Clathrate: Synthesis, Purification, Characterization and Crystal Structure

Organic clathrate compounds, particularly those formed between hydroquinone (HQ) and gases, are supramolecular entities recently highlighted as promising alternatives for applications such as gas storage and separation processes. This study provides new insights into CO2–HQ clathrate, which is a key structure in some of the proposed future applications of these compounds. We present a novel synthesis and purification of CO2–HQ clathrate monocrystals. Clathrate crystals obtained from a single synthesis and native HQ are characterized and compared using Raman/Fourier transform infrared/NMR spectroscopies, optical microscopy, and thermogravimetric analysis coupled to mass spectrometry. The molecular structure of the clathrate has been resolved by X-ray diffraction analysis, and detailed crystallographic information is presented for the first time

Soft Water Level Sensors for Characterizing the Hydrological Behaviour of Agricultural Catchments

An innovative soft water level sensor is proposed to characterize the hydrological behaviour of agricultural catchments by measuring rainfall and stream flows. This sensor works as a capacitor coupled with a capacitance to frequency converter and measures water level at an adjustable time step acquisition. It was designed to be handy, minimally invasive and optimized in terms of energy consumption and low-cost fabrication so as to multiply its use on several catchments under natural conditions. It was used as a stage recorder to measure water level dynamics in a channel during a runoff event and as a rain gauge to measure rainfall amount and intensity. Based on the Manning equation, a method allowed estimation of water discharge with a given uncertainty and hence runoff volume at an event or annual scale. The sensor was tested under controlled conditions in the laboratory and under real conditions in the field. Comparisons of the sensor to reference devices (tipping bucket rain gauge, hydrostatic pressure transmitter limnimeter, Venturi channels…) showed accurate results: rainfall intensities and dynamic responses were accurately reproduced and discharges were estimated with an uncertainty usually acceptable in hydrology. Hence, it was used to monitor eleven small agricultural catchments located in the Mediterranean region. Both catchment reactivity and water budget have been calculated. Dynamic response of the catchments has been studied at the event scale through the rising time determination and at the annual scale by calculating the frequency of occurrence of runoff events. It provided significant insight into catchment hydrological behaviour which could be useful for agricultural management perspectives involving pollutant transport, flooding event and global water balance

Probing the in vitro mechanism of action of cationic lipid/DNA lipoplexes at a nanometric scale

Cationic lipids are used for delivering nucleic acids (lipoplexes) into cells for both therapeutic and biological applications. A better understanding of the identified key-steps, including endocytosis, endosomal escape and nuclear delivery is required for further developments to improve their efficacy. Here, we developed a labelling protocol using aminated nanoparticles as markers for plasmid DNA to examine the intracellular route of lipoplexes in cell lines using transmission electron microscopy. Morphological changes of lipoplexes, membrane reorganizations and endosomal membrane ruptures were observed allowing the understanding of the lipoplex mechanism until the endosomal escape mediated by cationic lipids. The study carried out on two cationic lipids, bis(guanidinium)-tris(2-aminoethyl)amine-cholesterol (BGTC) and dioleyl succinyl paramomycin (DOSP), showed two pathways of endosomal escape that could explain their different transfection efficiencies. For BGTC, a partial or complete dissociation of DNA from cationic lipids occurred before endosomal escape while for DOSP, lipoplexes remained visible within ruptured vesicles suggesting a more direct pathway for DNA release and endosome escape. In addition, the formation of new multilamellar lipid assemblies was noted, which could result from the interaction between cationic lipids and cellular compounds. These results provide new insights into DNA transfer pathways and possible implications of cationic lipids in lipid metabolism

Interpreting Deep Learning-Based Networking Systems

While many deep learning (DL)-based networking systems have demonstrated superior performance, the underlying Deep Neural Networks (DNNs) remain blackboxes and stay uninterpretable for network operators. The lack of interpretability makes DL-based networking systems prohibitive to deploy in practice. In this paper, we propose Metis, a framework that provides interpretability for two general categories of networking problems spanning local and global control. Accordingly, Metis introduces two different interpretation methods based on decision tree and hypergraph, where it converts DNN policies to interpretable rule-based controllers and highlight critical components based on analysis over hypergraph. We evaluate Metis over several state-of-the-art DL-based networking systems and show that Metis provides human-readable interpretations while preserving nearly no degradation in performance. We further present four concrete use cases of Metis, showcasing how Metis helps network operators to design, debug, deploy, and ad-hoc adjust DL-based networking systems.Comment: To appear at ACM SIGCOMM 202