Audience-retention-rate-aware caching and coded video delivery with asynchronous demands

Most of the current literature on coded caching focus on a static scenario, in which a fixed number of users synchronously place their requests from a content library, and the performance is measured in terms of the latency in satisfying all of these requests. In practice, however, users start watching an online video content asynchronously over time, and often abort watching a video before it is completed. The latter behaviour is captured by the notion of audience retention rate, which measures the portion of a video content watched on average. In order to bring coded caching one step closer to practice, asynchronous user demands are considered in this paper, by allowing user demands to arrive randomly over time, and both the popularity of video files, and the audience retention rates are taken into account. A decentralized partial coded delivery (PCD) scheme is proposed, and two cache allocation schemes are employed; namely homogeneous cache allocation (HoCA) and heterogeneous cache allocation (HeCA), which allocate users’ caches among different chunks of the video files in the library. Numerical results validate that the proposed PCD scheme, either with HoCA or HeCA, outperforms conventional uncoded caching as well as the state-of-the-art decentralized caching schemes, which consider only the file popularities, and are designed for synchronous demand arrivals. An information-theoretical lower bound on the average delivery rate is also presented

Optimal energy efficiency link adaptation in IEEE 802.15.6 IR-UWB body area networks

© 2014 IEEE. We propose a novel link adaptation mechanism to maximize energy efficiency in IEEE 802.15.6 impulse radio ultra wideband (IR-UWB) wireless body area networks (WBANs). We consider noncoherent energy detection and autocorrelation receivers, suitable for low complexity implementations. The amount of captured energy is first modeled for the on-body WBAN channel. Using our energy capture model and Gaussian approximations for the decision statistic, the error performance of various physical layer modes of the IEEE 802.15.6 standard is derived assuming intra-symbol interference. We refer to the IEEE 802.15.6 specification as a use case. The proposed adaptation scheme can be applied to any other IR-UWB system with noncoherent receivers and is based on the estimated signal to noise ratio and the channel's energy capture index for which we propose unbiased estimators

Energy-delay tradeoffs in impulse-based ultra-wideband body area networks with noncoherent receivers

© 2014 IEEE. In this paper we address the problem of rate scheduling in the Impulse Radio (IR) ultra-wideband (UWB) wireless body area networks (WBANs) and the minimum energy required to stabilize the queuing system. Targeting low complexity WBAN applications, we assume noncoherent receivers based on energy detection and autocorrelation for all nodes. The coordinating node can minimize the average energy consumption of the system and achieve the queue backlog stability of the sensor nodes by controlling the number of pulses per symbol. We first illustrate the necessary and sufficient conditions of network stability for a multi-mode UWB system and then propose a feasible rate scheduling algorithm based on the Lyapunov optimization theory. The scheduling algorithm uses the instantaneous channel state information and the length of the local queue of all sensor nodes and can approach the optimal energy-delay tradeoff of the network. We apply our theoretical framework to the IR-UWB physical layer of the IEEE 802.15.6 standard and extract the optimal physical layer modes that can achieve the desired energy-delay tradeoff

COVID-19 publications: Database coverage, citations, readers, tweets, news, Facebook walls, Reddit posts

© 2020 The Authors. Published by MIT Press. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1162/qss_a_00066The COVID-19 pandemic requires a fast response from researchers to help address biological, medical and public health issues to minimize its impact. In this rapidly evolving context, scholars, professionals and the public may need to quickly identify important new studies. In response, this paper assesses the coverage of scholarly databases and impact indicators during 21 March to 18 April 2020. The rapidly increasing volume of research, is particularly accessible through Dimensions, and less through Scopus, the Web of Science, and PubMed. Google Scholar’s results included many false matches. A few COVID-19 papers from the 21,395 in Dimensions were already highly cited, with substantial news and social media attention. For this topic, in contrast to previous studies, there seems to be a high degree of convergence between articles shared in the social web and citation counts, at least in the short term. In particular, articles that are extensively tweeted on the day first indexed are likely to be highly read and relatively highly cited three weeks later. Researchers needing wide scope literature searches (rather than health focused PubMed or medRxiv searches) should start with Dimensions (or Google Scholar) and can use tweet and Mendeley reader counts as indicators of likely importance

Influence of a classical homogeneous gravitational field on dissipative dynamics of the Jaynes-Cummings model with phase damping

In this paper, we study the dissipative dynamics of the Jaynes-Cummings model with phase damping in the presence of a classical homogeneous gravitational field. The model consists of a moving two-level atom simultaneously exposed to the gravitational field and a single-mode traveling radiation field in the presence of the phase damping. We present a quantum treatment of the internal and external dynamics of the atom based on an alternative su(2) dynamical algebraic structure. By making use of the super-operator technique, we obtain the solution of the master equation for the density operator of the quantum system, under the Markovian approximation. Assuming that initially the radiation field is prepared in a Glauber coherent state and the two-level atom is in the excited state, we investigate the influence of gravity on the temporal evolution of collapses and revivals of the atomic population inversion, atomic dipole squeezing, atomic momentum diffusion, photon counting statistics and quadrature squeezing of the radiation field in the presence of phase damping.Comment: 25 pages, 15 figure

Synthesis and characterization of ZnO nanoparticle synthesized by a microwave-assisted combustion method and catalytic activity for the removal of ortho-nitrophenol

ZnO nanoparticles were manufactured using microwave-assisted combustion. The structural and morphological properties of the nanoparticles were characterized by X-ray diffraction (XRD), field emission scanning electron microscopy, and Fourier transform infrared spectroscopy. Photocatalytic degradation of ortho-nitrophenol (O-NP) in aqueous solution using the synthesized nanoparticles was performed under UV–C irradiation and is reported for the first time. The effect of the initial O-NP concentration, amount of photocatalyst, pH, and salt was investigated during photodegradation. Analysis of the degraded samples using HPLC with UV detection revealed that photocatalysis in the presence of ZnO nanoparticles removed 98% of the O-NP in 5 h. In addition, the photocatalytic degradation kinetics of O-NP were studied, and the results suggest that the data are best fitted to pseudo-first-order kinetic and Langmuir–Hinshelwood models

Are Mendeley Reader Counts Useful Impact Indicators in all Fields?

Reader counts from the social reference sharing site Mendeley are known to be valuable for early research evaluation. They have strong correlations with citation counts for journal articles but appear about a year before them. There are disciplinary differences in the value of Mendeley reader counts but systematic evidence is needed at the level of narrow fields to reveal its extent. In response, this article compares Mendeley reader counts with Scopus citation counts for journal articles from 2012 in 325 narrow Scopus fields. Despite strong positive correlations in most fields, averaging 0.671, the correlations in some fields are as weak as 0.255. Technical reasons explain most weaker correlations, suggesting that the underlying relationship is almost always strong. The exceptions are caused by unusually high educational or professional use or topics of interest within countries that avoid Mendeley. The findings suggest that if care is taken then Mendeley reader counts can be used for early citation impact evidence in almost all fields and for related impact in some of the remainder. As an additional application of the results, cross-checking with Mendeley data can be used to identify indexing anomalies in citation databases

Fast and Accurate Lung Tumor Spotting and Segmentation for Boundary Delineation on CT Slices In A Coarse-To-Fine Framework

Label noise and class imbalance are two of the critical challenges when training image-based deep neural networks, especially in the biomedical image processing domain. Our work focuses on how to address the two challenges effectively and accurately in the task of lesion segmentation from biomedical/medical images. To address the pixel-level label noise problem, we propose an advanced transfer training and learning approach with a detailed DICOM pre-processing method. To address the tumor/non-tumor class imbalance problem, we exploit a self-adaptive fully convolutional neural network with an automated weight distribution mechanism to spot the Radiomics lung tumor regions accurately. Furthermore, an improved conditional random field method is employed to obtain sophisticated lung tumor contour delineation and segmentation. Finally, our approach has been evaluated using several well-known evaluation metrics on the Lung Tumor segmentation dataset used in the 2018 IEEE VIP-CUP Challenge. Experimental results show that our weakly supervised learning algorithm outperforms other deep models and state-of-the-art approache

Applications of Nature-Inspired Algorithms for Dimension Reduction: Enabling Efficient Data Analytics

In [1], we have explored the theoretical aspects of feature selection and evolutionary algorithms. In this chapter, we focus on optimization algorithms for enhancing data analytic process, i.e., we propose to explore applications of nature-inspired algorithms in data science. Feature selection optimization is a hybrid approach leveraging feature selection techniques and evolutionary algorithms process to optimize the selected features. Prior works solve this problem iteratively to converge to an optimal feature subset. Feature selection optimization is a non-specific domain approach. Data scientists mainly attempt to find an advanced way to analyze data n with high computational efficiency and low time complexity, leading to efficient data analytics. Thus, by increasing generated/measured/sensed data from various sources, analysis, manipulation and illustration of data grow exponentially. Due to the large scale data sets, Curse of dimensionality (CoD) is one of the NP-hard problems in data science. Hence, several efforts have been focused on leveraging evolutionary algorithms (EAs) to address the complex issues in large scale data analytics problems. Dimension reduction, together with EAs, lends itself to solve CoD and solve complex problems, in terms of time complexity, efficiently. In this chapter, we first provide a brief overview of previous studies that focused on solving CoD using feature extraction optimization process. We then discuss practical examples of research studies are successfully tackled some application domains, such as image processing, sentiment analysis, network traffics / anomalies analysis, credit score analysis and other benchmark functions/data sets analysis

Thermal Correlators in Holographic Models with Lifshitz scaling

We study finite temperature effects in two distinct holographic models that exhibit Lifshitz scaling, looking to identify model independent features in the dual strong coupling physics. We consider the thermodynamics of black branes and find different low-temperature behavior of the specific heat. Deformation away from criticality leads to non-trivial temperature dependence of correlation functions and we study how the characteristic length scale in the two point function of scalar operators varies as a function of temperature and deformation parameters.Comment: 28 pages, 8 figures; typos corrected, references added, published versio