Showing papers by "University of Waterloo published in 2021"
TL;DR: Why COVID-19 is an analogue to the ongoing climate crisis, and why there is a need to question the volume growth tourism model advocated by UNWTO, ICAO, CLIA, WTTC and other tourism organizations are discussed.
Abstract: The novel coronavirus (COVID-19) is challenging the world. With no vaccine and limited medical capacity to treat the disease, nonpharmaceutical interventions (NPI) are the main strategy to contain ...
2,508 citations
Daniel J. Klionsky1, Amal Kamal Abdel-Aziz2, Sara Abdelfatah3, Mahmoud Abdellatif4 +2980 more•Institutions (777)
TL;DR: In this article, the authors present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes.
Abstract: In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.
1,129 citations
Southeast University1, ShanghaiTech University2, Beijing University of Posts and Telecommunications3, University of Electronic Science and Technology of China4, China Mobile Research Institute5, University of Southampton6, University of Waterloo7, University of Technology, Sydney8, University of Manchester9, University of Edinburgh10, Huawei11, Linköping University12, Queen's University Belfast13, Georgia Institute of Technology14, University of Surrey15, Princeton University16, Dresden University of Technology17
TL;DR: 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.
Abstract: The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.
935 citations
TL;DR: A comprehensive review of deep learning-based image segmentation can be found in this article, where the authors investigate the relationships, strengths, and challenges of these DL-based models, examine the widely used datasets, compare performances, and discuss promising research directions.
Abstract: Image segmentation is a key task in computer vision and image processing with important applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among others, and numerous segmentation algorithms are found in the literature. Against this backdrop, the broad success of Deep Learning (DL) has prompted the development of new image segmentation approaches leveraging DL models. We provide a comprehensive review of this recent literature, covering the spectrum of pioneering efforts in semantic and instance segmentation, including convolutional pixel-labeling networks, encoder-decoder architectures, multiscale and pyramid-based approaches, recurrent networks, visual attention models, and generative models in adversarial settings. We investigate the relationships, strengths, and challenges of these DL-based segmentation models, examine the widely used datasets, compare performances, and discuss promising research directions.
827 citations
University of Edinburgh1, Autonomous University of Madrid2, Pierre-and-Marie-Curie University3, University of Portsmouth4, University of Wisconsin-Madison5, University of Cape Town6, Moscow State University7, New Mexico State University8, New York University9, University of Utah10, University of Toronto11, Université Paris-Saclay12, University of Waterloo13, Sejong University14, Stanford University15, Max Planck Society16, University College London17, Texas Christian University18, National Autonomous University of Mexico19, University of Barcelona20, Consejo Nacional de Ciencia y Tecnología21, Universidad de Guanajuato22, Liverpool John Moores University23, Lawrence Berkeley National Laboratory24, Ohio State University25, École Polytechnique Fédérale de Lausanne26, University of Wyoming27, Haverford College28, University of Oxford29, University of Pittsburgh30, Perimeter Institute for Theoretical Physics31, California Institute of Technology32, Ohio University33, Swinburne University of Technology34, Fermilab35, University of Washington36, Korea Astronomy and Space Science Institute37, Brookhaven National Laboratory38, University of St Andrews39, Durham University40
TL;DR: In this article, the authors present the cosmological implications from final measurements of clustering using galaxies, quasars, and Lyα forests from the completed SDSS lineage of experiments in large-scale structure.
Abstract: We present the cosmological implications from final measurements of clustering using galaxies, quasars, and Lyα forests from the completed Sloan Digital Sky Survey (SDSS) lineage of experiments in large-scale structure. These experiments, composed of data from SDSS, SDSS-II, BOSS, and eBOSS, offer independent measurements of baryon acoustic oscillation (BAO) measurements of angular-diameter distances and Hubble distances relative to the sound horizon, rd, from eight different samples and six measurements of the growth rate parameter, fσ8, from redshift-space distortions (RSD). This composite sample is the most constraining of its kind and allows us to perform a comprehensive assessment of the cosmological model after two decades of dedicated spectroscopic observation. We show that the BAO data alone are able to rule out dark-energy-free models at more than eight standard deviations in an extension to the flat, ΛCDM model that allows for curvature. When combined with Planck Cosmic Microwave Background (CMB) measurements of temperature and polarization, under the same model, the BAO data provide nearly an order of magnitude improvement on curvature constraints relative to primary CMB constraints alone. Independent of distance measurements, the SDSS RSD data complement weak lensing measurements from the Dark Energy Survey (DES) in demonstrating a preference for a flat ΛCDM cosmological model when combined with Planck measurements. The combined BAO and RSD measurements indicate σ8=0.85±0.03, implying a growth rate that is consistent with predictions from Planck temperature and polarization data and with General Relativity. When combining the results of SDSS BAO and RSD, Planck, Pantheon Type Ia supernovae (SNe Ia), and DES weak lensing and clustering measurements, all multiple-parameter extensions remain consistent with a ΛCDM model. Regardless of cosmological model, the precision on each of the three parameters, ωΛ, H0, and σ8, remains at roughly 1%, showing changes of less than 0.6% in the central values between models. In a model that allows for free curvature and a time-evolving equation of state for dark energy, the combined samples produce a constraint ωk=-0.0022±0.0022. The dark energy constraints lead to w0=-0.909±0.081 and wa=-0.49-0.30+0.35, corresponding to an equation of state of wp=-1.018±0.032 at a pivot redshift zp=0.29 and a Dark Energy Task Force Figure of Merit of 94. The inverse distance ladder measurement under this model yields H0=68.18±0.79 km s-1 Mpc-1, remaining in tension with several direct determination methods; the BAO data allow Hubble constant estimates that are robust against the assumption of the cosmological model. In addition, the BAO data allow estimates of H0 that are independent of the CMB data, with similar central values and precision under a ΛCDM model. Our most constraining combination of data gives the upper limit on the sum of neutrino masses at mν<0.115 eV (95% confidence). Finally, we consider the improvements in cosmology constraints over the last decade by comparing our results to a sample representative of the period 2000-2010. We compute the relative gain across the five dimensions spanned by w, ωk, mν, H0, and σ8 and find that the SDSS BAO and RSD data reduce the total posterior volume by a factor of 40 relative to the previous generation. Adding again the Planck, DES, and Pantheon SN Ia samples leads to an overall contraction in the five-dimensional posterior volume of 3 orders of magnitude.
575 citations
TL;DR: Monroe et al. as discussed by the authors used a laser-cooled and trapped atomic ions for the simulation of interacting quantum spin models, where effective spins are represented by appropriate internal energy levels within each ion, and the spins can be measured with near-perfect efficiency using state-dependent fluorescence techniques.
Abstract: Author(s): Monroe, C; Campbell, WC; Duan, LM; Gong, ZX; Gorshkov, AV; Hess, PW; Islam, R; Kim, K; Linke, NM; Pagano, G; Richerme, P; Senko, C; Yao, NY | Abstract: Laser-cooled and trapped atomic ions form an ideal standard for the simulation of interacting quantum spin models. Effective spins are represented by appropriate internal energy levels within each ion, and the spins can be measured with near-perfect efficiency using state-dependent fluorescence techniques. By applying optical fields that exert optical dipole forces on the ions, their Coulomb interaction can be modulated to produce long-range and tunable spin-spin interactions that can be reconfigured by shaping the spectrum and pattern of the laser fields in a prototypical example of a quantum simulator. Here the theoretical mapping of atomic ions to interacting spin systems, the preparation of complex equilibrium states, and the study of dynamical processes in these many-body interacting quantum systems are reviewed, and the use of this platform for optimization and other tasks is discussed. The use of such quantum simulators for studying spin models may inform our understanding of exotic quantum materials and shed light on the behavior of interacting quantum systems that cannot be modeled with conventional computers.
413 citations
TL;DR: In this article, a comprehensive understanding of the fundamentals of the microstructural evolution during FSW/P has been developed, including the mechanisms underlying the development of grain structures and textures, phases, phase transformations and precipitation.
390 citations
TL;DR: The space charge mechanism revealed by in situ magnetometry can be generalized to a broad range of transition metal compounds for which a large electron density of states is accessible, and provides pivotal guidance for creating advanced energy storage systems.
Abstract: In lithium-ion batteries (LIBs), many promising electrodes that are based on transition metal oxides exhibit anomalously high storage capacities beyond their theoretical values. Although this phenomenon has been widely reported, the underlying physicochemical mechanism in such materials remains elusive and is still a matter of debate. In this work, we use in situ magnetometry to demonstrate the existence of strong surface capacitance on metal nanoparticles, and to show that a large number of spin-polarized electrons can be stored in the already-reduced metallic nanoparticles (that are formed during discharge at low potentials in transition metal oxide LIBs), which is consistent with a space charge mechanism. Through quantification of the surface capacitance by the variation in magnetism, we further show that this charge capacity of the surface is the dominant source of the extra capacity in the Fe3O4/Li model system, and that it also exists in CoO, NiO, FeF2 and Fe2N systems. The space charge mechanism revealed by in situ magnetometry can therefore be generalized to a broad range of transition metal compounds for which a large electron density of states is accessible, and provides pivotal guidance for creating advanced energy storage systems.
390 citations
01 Mar 2021
TL;DR: In this paper, the authors identified five broad public health themes concerning the role of online social media platforms and COVID-19, focusing on: surveying public attitudes, identifying infodemics, assessing mental health, detecting or predicting COVID19 cases, analysing government responses to the pandemic, and evaluating quality of health information in prevention education videos.
Abstract: With the onset of the COVID-19 pandemic, social media has rapidly become a crucial communication tool for information generation, dissemination, and consumption. In this scoping review, we selected and examined peer-reviewed empirical studies relating to COVID-19 and social media during the first outbreak from November, 2019, to November, 2020. From an analysis of 81 studies, we identified five overarching public health themes concerning the role of online social media platforms and COVID-19. These themes focused on: surveying public attitudes, identifying infodemics, assessing mental health, detecting or predicting COVID-19 cases, analysing government responses to the pandemic, and evaluating quality of health information in prevention education videos. Furthermore, our Review emphasises the paucity of studies on the application of machine learning on data from COVID-19-related social media and a scarcity of studies documenting real-time surveillance that was developed with data from social media on COVID-19. For COVID-19, social media can have a crucial role in disseminating health information and tackling infodemics and misinformation.
377 citations
TL;DR: Four network statistics to identify bridge symptoms are developed: bridge strength, bridge betweenness, bridge closeness, and bridge expected influence, which are nonspecific to the type of network estimated, making them potentially useful in individual-level psychometric networks, group-level psychology networks, and networks outside the field of psychopathology such as social networks.
Abstract: Recently, researchers in clinical psychology have endeavored to create network models of the relationships between symptoms, both within and across mental disorders. Symptoms that connect two mental disorders are called "bridge symptoms." Unfortunately, no formal quantitative methods for identifying these bridge symptoms exist. Accordingly, we developed four network statistics to identify bridge symptoms: bridge strength, bridge betweenness, bridge closeness, and bridge expected influence. These statistics are nonspecific to the type of network estimated, making them potentially useful in individual-level psychometric networks, group-level psychometric networks, and networks outside the field of psychopathology such as social networks. We first tested the fidelity of our statistics in predicting bridge nodes in a series of simulations. Averaged across all conditions, the statistics achieved a sensitivity of 92.7% and a specificity of 84.9%. By simulating datasets of varying sample sizes, we tested the robustness of our statistics, confirming their suitability for network psychometrics. Furthermore, we simulated the contagion of one mental disorder to another, showing that deactivating bridge nodes prevents the spread of comorbidity (i.e., one disorder activating another). Eliminating nodes based on bridge statistics was more effective than eliminating nodes high on traditional centrality statistics in preventing comorbidity. Finally, we applied our algorithms to 18 group-level empirical comorbidity networks from published studies and discussed the implications of this analysis.
355 citations
National Technical University of Athens1, University of Manchester2, University of Chile3, Iowa State University4, University of Waterloo5, University of Duisburg-Essen6, University of Hong Kong7, University of Michigan8, Hydro-Québec9, Imperial College London10, Tufts University11, Arizona State University12
TL;DR: This paper based on an IEEE PES report summarizes the major results of the work of the Task Force and presents extended definitions and classification of power system stability.
Abstract: Since the publication of the original paper on power system stability definitions in 2004, the dynamic behavior of power systems has gradually changed due to the increasing penetration of converter interfaced generation technologies, loads, and transmission devices. In recognition of this change, a Task Force was established in 2016 to re-examine and extend, where appropriate, the classic definitions and classifications of the basic stability terms to incorporate the effects of fast-response power electronic devices. This paper based on an IEEE PES report summarizes the major results of the work of the Task Force and presents extended definitions and classification of power system stability.
University of Manchester1, City University of New York2, Istanbul Technical University3, New York University4, Heidelberg University5, Niels Bohr Institute6, University of Edinburgh7, University of Bologna8, Academy of Athens9, Sapienza University of Rome10, University of Naples Federico II11, Stanford University12, Institut d'Astrophysique de Paris13, University of Portsmouth14, Cardiff University15, Universidade Federal do Espírito Santo16, University of Michigan17, Asia Pacific Center for Theoretical Physics18, University of New Mexico19, University of Barcelona20, University of St. Thomas (Minnesota)21, Princeton University22, National Autonomous University of Mexico23, California Institute of Technology24, INAF25, University of Chicago26, Michigan Technological University27, Lawrence Berkeley National Laboratory28, University of Cambridge29, Imperial College London30, Ruhr University Bochum31, University of Waterloo32, University of Oxford33, Johns Hopkins University34, University of Pennsylvania35, University of California, Davis36, Birla Institute of Technology and Science37, RWTH Aachen University38, Université libre de Bruxelles39, University of Padua40, Indian Institute of Technology Kharagpur41, Spanish National Research Council42, University of North Carolina at Chapel Hill43, University of Arizona44, University of Oslo45, Jamia Millia Islamia46, University of Southern Denmark47, National Institute for Space Research48, Fermilab49, Presidency University, Kolkata50, Université Paris-Saclay51, University of Montpellier52, University of Szczecin53, Korea Astronomy and Space Science Institute54, University of California, Los Angeles55, University of Paris56, Leiden University57, Swarthmore College58, University of Sheffield59, University of Amsterdam60, United College, Winnipeg61, Liaoning Normal University62
TL;DR: In this article, the authors focus on the 4.4σ tension between the Planck estimate of the Hubble constant H0 and the SH0ES collaboration measurements and discuss how the next decade's experiments will be crucial.
Kazunori Akiyama1, Kazunori Akiyama2, Kazunori Akiyama3, Juan-Carlos Algaba4 +291 more•Institutions (72)
TL;DR: In this article, the authors present a list of authors who have contributed to the work of the authors of this paper: Akiyama, Kazunori; Algaba, Juan Carlos; Alberdi, Antxon; Alef, Walter; Anantua, Richard; Asada, Keiichi; Azulay, Rebecca; Baczko, Anne-Kathrin; Ball, David; Balokovic, Mislav; Barrett, John; Benson, Bradford A.; Bintley, Dan; Blackburn, Lindy; Blundell
Abstract: Full list of authors: Akiyama, Kazunori; Algaba, Juan Carlos; Alberdi, Antxon; Alef, Walter; Anantua, Richard; Asada, Keiichi; Azulay, Rebecca; Baczko, Anne-Kathrin; Ball, David; Balokovic, Mislav; Barrett, John; Benson, Bradford A.; Bintley, Dan; Blackburn, Lindy; Blundell, Raymond; Boland, Wilfred; Bouman, Katherine L.; Bower, Geoffrey C.; Boyce, Hope Bremer, Michael; Brinkerink, Christiaan D.; Brissenden, Roger; Britzen, Silke; Broderick, Avery E.; Broguiere, Dominique; Bronzwaer, Thomas; Byun, Do-Young; Carlstrom, John E.; Chael, Andrew; Chan, Chi-kwan; Chatterjee, Shami; Chatterjee, Koushik; Chen, Ming-Tang; Chen, Yongjun; Chesler, Paul M.; Cho, Ilje; Christian, Pierre; Conway, John E.; Cordes, James M.; Crawford, Thomas M.; Crew, Geoffrey B.; Cruz-Osorio, Alejandro; Cui, Yuzhu; Davelaar, Jordy; De Laurentis, Mariafelicia; Deane, Roger; Dempsey, Jessica; Desvignes, Gregory; Dexter, Jason; Doeleman, Sheperd S.; Eatough, Ralph P.; Falcke, Heino; Farah, Joseph; Fish, Vincent L.; Fomalont, Ed; Ford, H. Alyson; Fraga-Encinas, Raquel; Friberg, Per; Fromm, Christian M.; Fuentes, Antonio; Galison, Peter; Gammie, Charles F.; Garcia, Roberto; Gelles, Zachary; Gentaz, Olivier; Georgiev, Boris; Goddi, Ciriaco; Gold, Roman; Gomez, Jose L.; Gomez-Ruiz, Arturo I.; Gu, Minfeng; Gurwell, Mark; Hada, Kazuhiro; Haggard, Daryl; Hecht, Michael H.; Hesper, Ronald; Himwich, Elizabeth; Ho, Luis C.; Ho, Paul; Honma, Mareki; Huang, Chih-Wei L.; Huang, Lei; Hughes, David H.; Ikeda, Shiro; Inoue, Makoto; Issaoun, Sara; James, David J.; Jannuzi, Buell T.; Janssen, Michael; Jeter, Britton; Jiang, Wu; Jimenez-Rosales, Alejandra; Johnson, Michael D.; Jorstad, Svetlana; Jung, Taehyun; Karami, Mansour; Karuppusamy, Ramesh; Kawashima, Tomohisa; Keating, Garrett K.; Kettenis, Mark; Kim, Dong-Jin; Kim, Jae-Young; Kim, Jongsoo; Kim, Junhan; Kino, Motoki; Koay, Jun Yi; Kofuji, Yutaro; Koch, Patrick M.; Koyama, Shoko; Kramer, Michael; Kramer, Carsten; Krichbaum, Thomas P.; Kuo, Cheng-Yu; Lauer, Tod R.; Lee, Sang-Sung; Levis, Aviad; Li, Yan-Rong; Li, Zhiyuan; Lindqvist, Michael; Lico, Rocco; Lindahl, Greg; Liu, Jun; Liu, Kuo; Liuzzo, Elisabetta; Lo, Wen-Ping; Lobanov, Andrei P.; Loinard, Laurent; Lonsdale, Colin; Lu, Ru-Sen; MacDonald, Nicholas R.; Mao, Jirong; Marchili, Nicola; Markoff, Sera; Marrone, Daniel P.; Marscher, Alan P.; Marti-Vidal, Ivan; Matsushita, Satoki; Matthews, Lynn D.; Medeiros, Lia; Menten, Karl M.; Mizuno, Izumi; Mizuno, Yosuke; Moran, James M.; Moriyama, Kotaro; Moscibrodzka, Monika; Muller, Cornelia; Musoke, Gibwa; Mus Mejias, Alejandro; Michalik, Daniel; Nadolski, Andrew; Nagai, Hiroshi; Nagar, Neil M.; Nakamura, Masanori; Narayan, Ramesh; Narayanan, Gopal; Natarajan, Iniyan; Nathanail, Antonios; Neilsen, Joey; Neri, Roberto; Ni, Chunchong; Noutsos, Aristeidis; Nowak, Michael A.; Okino, Hiroki; Olivares, Hector; Ortiz-Leon, Gisela N.; Oyama, Tomoaki; Ozel, Feryal; Palumbo, Daniel C. M.; Park, Jongho; Patel, Nimesh; Pen, Ue-Li; Pesce, Dominic W.; Pietu, Vincent; Plambeck, Richard; PopStefanija, Aleksandar; Porth, Oliver; Potzl, Felix M.; Prather, Ben; Preciado-Lopez, Jorge A.; Psaltis, Dimitrios; Pu, Hung-Yi; Ramakrishnan, Venkatessh; Rao, Ramprasad; Rawlings, Mark G.; Raymond, Alexander W.; Rezzolla, Luciano; Ricarte, Angelo; Ripperda, Bart; Roelofs, Freek; Rogers, Alan; Ros, Eduardo; Rose, Mel; Roshanineshat, Arash; Rottmann, Helge; Roy, Alan L.; Ruszczyk, Chet; Rygl, Kazi L. J.; Sanchez, Salvador; Sanchez-Arguelles, David; Sasada, Mahito; Savolainen, Tuomas; Schloerb, F. Peter; Schuster, Karl-Friedrich; Shao, Lijing; Shen, Zhiqiang; Small, Des; Sohn, Bong Won; SooHoo, Jason; Sun, He; Tazaki, Fumie; Tetarenko, Alexandra J.; Tiede, Paul; Tilanus, Remo P. J.; Titus, Michael; Toma, Kenji; Torne, Pablo; Trent, Tyler; Traianou, Efthalia; Trippe, Sascha; van Bemmel, Ilse; van Langevelde, Huib Jan; van Rossum, Daniel R.; Wagner, Jan; Ward-Thompson, Derek; Wardle, John; Weintroub, Jonathan; Wex, Norbert; Wharton, Robert; Wielgus, Maciek; Wong, George N.; Wu, Qingwen; Yoon, Doosoo; Young, Andre; Young, Ken; Younsi, Ziri; Yuan, Feng; Yuan, Ye-Fei; Zensus, J. Anton; Zhao, Guang-Yao; Zhao, Shan-Shan; Event Horizon Telescope Collaboration.-- This is an open access article, original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
TL;DR: The pepper mild mottle virus (PMMoV) is determined to have a less variable RNA signal in PCS over a three month period for two WRRFs, regardless of environmental conditions, making PMMoV a potentially useful biomarker for normalization of SARS-CoV-2 signal.
TL;DR: In this paper, the authors identified five overarching public health themes concerning the role of online social platforms and COVID-19 pandemic, including surveying public attitudes, identifying infodemics, assessing mental health, detecting or predicting COVID19 cases, and evaluating quality of health information in prevention education videos.
Abstract: With the onset of COVID-19 pandemic, social media has rapidly become a crucial communication tool for information generation, dissemination, and consumption. In this scoping review, we selected and examined peer-reviewed empirical studies relating to COVID-19 and social media during the first outbreak starting in November 2019 until May 2020. From an analysis of 81 studies, we identified five overarching public health themes concerning the role of online social platforms and COVID-19. These themes focused on: (i) surveying public attitudes, (ii) identifying infodemics, (iii) assessing mental health, (iv) detecting or predicting COVID-19 cases, (v) analyzing government responses to the pandemic, and (vi) evaluating quality of health information in prevention education videos. Furthermore, our review highlights the paucity of studies on the application of machine learning on social media data related to COVID-19 and a lack of studies documenting real-time surveillance developed with social media data on COVID-19. For COVID-19, social media can play a crucial role in disseminating health information as well as tackling infodemics and misinformation.
Kazunori Akiyama1, Kazunori Akiyama2, Kazunori Akiyama3, Juan-Carlos Algaba4 +292 more•Institutions (73)
TL;DR: In this article, the authors present a list of the authors who contributed to the development of this work, including: Akiyama, Kazunori; Algaba, Juan Carlos; Alberdi, Antxon; Anantua, Richard; Asada, Keiichi; Azulay, Rebecca; Baczko, Anne-Kathrin; Ball, David; Balokovic, Mislav; Barrett, John; Benson, Bradford A; Bintley, Dan; Bunderwood, Nissim; Bower, Geoffrey C;
Abstract: Full list of authors: Akiyama, Kazunori; Algaba, Juan Carlos; Alberdi, Antxon; Alef, Walter; Anantua, Richard; Asada, Keiichi; Azulay, Rebecca; Baczko, Anne-Kathrin; Ball, David; Balokovic, Mislav; Barrett, John; Benson, Bradford A.; Bintley, Dan; Blackburn, Lindy; Blundell, Raymond; Boland, Wilfred; Bouman, Katherine L.; Bower, Geoffrey C.; Boyce, Hope Bremer, Michael; Brinkerink, Christiaan D.; Brissenden, Roger; Britzen, Silke; Broderick, Avery E.; Broguiere, Dominique; Bronzwaer, Thomas; Byun, Do-Young; Carlstrom, John E.; Chael, Andrew; Chan, Chi-kwan; Chatterjee, Shami; Chatterjee, Koushik; Chen, Ming-Tang; Chen, Yongjun; Chesler, Paul M.; Cho, Ilje; Christian, Pierre; Conway, John E.; Cordes, James M.; Crawford, Thomas M.; Crew, Geoffrey B.; Cruz-Osorio, Alejandro; Cui, Yuzhu; Davelaar, Jordy; De Laurentis, Mariafelicia; Deane, Roger; Dempsey, Jessica; Desvignes, Gregory; Dexter, Jason; Doeleman, Sheperd S.; Eatough, Ralph P.; Falcke, Heino; Farah, Joseph; Fish, Vincent L.; Fomalont, Ed; Ford, H. Alyson; Fraga-Encinas, Raquel; Freeman, William T.; Friberg, Per; Fromm, Christian M.; Fuentes, Antonio; Galison, Peter; Gammie, Charles F.; Garcia, Roberto; Gentaz, Olivier; Georgiev, Boris; Goddi, Ciriaco; Gold, Roman; Gomez, Jose L.; Gomez-Ruiz, Arturo I.; Gu, Minfeng; Gurwell, Mark; Hada, Kazuhiro; Haggard, Daryl; Hecht, Michael H.; Hesper, Ronald; Ho, Luis C.; Ho, Paul; Honma, Mareki; Huang, Chih-Wei L.; Huang, Lei; Hughes, David H.; Ikeda, Shiro; Inoue, Makoto; Issaoun, Sara; James, David J.; Jannuzi, Buell T.; Janssen, Michael; Jeter, Britton; Jiang, Wu; Jimenez-Rosales, Alejandra; Johnson, Michael D.; Jorstad, Svetlana; Jung, Taehyun; Karami, Mansour; Karuppusamy, Ramesh; Kawashima, Tomohisa; Keating, Garrett K.; Kettenis, Mark; Kim, Dong-Jin; Kim, Jae-Young; Kim, Jongsoo; Kim, Junhan; Kino, Motoki; Koay, Jun Yi; Kofuji, Yutaro; Koch, Patrick M.; Koyama, Shoko; Kramer, Michael; Kramer, Carsten; Krichbaum, Thomas P.; Kuo, Cheng-Yu; Lauer, Tod R.; Lee, Sang-Sung; Levis, Aviad; Li, Yan-Rong; Li, Zhiyuan; Lindqvist, Michael; Lico, Rocco; Lindahl, Greg; Liu, Jun; Liu, Kuo; Liuzzo, Elisabetta; Lo, Wen-Ping; Lobanov, Andrei P.; Loinard, Laurent; Lonsdale, Colin; Lu, Ru-Sen; MacDonald, Nicholas R.; Mao, Jirong; Marchili, Nicola; Markoff, Sera; Marrone, Daniel P.; Marscher, Alan P.; Marti-Vidal, Ivan; Matsushita, Satoki; Matthews, Lynn D.; Medeiros, Lia; Menten, Karl M.; Mizuno, Izumi; Mizuno, Yosuke; Moran, James M.; Moriyama, Kotaro; Moscibrodzka, Monika; Muller, Cornelia; Musoke, Gibwa; Mejias, Alejandro Mus; Michalik, Daniel; Nadolski, Andrew; Nagai, Hiroshi; Nagar, Neil M.; Nakamura, Masanori; Narayan, Ramesh; Narayanan, Gopal; Natarajan, Iniyan; Nathanail, Antonios; Neilsen, Joey; Neri, Roberto; Ni, Chunchong; Noutsos, Aristeidis; Nowak, Michael A.; Okino, Hiroki; Olivares, Hector; Ortiz-Leon, Gisela N.; Oyama, Tomoaki; Ozel, Feryal; Palumbo, Daniel C. M.; Park, Jongho; Patel, Nimesh; Pen, Ue-Li; Pesce, Dominic W.; Pietu, Vincent; Plambeck, Richard; PopStefanija, Aleksandar; Porth, Oliver; Potzl, Felix M.; Prather, Ben; Preciado-Lopez, Jorge A.; Psaltis, Dimitrios; Pu, Hung-Yi; Ramakrishnan, Venkatessh; Rao, Ramprasad; Rawlings, Mark G.; Raymond, Alexander W.; Rezzolla, Luciano; Ricarte, Angelo; Ripperda, Bart; Roelofs, Freek; Rogers, Alan; Ros, Eduardo; Rose, Mel; Roshanineshat, Arash; Rottmann, Helge; Roy, Alan L.; Ruszczyk, Chet; Rygl, Kazi L. J.; Sanchez, Salvador; Sanchez-Arguelles, David; Sasada, Mahito; Savolainen, Tuomas; Schloerb, F. Peter; Schuster, Karl-Friedrich; Shao, Lijing; Shen, Zhiqiang; Small, Des; Sohn, Bong Won; SooHoo, Jason; Sun, He; Tazaki, Fumie; Tetarenko, Alexandra J.; Tiede, Paul; Tilanus, Remo P. J.; Titus, Michael; Toma, Kenji; Torne, Pablo; Trent, Tyler; Traianou, Efthalia; Trippe, Sascha; van Bemmel, Ilse; van Langevelde, Huib Jan; van Rossum, Daniel R.; Wagner, Jan; Ward-Thompson, Derek; Wardle, John; Weintroub, Jonathan; Wex, Norbert; Wharton, Robert; Wielgus, Maciek; Wong, George N.; Wu, Qingwen; Yoon, Doosoo; Young, Andre; Young, Ken; Younsi, Ziri; Yuan, Feng; Yuan, Ye-Fei; Zensus, J. Anton; Zhao, Guang-Yao; Zhao, Shan-Shan; Event Horizon Telescope Collaboration.-- This is an open access article, original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
TL;DR: In this article, the authors presented a novel modified battery module configuration employing two-layer nanoparticle enhanced phase change materials (nePCM), and compared the cooling performance of proposed battery thermal management systems (BTMS) at an ambient temperature ranging from 30°C to 40°C with external natural convection conditions.
TL;DR: In this paper, a terahertz quantum cascade laser (QCL) with a maximum operating temperature of 250 k was developed, which enables real-time imaging with a room-temperature THz camera, as well as fast spectral measurements using a room temperature detector.
Abstract: Terahertz (THz) frequencies remain among the least utilized in the electromagnetic spectrum, largely due to the lack of powerful and compact sources. The invention of THz quantum cascade lasers (QCLs) was a major breakthrough to bridge the so-called ‘THz gap’ between semiconductor electronic and photonic sources. However, their demanding cooling requirement has confined the technology to a laboratory environment. A portable and high-power THz laser system will have a qualitative impact on applications in medical imaging, communications, quality control, security and biochemistry. Here, by adopting a design strategy that achieves a clean three-level system, we have developed THz QCLs (at ~4 THz) with a maximum operating temperature of 250 K. The high operating temperature enables portable THz systems to perform real-time imaging with a room-temperature THz camera, as well as fast spectral measurements with a room-temperature detector. GaAs-based terahertz quantum cascade lasers emitting around 4 THz are demonstrated up to 250 K without a magnetic field. To elevate the operation temperature, carrier leakage channels are reduced by carefully designing the quantum well structures.
TL;DR: Stochastic optimization techniques are applied to transform the original stochastic problem into a deterministic optimization problem, and an energy efficient dynamic offloading algorithm called EEDOA is proposed, which can approximate the minimal transmission energy consumption while still bounding the queue length.
Abstract: With proliferation of computation-intensive Internet of Things (IoT) applications, the limited capacity of end devices can deteriorate service performance. To address this issue, computation tasks can be offloaded to the Mobile Edge Computing (MEC) for processing. However, it consumes considerable energy to transmit and process these tasks. In this paper, we study the energy efficient task offloading in MEC. Specifically, we formulate it as a stochastic optimization problem, with the objective of minimizing the energy consumption of task offloading while guaranteeing the average queue length. Solving this offloading optimization problem faces many technical challenges due to the uncertainty and dynamics of wireless channel state and task arrival process, and the large scale of solution space. To tackle these challenges, we apply stochastic optimization techniques to transform the original stochastic problem into a deterministic optimization problem, and propose an energy efficient dynamic offloading algorithm called EEDOA. EEDOA can be implemented in an online manner to make the task offloading decisions with polynomial time complexity. Theoretical analysis is provided to demonstrate that EEDOA can approximate the minimal transmission energy consumption while still bounding the queue length. Experiment results are presented which show the EEDOA’s effectiveness.
TL;DR: This article reformulates the microservice coordination problem using Markov decision process framework and then proposes a reinforcement learning-based online micro service coordination algorithm to learn the optimal strategy, which proves that the offline algorithm can find the optimal solution while the online algorithm can achieve near-optimal performance.
Abstract: As an emerging service architecture, microservice enables decomposition of a monolithic web service into a set of independent lightweight services which can be executed independently. With mobile edge computing, microservices can be further deployed in edge clouds dynamically, launched quickly, and migrated across edge clouds easily, providing better services for users in proximity. However, the user mobility can result in frequent switch of nearby edge clouds, which increases the service delay when users move away from their serving edge clouds. To address this issue, this article investigates microservice coordination among edge clouds to enable seamless and real-time responses to service requests from mobile users. The objective of this work is to devise the optimal microservice coordination scheme which can reduce the overall service delay with low costs. To this end, we first propose a dynamic programming-based offline microservice coordination algorithm, that can achieve the globally optimal performance. However, the offline algorithm heavily relies on the availability of the prior information such as computation request arrivals, time-varying channel conditions and edge cloud's computation capabilities required, which is hard to be obtained. Therefore, we reformulate the microservice coordination problem using Markov decision process framework and then propose a reinforcement learning-based online microservice coordination algorithm to learn the optimal strategy. Theoretical analysis proves that the offline algorithm can find the optimal solution while the online algorithm can achieve near-optimal performance. Furthermore, based on two real-world datasets, i.e., the Telecom's base station dataset and Taxi Track dataset from Shanghai, experiments are conducted. The experimental results demonstrate that the proposed online algorithm outperforms existing algorithms in terms of service delay and migration costs, and the achieved performance is close to the optimal performance obtained by the offline algorithm.
01 Aug 2021
TL;DR: The fundamental catalytic mechanisms of Fe3O4 nanozymes and recent advances in tumor catalytic therapy are reviewed, and the importance of surface modification is discussed, to provide an outlook on the improvement of nanozyme‐based antitumor activity.
TL;DR: From the simulation results, the MADDPG-based method can converge within 200 training episodes, comparable to the single-agent DDPG (SADDPG)-based one, and can achieve higher delay/QoS satisfaction ratios than the SADDPg-based and random schemes.
Abstract: In this paper, we investigate multi-dimensional resource management for unmanned aerial vehicles (UAVs) assisted vehicular networks. To efficiently provide on-demand resource access, the macro eNodeB and UAV, both mounted with multi-access edge computing (MEC) servers, cooperatively make association decisions and allocate proper amounts of resources to vehicles. Since there is no central controller, we formulate the resource allocation at the MEC servers as a distributive optimization problem to maximize the number of offloaded tasks while satisfying their heterogeneous quality-of-service (QoS) requirements, and then solve it with a multi-agent deep deterministic policy gradient (MADDPG)-based method. Through centrally training the MADDPG model offline, the MEC servers, acting as learning agents, then can rapidly make vehicle association and resource allocation decisions during the online execution stage. From our simulation results, the MADDPG-based method can converge within 200 training episodes, comparable to the single-agent DDPG (SADDPG)-based one. Moreover, the proposed MADDPG-based resource management scheme can achieve higher delay/QoS satisfaction ratios than the SADDPG-based and random schemes.
11 Jul 2021
TL;DR: This work introduces an efficient topic-aware query and balanced margin sampling technique, called TAS-Balanced, and produces the first dense retriever that outperforms every other method on recall at any cutoff on TREC-DL and allows more resource intensive re-ranking models to operate on fewer passages to improve results further.
Abstract: A vital step towards the widespread adoption of neural retrieval models is their resource efficiency throughout the training, indexing and query workflows. The neural IR community made great advancements in training effective dual-encoder dense retrieval (DR) models recently. A dense text retrieval model uses a single vector representation per query and passage to score a match, which enables low-latency first-stage retrieval with a nearest neighbor search. Increasingly common, training approaches require enormous compute power, as they either conduct negative passage sampling out of a continuously updating refreshing index or require very large batch sizes. Instead of relying on more compute capability, we introduce an efficient topic-aware query and balanced margin sampling technique, called TAS-Balanced. We cluster queries once before training and sample queries out of a cluster per batch. We train our lightweight 6-layer DR model with a novel dual-teacher supervision that combines pairwise and in-batch negative teachers. Our method is trainable on a single consumer-grade GPU in under 48 hours. We show that our TAS-Balanced training method achieves state-of-the-art low-latency (64ms per query) results on two TREC Deep Learning Track query sets. Evaluated on NDCG@10, we outperform BM25 by 44%, a plainly trained DR by 19%, docT5query by 11%, and the previous best DR model by 5%. Additionally, TAS-Balanced produces the first dense retriever that outperforms every other method on recall at any cutoff on TREC-DL and allows more resource intensive re-ranking models to operate on fewer passages to improve results further.
TL;DR: A comprehensive review of high-entropy materials in the energy field, including alloys, oxides and other entropy-stabilized compounds and composites, in various energy storage and conversion systems.
Abstract: The essential demand for functional materials enabling the realization of new energy technologies has triggered tremendous efforts in scientific and industrial research in recent years. Recently, high-entropy materials, with their unique structural characteristics, tailorable chemical composition and correspondingly tunable functional properties, have drawn increasing interest in the fields of environmental science and renewable energy technology. Herein, we provide a comprehensive review of this new class of materials in the energy field. We begin with discussions on the latest reports on the applications of high-entropy materials, including alloys, oxides and other entropy-stabilized compounds and composites, in various energy storage and conversion systems. In addition, we describe effective strategies for rationally designing high-entropy materials from computational techniques and experimental aspects. Based on this overview, we subsequently present the fundamental insights and give a summary of their potential advantages and remaining challenges, which will ideally provide researchers with some general guides and principles for the investigation and development of advanced high-entropy materials.
University of Washington1, University of Sydney2, Monash University3, University of Waterloo4, University of New South Wales5, Children's Hospital at Westmead6, Loughborough University7, University of Tsukuba8, University of London9, Shandong University10, University of Copenhagen11, ETH Zurich12, Arizona State University13
TL;DR: The authors in this paper suggest that without investment in research and risk management actions, heat-related morbidity and mortality are likely to increase, with greater risk associated with higher degrees of global warming.
University of Manchester1, City University of New York2, Istanbul Technical University3, New York University4, Heidelberg University5, Niels Bohr Institute6, University of Edinburgh7, University of Bologna8, Academy of Athens9, Sapienza University of Rome10, University of Naples Federico II11, Stanford University12, Institut d'Astrophysique de Paris13, University of Portsmouth14, Cardiff University15, Universidade Federal do Espírito Santo16, University of Michigan17, Asia Pacific Center for Theoretical Physics18, University of New Mexico19, University of Barcelona20, Centre national de la recherche scientifique21, Princeton University22, National Autonomous University of Mexico23, Jet Propulsion Laboratory24, INAF25, University of Chicago26, Michigan Technological University27, University of Cambridge28, Imperial College London29, Ruhr University Bochum30, University of Waterloo31, Johns Hopkins University32, University of Pennsylvania33, University of California, Davis34, Birla Institute of Technology and Science35, RWTH Aachen University36, Université libre de Bruxelles37, University of Padua38, Indian Institute of Technology Kharagpur39, Spanish National Research Council40, University of North Carolina at Chapel Hill41, University of Arizona42, University of Oslo43, Jamia Millia Islamia44, National Institute for Space Research45, Fermilab46, Presidency University, Kolkata47, Université Paris-Saclay48, University of Montpellier49, University of Szczecin50, Korea Astronomy and Space Science Institute51, University of California, Los Angeles52, Leiden University53, University of Southern Denmark54, Swarthmore College55, University of Sheffield56, University of Amsterdam57, United College, Winnipeg58, Liaoning Normal University59
TL;DR: In this article, the authors focus on the tension between Planck data and weak lensing measurements and redshift surveys, and discuss the importance of trying to fit multiple cosmological datasets with complete physical models, rather than fitting individual datasets with a few handpicked theoretical parameters.
17 May 2021
TL;DR: This thesis addresses variants of the SVNE problem with different bandwidth and reliability requirements for transport networks through extensive simulations and proposes a connectivity-aware VNE approach that ensures VN connectivity without bandwidth guarantee in the face of multiple link failures.
Abstract: Network Virtualization (NV) is an enabling technology for the future Internet and next-generation communication networks. A fundamental problem in NV is to map the virtual nodes and virtual links of a VN to physical nodes and paths, respectively, known as the Virtual Network Embedding (VNE) problem. A VNE that can survive physical resource failures is known as the survivable VNE (SVNE) problem, and has received significant attention recently. In this thesis, we address variants of the SVNE problem with different bandwidth and reliability requirements for transport networks. Specifically, the thesis includes four main contributions. First, a connectivity-aware VNE approach that ensures VN connectivity without bandwidth guarantee in the face of multiple link failures. Second, a joint spare capacity allocation and VNE scheme that provides bandwidth guarantee against link failures by augmenting VNs with necessary spare capacity. Third, a generalized recovery mechanism to re-embed the VNs that are impacted by a physical node failure. Fourth, a reliable VNE scheme with dedicated protection that allows tuning of available bandwidth of a VN during a physical link failure. We show the effectiveness of the proposed SVNE schemes through extensive simulations.
TL;DR: This paper proposes the first certificateless public verification scheme against procrastinating auditors (CPVPA) by using blockchain technology, and presents rigorous security proofs to demonstrate the security of CPVPA, and conducts a comprehensive performance evaluation to show that CPVpa is efficient.
Abstract: The deployment of cloud storage services has significant benefits in managing data for users. However, it also causes many security concerns, and one of them is data integrity. Public verification techniques can enable a user to employ a third-party auditor to verify the data integrity on behalf of her/him, whereas existing public verification schemes are vulnerable to procrastinating auditors who may not perform verifications on time. Furthermore, most of public verification schemes are constructed on the public key infrastructure (PKI), and thereby suffer from certificate management problem. In this paper, we propose a c ertificateless p ublic v erification scheme against p rocrastinating a uditors (CPVPA) by using blockchain technology . The key idea is to require auditors to record each verification result into a transaction on a blockchain. Because transactions on the blockchain are time-sensitive, the verification can be time-stamped after the transaction is recorded into the blockchain, which enables users to check whether auditors perform the verifications at the prescribed time. Moreover, CPVPA is built on certificateless cryptography, and is free from the certificate management problem. We present rigorous security proofs to demonstrate the security of CPVPA, and conduct a comprehensive performance evaluation to show that CPVPA is efficient.
TL;DR: In this paper, a review of deep learning based systems for the detection of the new coronavirus (COVID-19) outbreak has been presented, which can be potentially further utilized to combat the outbreak.
Abstract: Novel coronavirus (COVID-19) outbreak, has raised a calamitous situation all over the world and has become one of the most acute and severe ailments in the past hundred years. The prevalence rate of COVID-19 is rapidly rising every day throughout the globe. Although no vaccines for this pandemic have been discovered yet, deep learning techniques proved themselves to be a powerful tool in the arsenal used by clinicians for the automatic diagnosis of COVID-19. This paper aims to overview the recently developed systems based on deep learning techniques using different medical imaging modalities like Computer Tomography (CT) and X-ray. This review specifically discusses the systems developed for COVID-19 diagnosis using deep learning techniques and provides insights on well-known data sets used to train these networks. It also highlights the data partitioning techniques and various performance measures developed by researchers in this field. A taxonomy is drawn to categorize the recent works for proper insight. Finally, we conclude by addressing the challenges associated with the use of deep learning methods for COVID-19 detection and probable future trends in this research area. The aim of this paper is to facilitate experts (medical or otherwise) and technicians in understanding the ways deep learning techniques are used in this regard and how they can be potentially further utilized to combat the outbreak of COVID-19.