86,904 research outputs found
A Tripartite Post-Recession Rebalancing
No full textIn this latest Advance & Rutgers Report, entitled “A Tripartite Post-Recession Rebalancing,” Dean James W. Hughes and Professor Joseph J. Seneca deliver an incisive assessment of the current market conditions and obstacles in the path of our economic recovery. They offer a statistical cautionary tale that the private and public sector need to hear and acknowledge in order for the economy to make continued progress.This report was published as Issue Paper Number 7, November 2011, in Advance & Rutgers Report
Developing Magnesium Alloys with a Combination of Strength and Ductility Based on Friction Stir-Based Technologies
No full textSome of the key data for the thesis "Developing Magnesium Alloys with a Combination of Strength and Ductility Based on Friction Stir-Based Technologies", including SEM/EDS/EBSD, and TEM data. They are stored in the .oipx format which can be opened by Aztec, .cpr which can be opened by AztecCrystal, and .csv and . tiff as spreadsheets and images.
Part of the data is associated with the following publications:
[1] X. Zhao, X. Zeng, L. Yuan, J. Gandra, Q. Hayat, M. Bai, W.M. Rainforth, D. Guan, A novel approach for producing Mg-3Al-1Zn-0.2 Mn alloy wire with a promising combination of strength and ductility using CoreFlowTM, Scripta Materialia 227 (2023) 115301.
[2] X. Zhao, Y. Xie, J. Gandra, M. Murphy, W.M. Rainforth, D. Guan, A Succinct Method to Recycle WE43 Mg Alloys—From Wasted Chips to Consolidated Billets, TMS Annual Meeting & Exhibition, Springer, (2024), 151-153.
[3] X. Zhao, D. Olden, B. Williams, A. Pariyar, D. Zhang, M. Murphy, P. Reed, P. Allison, B. Jordon, J. Qi, W. M. Rainforth, D. Guan, Grain growth stagnation at 525° C by nanoparticles in a solid-state additively manufactured Mg-4Y-3RE alloy, Journal of Magnesium and Alloys (2024), 4976-4987.
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The Receding Metropolitan Perimeter: A New Postsuburban Demographic Normal
No full textThe report traces population changes for two time periods: 1950 to 1980, reflecting the nation’s unprecedented postwar suburbanization, and 2010 to 2013, for the recovery period to date from aftershocks of the Great 2007-2009 Recession. The decades between the two time periods analyzed – the 1980s, 1990s, and 2000s – are also examined for the influence of overall regional growth, age-structure variations and immigration levels on population change.
Twenty-seven of the suburban-ring counties in the four states witnessed explosive growth in the 30-year period from 1950 to 1980, gaining more than 5.3 million residents, and nearly doubling their population. By contrast, the regional core of eight urban counties in New York and New Jersey contracted sharply during the same period, losing nearly a million people.
Then, during the 2010–2013 period, the trend reversed: the regional core grew at a rate more than double that of the suburban ring, adding 85,284 persons per year. The regional core accounted for most of the total population growth, a phenomenon unparalleled since World War II. All of the suburban counties with population losses were on the metropolitan outer ring with the exception of Monmouth County, which suffered impacts from Superstorm Sandy.
The authors insistently caution that this shift in population growth is not necessarily a long-term change since the latest time period is so limited. However, the data suggest a change of the crest of the wave nature indicating that the multidecade pattern of further growth on the perimeter of the region out has shifted.
The report also discusses the influence of young adults’ locational preferences for urban lifestyle and workplace choices post-2000 as one contributing factor to these shifting population patterns
Skyrmion-skyrmion and skyrmion-edge repulsions in skyrmion-based racetrack memory
No full textMagnetic skyrmions are promising for building next-generation magnetic memories and spintronic devices due to their stability, small size and the extremely low currents needed to move them. In particular, skyrmion-based racetrack memory is attractive for information technology, where skyrmions are used to store information as data bits instead of traditional domain walls. Here we numerically demonstrate the impacts of skyrmion-skyrmion and skyrmion-edge repulsions on the feasibility of skyrmion-based racetrack memory. The reliable and practicable spacing between consecutive skyrmionic bits on the racetrack as well as the ability to adjust it are investigated. Clogging of skyrmionic bits is found at the end of the racetrack, leading to the reduction of skyrmion size. Further, we demonstrate an effective and simple method to avoid the clogging of skyrmionic bits, which ensures the elimination of skyrmionic bits beyond the reading element. Our results give guidance for the design and development of future skyrmion-based racetrack memory
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Song, Y., Geng, K., Zhang, B., Hyde, K.D., Zhao, W.-S., Wei, J.-G., Kang, J.-C. & Wang, Y. (2013) Two new species of Pestalotiopsis from Southern China. Phytotaxa 126 (1), 22–30.<br />
No full textSONG, YU, GENG, KUN, ZHANG, BIN, HYDE, KEVIN D., ZHAO, WEN-SHENG, WEI, JI-GUANG, KANG, JI-CHUAN, WANG, YONG (2013): Song, Y., Geng, K., Zhang, B., Hyde, K.D., Zhao, W.-S., Wei, J.-G., Kang, J.-C. & Wang, Y. (2013) Two new species of Pestalotiopsis from Southern China. Phytotaxa 126 (1), 22–30.<br>. Phytotaxa 135 (1): 64, DOI: 10.11646/phytotaxa.135.1.8, URL: http://dx.doi.org/10.11646/phytotaxa.135.1.
Solar Power in the Garden State
No full textThis special issue on energy and solar power in New Jersey was made possible because of the extensive portfolio of research centers and institutes at the Edward J. Bloustein School of Planning and Public Policy. Dr. Frank A. Felder, an Associate Research Professor, has been director of the School’s Center for Energy, Economic & Environmental Policy (CEEEP) since 2006. Frank is a nuclear engineer with a PhD degree from MIT, and he, along with his CEEEP colleague, Shankar N. Chandramowli, coauthored the main article in this issue of the Advance & Rutgers Report. CEEEP has worked extensively with the New Jersey Board of Public Utilities on projects, including New Jersey’s current Energy Master Plan.Shining Brightly: Bloustein's Centers of Excellence / by James W. Hughes and Joseph S. Seneca -- Solar Power in the Garden States / by Shankar N. Chandramowli and Frank A. Felder.Guest contributors include Shankar N. Chandramowli and Frank A. Felder, PhD, Director—Center for Energy, Economic and Environmental Policy at the Edward J. Bloustein School of Planning and Public PolicyReports published as Issue Paper Number 5, May 2011, in Advance & Rutgers Report, Special Issue
Automaattinen hermoverkko-oppiminen ihmisen käyttäytymisen ymmärtämiseksi
No full textAbstractUnderstanding human behavior is one of the most pivotal steps toward real-world Artificial Intelligence (AI) or even Artificial general intelligence (AGI). However, this task is challenging as human social attributes make human beings unique, leading to various and complicated behaviors. Moreover, real-life behavior data are normally high-dimensional with dynamic changes or even non-Euclidean structures, involving multiple modalities. Currently, one of the first alternatives to addressing these challenges is using deep neural networks or deep learning, which has brought revolutionary changes in data computation and computer sciences. Nevertheless, expert knowledge of both neural architecture design and human behavior analysis is expected more than ever before in this interdisciplinary research field. All these issues spur the current deep learning studies towards automatic deep neural network learning, which could automatically sketch a neural architecture for a given behavior analysis task. In line with this topic, this thesis explores the automatic neural network learning approach for human behavior understanding from the most representative behaviors, including human facial expression and actions, step by step. First, manually designed computational models are proposed for human facial expression and actions with dynamic information and graph structures. Based on this, to free humans from the exhausting process, more advanced methods, i.e., automatic neural network learning, are presented. Extensive experiments on benchmark facial expression datasets and action recognition datasets are conducted and comparison results prove the effectiveness of the proposed methods.Original papersOriginal papers are not included in the electronic version of the dissertation.Peng, W., Hong, X., Xu, Y., & Zhao, G. (2019). A boost in revealing subtle facial expressions: A consolidated eulerian framework. 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019), 1–5. https://doi.org/10.1109/FG.2019.8756541Self-archived versionPeng, W., Hong, X., Zhao, G., & Cambria, E. (2021). Adaptive modality distillation for separable multimodal sentiment analysis. IEEE Intelligent Systems, 36(3), 82–89. https://doi.org/10.1109/MIS.2021.3057757Self-archived versionXia, Z., Peng, W., Khor, H.-Q., Feng, X., & Zhao, G. (2020). Revealing the invisible with model and data shrinking for composite-database micro-expression recognition. IEEE Transactions on Image Processing, 29, 8590–8605. https://doi.org/10.1109/TIP.2020.3018222Self-archived versionPeng, W., Hong, X., & Zhao, G. (2019). Video action recognition via neural architecture searching. 2019 IEEE International Conference on Image Processing (ICIP), 11–15. https://doi.org/10.1109/ICIP.2019.8802919Self-archived versionPeng, W., Shi, J., & Zhao, G. (2021). Spatial temporal graph deconvolutional network for skeleton-based human action recognition. IEEE Signal Processing Letters, 28, 244–248. https://doi.org/10.1109/LSP.2021.3049691Self-archived versionPeng, W., Hong, X., & Zhao, G. (2021). Tripool: Graph triplet pooling for 3D skeleton-based action recognition. Pattern Recognition, 115, 107921. https://doi.org/10.1016/j.patcog.2021.107921Self-archived versionPeng, W., Shi, J., Varanka, T., & Zhao, G. (2021). Rethinking the ST-GCNs for 3D skeleton-based human action recognition. Neurocomputing, 454, 45–53. https://doi.org/10.1016/j.neucom.2021.05.004Self-archived versionPeng, W., Varanka, T., Mostafa, A., Shi, H., & Zhao, G. (2021). Hyperbolic deep neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/TPAMI.2021.3136921Self-archived versionPeng, W., Hong, X., Chen, H., & Zhao, G. (2020). Learning graph convolutional network for skeleton-based human action recognition by neural searching. Proceedings of the AAAI Conference on Artificial Intelligence, 34(03), 2669–2676. https://doi.org/10.1609/aaai.v34i03.5652Self-archived versionPeng, W., Shi, J., Xia, Z., & Zhao, G. (2020). Mix dimension in poincaré geometry for 3d skeleton-based action recognition. Proceedings of the 28th ACM International Conference on Multimedia, 1432–1440. https://doi.org/10.1145/3394171.3413910Self-archived versionTiivistelmäIhmisten käyttäytymisen ymmärtäminen on yksi keskeisistä askeleista kohti todellista tekoälyä (AI) tai jopa yleistä tekoälyä (AGI). Tämä tehtävä on kuitenkin haastava, sillä ihmisen sosiaaliset ominaisuudet tekevät ihmisistä ainutlaatuisia, mikä johtaa erilaisiin ja monimutkaisiin käyttäytymismalleihin. Lisäksi tosielämän käyttäytymisdata on yleensä korkeaulotteinen, ja siinä on dynaamisia muutoksia tai jopa ei-euklidisia rakenteita, joihin liittyy useita modaliteetteja. Tällä hetkellä yksi ensimmäisistä vaihtoehdoista haasteisiin vastaamiseksi on syvän neuroverkon tai syväoppimisen käyttö, joka on tuonut mukanaan Vallankumoukselliset muutokset tietojen laskennassa ja tietojenkäsittelytieteissä. Asiantuntijatietoa sekä hermoarkkitehtuurin suunnittelusta että ihmisen käyttäytymisen analysoinnista odotetaan kuitenkin enemmän kuin koskaan aiemmin tällä tieteidenvälisellä tutkimusalueella. Kaikki nämä kysymykset kannustavat nykyisiä syväoppimistutkimuksia kohti automaattista syvän neuroverkon oppimista, joka voisi automaattisesti luonnostella hermoarkkitehtuurin tietylle käyttäytymisanalyysille Tämän aiheen mukaisesti opinnäytetyössä tutkitaan vaihe vaiheelta automaattista neuroverk- kooppimisen lähestymistapaa ihmisen käyttäytymisen ymmärtämiseen edustavimmista käyttäytymismalleista, mukaan lukien ihmisen ilmeet ja toiminnot. Ensin ehdotetaan manuaalisesti suunniteltuja laskennallisia malleja ihmisen ilmeille ja toiminnalle dynaamisilla tiedoilla ja graafirakenteilla, joiden pohjalta ihmisen uuvuttavasta edistymisestä vapauttamiseksi esitetään edistyneempiä menetelmiä, ie, automaattinen hermoverkkooppiminen. Kattavia kokeita benchmark ME-tietosarjoista ja toiminnantunnistustietosarjoista tehdään ja vertailutulokset osoittavat ehdotettujen menetelmien tehokkuuden.OsajulkaisutOsajulkaisut eivät sisälly väitöskirjan elektroniseen versioon.Peng, W., Hong, X., Xu, Y., & Zhao, G. (2019). A boost in revealing subtle facial expressions: A consolidated eulerian framework. 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019), 1–5. https://doi.org/10.1109/FG.2019.8756541Rinnakkaistallennettu versioPeng, W., Hong, X., Zhao, G., & Cambria, E. (2021). Adaptive modality distillation for separable multimodal sentiment analysis. IEEE Intelligent Systems, 36(3), 82–89. https://doi.org/10.1109/MIS.2021.3057757Rinnakkaistallennettu versioXia, Z., Peng, W., Khor, H.-Q., Feng, X., & Zhao, G. (2020). Revealing the invisible with model and data shrinking for composite-database micro-expression recognition. IEEE Transactions on Image Processing, 29, 8590–8605. https://doi.org/10.1109/TIP.2020.3018222Rinnakkaistallennettu versioPeng, W., Hong, X., & Zhao, G. (2019). Video action recognition via neural architecture searching. 2019 IEEE International Conference on Image Processing (ICIP), 11–15. https://doi.org/10.1109/ICIP.2019.8802919Rinnakkaistallennettu versioPeng, W., Shi, J., & Zhao, G. (2021). Spatial temporal graph deconvolutional network for skeleton-based human action recognition. IEEE Signal Processing Letters, 28, 244–248. https://doi.org/10.1109/LSP.2021.3049691Rinnakkaistallennettu versioPeng, W., Hong, X., & Zhao, G. (2021). Tripool: Graph triplet pooling for 3D skeleton-based action recognition. Pattern Recognition, 115, 107921. https://doi.org/10.1016/j.patcog.2021.107921Rinnakkaistallennettu versioPeng, W., Shi, J., Varanka, T., & Zhao, G. (2021). Rethinking the ST-GCNs for 3D skeleton-based human action recognition. Neurocomputing, 454, 45–53. https://doi.org/10.1016/j.neucom.2021.05.004Rinnakkaistallennettu versioPeng, W., Varanka, T., Mostafa, A., Shi, H., & Zhao, G. (2021). Hyperbolic deep neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/TPAMI.2021.3136921Rinnakkaistallennettu versioPeng, W., Hong, X., Chen, H., & Zhao, G. (2020). Learning graph convolutional network for skeleton-based human action recognition by neural searching. Proceedings of the AAAI Conference on Artificial Intelligence, 34(03), 2669–2676. https://doi.org/10.1609/aaai.v34i03.5652Rinnakkaistallennettu versioPeng, W., Shi, J., Xia, Z., & Zhao, G. (2020). Mix dimension in poincaré geometry for 3d skeleton-based action recognition. Proceedings of the 28th ACM International Conference on Multimedia, 1432–1440. https://doi.org/10.1145/3394171.3413910Rinnakkaistallennettu versioAcademic dissertation to be presented with the assent of the Doctoral Training Committee of Technology and Natural Sciences of the University of Oulu for public defence in the OP auditorium (L10), Linnanmaa, on 14 April 2022, at 12 noonAbstract
Understanding human behavior is one of the most pivotal steps toward real-world Artificial Intelligence (AI) or even Artificial general intelligence (AGI). However, this task is challenging as human social attributes make human beings unique, leading to various and complicated behaviors. Moreover, real-life behavior data are normally high-dimensional with dynamic changes or even non-Euclidean structures, involving multiple modalities. Currently, one of the first alternatives to addressing these challenges is using deep neural networks or deep learning, which has brought revolutionary changes in data computation and computer sciences. Nevertheless, expert knowledge of both neural architecture design and human behavior analysis is expected more than ever before in this interdisciplinary research field. All these issues spur the current deep learning studies towards automatic deep neural network learning, which could automatically sketch a neural architecture for a given behavior analysis task. In line with this topic, this thesis explores the automatic neural network learning approach for human behavior understanding from the most representative behaviors, including human facial expression and actions, step by step. First, manually designed computational models are proposed for human facial expression and actions with dynamic information and graph structures. Based on this, to free humans from the exhausting process, more advanced methods, i.e., automatic neural network learning, are presented. Extensive experiments on benchmark facial expression datasets and action recognition datasets are conducted and comparison results prove the effectiveness of the proposed methods.Tiivistelmä
Ihmisten käyttäytymisen ymmärtäminen on yksi keskeisistä askeleista kohti todellista tekoälyä (AI) tai jopa yleistä tekoälyä (AGI). Tämä tehtävä on kuitenkin haastava, sillä ihmisen sosiaaliset ominaisuudet tekevät ihmisistä ainutlaatuisia, mikä johtaa erilaisiin ja monimutkaisiin käyttäytymismalleihin. Lisäksi tosielämän käyttäytymisdata on yleensä korkeaulotteinen, ja siinä on dynaamisia muutoksia tai jopa ei-euklidisia rakenteita, joihin liittyy useita modaliteetteja. Tällä hetkellä yksi ensimmäisistä vaihtoehdoista haasteisiin vastaamiseksi on syvän neuroverkon tai syväoppimisen käyttö, joka on tuonut mukanaan Vallankumoukselliset muutokset tietojen laskennassa ja tietojenkäsittelytieteissä. Asiantuntijatietoa sekä hermoarkkitehtuurin suunnittelusta että ihmisen käyttäytymisen analysoinnista odotetaan kuitenkin enemmän kuin koskaan aiemmin tällä tieteidenvälisellä tutkimusalueella. Kaikki nämä kysymykset kannustavat nykyisiä syväoppimistutkimuksia kohti automaattista syvän neuroverkon oppimista, joka voisi automaattisesti luonnostella hermoarkkitehtuurin tietylle käyttäytymisanalyysille Tämän aiheen mukaisesti opinnäytetyössä tutkitaan vaihe vaiheelta automaattista neuroverk- kooppimisen lähestymistapaa ihmisen käyttäytymisen ymmärtämiseen edustavimmista käyttäytymismalleista, mukaan lukien ihmisen ilmeet ja toiminnot. Ensin ehdotetaan manuaalisesti suunniteltuja laskennallisia malleja ihmisen ilmeille ja toiminnalle dynaamisilla tiedoilla ja graafirakenteilla, joiden pohjalta ihmisen uuvuttavasta edistymisestä vapauttamiseksi esitetään edistyneempiä menetelmiä, ie, automaattinen hermoverkkooppiminen. Kattavia kokeita benchmark ME-tietosarjoista ja toiminnantunnistustietosarjoista tehdään ja vertailutulokset osoittavat ehdotettujen menetelmien tehokkuuden
Author Correction: Establishment and equilibrium levels of deleterious mutations in large populations (Scientific Reports, (2019), 9, 1, (10384), 10.1038/s41598-019-46803-7)
No full textThe original version of this Article contained errors. Affiliations 1 and 2 were reversed. Secondly, Affiliation 7 was incorrectly given as ‘Institute for Cellular and Molecular Medicine, Department of Immunology, and SAMRC Extramural Unit for Stem Cell Research and Therapy, Faculty of Health Sciences, University of Pretoria, Pretoria, 0084, South Africa’. Thirdly, an affiliation was omitted for the author Michael S. Pepper, which is now listed as Affiliation 8. Fourthly, Affiliation 1 was omitted for the author Johan W. Viljoen. Finally, Augustinus J. van Zyl was incorrectly affiliated with ‘Institute for Maternal and Child Health, IRCCS ‘Burlo Garofolo’, Trieste, Italy.’ The correct author affiliations are listed below: Affiliation 1: Department of Electrical, Electronic and Computer Engineering, EBIT, University of Pretoria, Pretoria 0028, South Africa Johan W. Viljoen and J. Pieter de Villiers Affiliation 2: Development, Research and Technology Department, Hensoldt Optronics, Centu..
Mr. Melvin J. Collier, RWWL AUC, June 2011
No full textThis video is a conversation with Mr. Melvin J. Collier. Mr. Collier talks about his book, "From Mississippi to Africa: A Journey of Discovery". Daniel Le, AUC Woodruff Library, is the interviewer
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