| 1 |
Soubeyrand_2020 |
The current COVID-19 wave will likely be mitigated in the second-line European countries |
Samuel Soubeyrand; Melina Ribaud; Virgile Baudrot; Denis Allard; Denys Pommeret; Lionel Roques |
2020 |
2020-04-22 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>69179 |
dh3cgd48 |
0.932581 |
Lim_T_2012 |
|
| 2 |
Rudnicki_2020 |
Estimate of Covid-19 prevalence using imperfect data |
Witold R Rudnicki; Radosław Piliszek |
2020 |
2020-04-17 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>64840 |
bbzmu37q |
0.931755 |
|
|
| 3 |
Park_2020 |
Potential roles of social distancing in mitigating the spread of coronavirus disease 2019 (COVID-19) in South Korea |
Sang Woo Park; Kaiyuan Sun; Cécile Viboud; Bryan T Grenfell; Jonathan Dushoff |
2020 |
2020-03-30 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>45815 |
7t3ci8tl |
0.914968 |
Wang_2014, Lim_T_2012 |
|
| 4 |
Dhenain_2020 |
ESTIMATION OF COVID-19 CASES IN FRANCE AND IN DIFFERENT COUNTRIES: HOMOGENEISATION BASED ON MORTALITY |
Marc Dhenain |
2020 |
2020-04-11 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>55913 |
rxkltaqy |
0.912943 |
Lim_T_2012, Pullano_2020, Pearson_2020, Georgiev_2009, Dutra_2020 |
|
| 5 |
Dutra_2020 |
Estimate of the Maximum Limit of Total Cases of Infected Patients COVID-19 |
Carlos Maximiliano Dutra; Carlos Augusto Riella de Melo |
2020 |
2020-04-15 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>60822 |
zrgax3qa |
0.911429 |
|
|
| 6 |
Asteris_2020 |
A Novel Heuristic Global Algorithm to Predict the COVID-19 Pandemic Trend |
Panagiotis G. Asteris; Maria Douvika; Christina Karamani; Athanasia Skentou; Tryfon Daras; Liborio Cavaleri; Danial Jahed Armaghani; Katerina Chlichlia; Theoklis E. Zaoutis |
2020 |
2020-04-22 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>68445 |
v6on5gv9 |
0.871720 |
|
|
| 7 |
Brown_2020 |
Mortality from COVID-19 in 12 countries and 6 states of the United States |
Patrick Brown; Prabhat Jha |
2020 |
2020-04-22 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>69161 |
2r4uty9g |
0.852383 |
Lim_T_2012, Pearson_2020, Georgiev_2009, Pullano_2020 |
|
| 8 |
Lachmann_2020 |
Correcting under-reported COVID-19 case numbers |
Alexander Lachmann |
2020 |
2020-03-18 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>36178 |
gttuxtw6 |
0.846483 |
Pai_M_2006 |
|
| 9 |
Fisman_2020 |
Standardization and Age-Distribution of COVID-19: Implications for Variability in Case Fatality and Outbreak Identification |
David Fisman; Amy Greer; Ashleigh Tuite |
2020 |
2020-04-14 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>59832 |
63hs1loe |
0.830443 |
Lim_T_2012, Wu_Z_2013, de_Zwart_2007, Yang_2011 |
|
| 10 |
Qi_R_2020 |
Case fatality rate of novel coronavirus disease 2019 in China |
Rui Qi; Chao Ye; Xiang-rong Qin; Xue-Jie Yu |
2020 |
2020-02-26 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>28076 |
hk491mr6 |
0.822804 |
Pearson_2020 |
|
| 11 |
Riou_2020 |
Adjusted age-specific case fatality ratio during the COVID-19 epidemic in Hubei, China, January and February 2020 |
Julien Riou; Anthony Hauser; Michel J Counotte; Christian L Althaus |
2020 |
2020-03-06 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>31104 |
mrsya6wz |
0.821865 |
Win_K_2011, Lim_T_2012, Wang_2014 |
|
| 12 |
Pike_2020 |
An international comparison of the second derivative of COVID-19 deaths after implementation of social distancing measures |
William Thomas Pike; Vikas Saini |
2020 |
2020-03-25 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>41475 |
ln26bux8 |
0.803219 |
|
|
| 13 |
Verelst_2020 |
Indications for healthcare surge capacity in European countries facing an exponential increase in COVID19 cases |
Frederik Verelst; Elise J. Kuylen; Philippe Beutels |
2020 |
2020-03-17 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>35980 |
jmc9yhr0 |
0.802596 |
Lim_T_2012, Georgiev_2009 |
|
| 14 |
Dudel_2020 |
Monitoring trends and differences in COVID-19 case fatality rates using decomposition methods: Contributions of age structure and age-specific fatality |
Christian Dudel; Tim Riffe; Enrique Acosta; Alyson A. van Raalte; Mikko Myrskyla |
2020 |
2020-04-02 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>48397 |
6ub9yh27 |
0.796456 |
Win_K_2011, Lim_T_2012, Kulu_2020, Wu_Z_2013, Galvani_2003 |
Fisman_2020, Dhenain_2020, Dowd_2020 |
| 15 |
Bignami_2020 |
A demographic adjustment to improve measurement of COVID-19 severity at the developing stage of the pandemic |
Simona Bignami; Daniela Ghio |
2020 |
2020-03-27 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>40998 |
tydj4705 |
0.769213 |
|
|
| 16 |
Potluri_2020 |
Making sense of the Global Coronavirus Data: The role of testing rates in understanding the pandemic and our exit strategy |
Rahul Potluri; Deepthi Lavu |
2020 |
2020-04-11 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>54239 |
1kxxg0s9 |
0.758906 |
|
|
| 17 |
BOELLE_2020 |
Excess cases of Influenza like illnesses in France synchronous with COVID19 invasion. |
Pierre-Yves BOELLE |
2020 |
2020-03-17 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>35741 |
ba3we97o |
0.757209 |
|
|
| 18 |
Medfod_2020 |
Population age structure only partially explains the large number of COVID-19 deaths at the oldest ages |
Anthony Medfod; Sergi Trias-Llimos |
2020 |
2020-04-11 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>56341 |
lkbwxo41 |
0.750747 |
|
Dowd_2020 |
| 19 |
Bulut_2020 |
New Epidemiological Model Suggestions Revealing Size of Epidemics Based on the COVID-19 Pandemic Example: Wavelength Models |
Tevfik Bulut |
2020 |
2020-04-11 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>56432 |
3mmo1x2n |
0.734599 |
Lim_T_2012, Dutra_2020 |
|
| 20 |
Liao_2020 |
A Tempo-geographic Analysis of Global COVID-19 Epidemic Outside of China |
Huipeng Liao; Gifty Marley; Yafei Si; Zaisheng Wang; Yewei Xie; Cheng Wang; Weiming Tang |
2020 |
2020-03-23 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>39602 |
fb0nwixw |
0.696702 |
Lim_T_2012 |
|
| 21 |
Khafaie_2020 |
Cross-Country Comparison of Case Fatality Rates of COVID-19/SARS-COV-2 |
Khafaie, Morteza Abdullatif; Rahim, Fakher |
2020 |
2020-04-23 |
NONCOMM |
Y |
PMC7104689 |
|
10.24171/j.phrp.2<IP_ADDRESS> |
het5173i |
0.665409 |
Lim_T_2012 |
|
| 22 |
Dahal_2020 |
Investigating spatial variability in COVID-19 pandemic severity across 19 geographic areas, Spain, 2020 |
Sushma Dahal; Kenji Mizumoto; Gerardo Chowell |
2020 |
2020-04-17 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>65524 |
8l2ed3vq |
0.653754 |
Lim_T_2012, Yang_2011 |
|
| 23 |
Mizumoto_2020 |
Spatial variability in the risk of death from COVID-19 in Italy, 2020 |
Kenji Mizumoto; Sushma Dahal; Gerardo Chowell |
2020 |
2020-04-03 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>49668 |
z74pb4r2 |
0.648166 |
Lim_T_2012 |
|
| 24 |
O'Brien_2014 |
Foodborne Diseases: Prevalence of Foodborne Diseases in Europe |
O'Brien, S.J. |
2014 |
2014-12-31 |
PMC |
N |
|
|
10.1016/b978-0-12-378612-8.00077-9 |
7vnd2r1x |
0.634976 |
Pai_M_2006 |
|
| 25 |
Dowd_2020 |
Demographic science aids in understanding the spread and fatality rates of COVID-19 |
Jennifer Beam Dowd; Valentina Rotondi; Liliana Adriano; David M Brazel; Per Block; Xuejie Ding; Yan Liu; Melinda C Mills |
2020 |
2020-03-18 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>36293 |
gv8wlo06 |
0.634787 |
|
|
| 26 |
Sherpa_2020 |
Estimating Impact of Austerity policies in COVID-19 fatality rates:Examining the dynamics of economic policy and Case Fatality Rates (CFR) of COVID-19 in OCED countries |
Dawa Sherpa |
2020 |
2020-04-06 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>47530 |
tm3loeir |
0.634340 |
|
Pike_2020, de_Figueiredo_2020 |
| 27 |
Goli_2020 |
How much of SARS-CoV-2 Infections is India detecting? A model-based estimation |
Srinivas Goli; K.S. James |
2020 |
2020-04-11 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>59014 |
c9bp9euw |
0.627353 |
Lim_T_2012, Pai_M_2006, Win_K_2011 |
|
| 28 |
Spiteri_2020 |
First cases of coronavirus disease 2019 (COVID-19) in the WHO European Region, 24 January to 21 February 2020 |
Spiteri, Gianfranco; Fielding, James; Diercke, Michaela; Campese, Christine; Enouf, Vincent; Gaymard, Alexandre; Bella, Antonino; Sognamiglio, Paola; Sierra Moros, Maria José; Riutort, Antonio Nicolau; Demina, Yulia V.; Mahieu, Romain; Broas, Markku; Bengnér, Malin; Buda, Silke; Schilling, Julia; Filleul, Laurent; Lepoutre, Agnès; Saura, Christine; Mailles, Alexandra; Levy-Bruhl, Daniel; Coignard, Bruno; Bernard-Stoecklin, Sibylle; Behillil, Sylvie; van der Werf, Sylvie; Valette, Martine; Lina, Bruno; Riccardo, Flavia; Nicastri, Emanuele; Casas, Inmaculada; Larrauri, Amparo; Salom Castell, Magdalena; Pozo, Francisco; Maksyutov, Rinat A.; Martin, Charlotte; Van Ranst, Marc; Bossuyt, Nathalie; Siira, Lotta; Sane, Jussi; Tegmark-Wisell, Karin; Palmérus, Maria; Broberg, Eeva K.; Beauté, Julien; Jorgensen, Pernille; Bundle, Nick; Pereyaslov, Dmitriy; Adlhoch, Cornelia; Pukkila, Jukka; Pebody, Richard; Olsen, Sonja; Ciancio, Bruno Christian |
2020 |
2020-03-05 |
COMM-USE |
Y |
PMC7068164 |
32156327.0 |
10.2807/1560-7917.es.2<IP_ADDRESS>0178 |
ivwn4nhl |
0.624507 |
Lim_T_2012, Pearson_2020, Georgiev_2009 |
|
| 29 |
Shojaee_2020 |
Predicting the mortality due to Covid-19 by the next month for Italy, Iran and South Korea; a simulation study |
Shojaee, Sajad; Pourhoseingholi, Mohamad Amin; Ashtari, Sara; Vahedian-Azimi, Amir; Asadzadeh-Aghdaei, Hamid; Zali, Mohammad Reza |
2020 |
2020-01-01 |
COMM-USE |
N |
PMC7149809 |
|
|
qdoseeyv |
0.619015 |
Lim_T_2012 |
|
| 30 |
Soneji_2020 |
Population-Level Mortality Rates from Novel Coronavirus (COVID-19) in South Korea |
Samir Soneji; Hiram Beltrán-Sánchez; JaeWon Yang; Caroline Mann |
2020 |
2020-03-27 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>41814 |
lizwiate |
0.605553 |
Lim_T_2012, Lee_Y_2016, Win_K_2011 |
|
| 31 |
Effenberger_2020 |
Association of the COVID-19 pandemic with Internet Search Volumes: A Google TrendsTM Analysis |
Effenberger, Maria; Kronbichler, Andreas; Shin, Jae Il; Mayer, Gert; Tilg, Herbert; Perco, Paul |
2020 |
2020-04-17 |
PMC |
Y |
|
|
10.1016/j.ijid.2020.04.033 |
z0m6uuzf |
0.594447 |
|
|
| 32 |
Wetzler_2020 |
Covid-19 excess deaths in the United States through July 2020 |
Harry P Wetzler; Erica A Wetzler |
2020 |
2020-04-06 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>51532 |
efarquxn |
0.579766 |
Pearson_2020, Lee_Y_2016, Mavalankar_2008 |
Miller_2020, Lachmann_2020 |
| 33 |
Bignami_2020 |
Estimates of COVID-19 case-fatality risk from individual-level data |
Simona Bignami; Daniela Ghio; Ari Van Assche |
2020 |
2020-04-22 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>67751 |
fgjhli0p |
0.569012 |
Rudnicki_2020 |
|
| 34 |
Velavan_2020 |
The COVID‐19 epidemic |
Velavan, Thirumalaisamy P.; Meyer, Christian G. |
2020 |
2020-02-16 |
PMC |
Y |
PMC7169770 |
32052514.0 |
10.1111/tmi.13383 |
e4hmo4yc |
0.555560 |
Lim_T_2012, Georgiev_2009 |
|
| 35 |
Alipio_2020 |
Epidemiological characteristics of an outbreak of Coronavirus Disease 2019 in the Philippines |
Mark Marcos Alipio; Joseph Dave Mendoza Pregoner |
2020 |
2020-04-16 |
BioRxiv |
N |
|
|
10.1101/2<IP_ADDRESS>53926 |
b3r3yyjd |
0.530122 |
Lim_T_2012, Georgiev_2009, Wang_2014 |
|
| 36 |
Tanaka_2020 |
Estimation of true number of COVID-19 infected people in Japan using LINE questionnaire |
Shin-ichiro Tanaka; Shinya Oku |
2020 |
2020-04-20 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>66100 |
d3lsgatj |
0.528666 |
Pearson_2020 |
|
| 37 |
Neher_2020 |
Potential impact of seasonal forcing on a SARS-CoV-2 pandemic |
Richard A Neher; Robert Dyrdak; Valentin Druelle; Emma B Hodcroft; Jan Albert |
2020 |
2020-02-17 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>22806 |
3p2dl8yf |
0.509629 |
|
|
| 38 |
Dayal_2020 |
Connecting BCG Vaccination and COVID-19: Additional Data |
Devi Dayal; Saniya Gupta |
2020 |
2020-04-08 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>53272 |
by4aefjc |
0.508642 |
|
Miller_2020, Goswami_2020 |
| 39 |
Lai_C_2020 |
Global epidemiology of coronavirus disease 2019 (COVID-19): disease incidence, daily cumulative index, mortality, and their association with country healthcare resources and economic status |
Lai, Chih-Cheng; Wang, Cheng-Yi; Wang, Ya-Hui; Hsueh, Shun-Chung; Ko, Wen-Chien; Hsueh, Po-Ren |
2020 |
2020-04-30 |
PMC |
Y |
|
32199877.0 |
10.1016/j.ijantimicag.2020.105946 |
lsrqko6p |
0.502974 |
Lim_T_2012, Georgiev_2009, Pearson_2020, Pullano_2020 |
|
| 40 |
Amariles_2020 |
COVID-19 in Colombia endpoints. Are we different, like Europe? |
Amariles, Pedro; Granados, Johan; Ceballos, Mauricio; Montoya, Carlos Julio |
2020 |
2020-03-31 |
PMC |
Y |
|
|
10.1016/j.sapharm.2020.03.013 |
lgqseo8t |
0.488523 |
Lim_T_2012, Wu_Z_2013, Wang_2014, Pearson_2020, Pai_M_2006 |
|
| 41 |
Rodrigues_2020 |
REGIONAL DETERMINANTS OF THE EXPANSION OF COVID-19 IN BRAZIL |
Waldecy Rodrigues; David Nadler Prata; Wainesten Camargo |
2020 |
2020-04-17 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>63925 |
epa6m6nq |
0.487818 |
|
|
| 42 |
O’Reilly_2018 |
Projecting the end of the Zika virus epidemic in Latin America: a modelling analysis |
Kathleen M O’Reilly; Rachel Lowe; W John Edmunds; Philippe Mayaud; Adam Kucharski; Rosalind M Eggo; Sebastian Funk; Deepit Bhatia; Kamran Khan; Moritz U Kraemar; Annelies Wilder-Smith; Laura C Rodrigues; Patricia Brasil; Eduardo Massad; Thomas Jaenisch; Simon Cauchemez; Oliver J Brady; Laith Yakob |
2018 |
2018-05-18 |
BioRxiv |
N |
|
|
10.1101/323915 |
58y8mg8m |
0.485516 |
Lim_T_2012, Wang_2014 |
Alkhamis_2018 |
| 43 |
Routledge_2018 |
Estimating spatiotemporally varying malaria reproduction numbers in a near elimination setting |
Routledge, Isobel; Chevéz, José Eduardo Romero; Cucunubá, Zulma M.; Rodriguez, Manuel Gomez; Guinovart, Caterina; Gustafson, Kyle B.; Schneider, Kammerle; Walker, Patrick G.T.; Ghani, Azra C.; Bhatt, Samir |
2018 |
2018-06-26 |
None |
N |
PMC6018772 |
29946060.0 |
10.1038/s41467-018-04577-y |
rc0zhw5h |
0.465238 |
|
Routledge_2019 |
| 44 |
Shet_2020 |
Differential COVID-19-attributable mortality and BCG vaccine use in countries |
Anita Shet; Debashree Ray; Neelika Malavige; Mathuram Santosham; Naor Bar-Zeev |
2020 |
2020-04-06 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>49478 |
1n96pz86 |
0.438892 |
Wu_Z_2013 |
Miller_2020, Hensel_2020, Sala_2020 |
| 45 |
Pearson_2020 |
Projected early spread of COVID-19 in Africa |
Carl Andrew Pearson; Cari Van Schalkwyk; Anna M Foss; Kathleen M O'Reilly; Juliet R C Pulliam |
2020 |
2020-04-10 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>54403 |
zl1t896w |
0.431998 |
Lee_Y_2016 |
BOELLE_2020 |
| 46 |
Bannister-Tyrrell_2020 |
Preliminary evidence that higher temperatures are associated with lower incidence of COVID-19, for cases reported globally up to 29th February 2020 |
Melanie Bannister-Tyrrell; Anne Meyer; Celine Faverjon; Angus Cameron |
2020 |
2020-03-20 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>36731 |
drzphrqj |
0.427898 |
|
Triplett_2020, Ficetola_2020 |
| 47 |
Liu_Q_2020 |
Assessing the Global Tendency of COVID-19 Outbreak |
Qinghe Liu; Zhicheng Liu; Junkai Zhu; Yuhao Zhu; Deqiang Li; Zefei Gao; Liuling Zhou; Junyan Yang; Qiao Wang |
2020 |
2020-03-20 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>38224 |
adcptyfj |
0.421608 |
Lim_T_2012, Georgiev_2009, Wang_2014, Pirie_2006 |
|
| 48 |
Craig_2020 |
Risk of COVID-19 importation to the Pacific islands through global air travel |
Craig, A. T.; Heywood, A. E.; Hall, J. |
2020 |
2020-03-23 |
COMM-USE |
Y |
PMC7113321 |
32202489.0 |
10.1017/s0950268820000710 |
nwl95ysz |
0.419779 |
Lim_T_2012, Wang_2014, Georgiev_2009 |
|
| 49 |
Triplett_2020 |
Evidence that higher temperatures are associated with lower incidence of COVID-19 in pandemic state, cumulative cases reported up to March 27, 2020 |
Michael Triplett |
2020 |
2020-04-06 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>51524 |
g26to20g |
0.406349 |
Ficetola_2020, Galvani_2003 |
Bannister-Tyrrell_2020, Ficetola_2020 |
| 50 |
Mehta_2020 |
Early Stage Prediction of US County Vulnerability to the COVID-19 Pandemic |
Mihir Mehta; Juxihong Julaiti; Paul Griffin; Soundar Kumara |
2020 |
2020-04-11 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>55285 |
901ghexi |
0.403983 |
|
|
| 51 |
Modi_2020 |
Total COVID-19 Mortality in Italy: Excess Mortality and Age Dependence through Time-Series Analysis |
Chirag Modi; Vanessa Boehm; Simone Ferraro; George Stein; Uros Seljak |
2020 |
2020-04-20 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>67074 |
vg96f35h |
0.388823 |
Pearson_2020, Win_K_2011, Lim_T_2012 |
|
| 52 |
Desjardins_2020 |
Rapid surveillance of COVID-19 in the United States using a prospective space-time scan statistic: Detecting and evaluating emerging clusters |
Desjardins, M.R.; Hohl, A.; Delmelle, E.M. |
2020 |
2020-05-31 |
PMC |
Y |
|
|
10.1016/j.apgeog.2020.102202 |
y691hark |
0.387409 |
|
|
| 53 |
Miller_2020 |
Mapping the Burden of COVID-19 in the United States |
Ian F Miller; Alexander D Becker; Bryan T Grenfell; C. Jessica E Metcalf |
2020 |
2020-04-11 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>54700 |
n5gykapg |
0.382239 |
|
|
| 54 |
Kinfu_2020 |
COVID-19 pandemic in the African continent: forecasts of cumulative cases, new infections, and mortality |
Yohannes Kinfu; Uzma Alam; Tom Achoki |
2020 |
2020-04-14 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>59154 |
atee6lis |
0.379583 |
Lim_T_2012, Pearson_2020, Wu_Z_2013 |
|
| 55 |
Zhang_2020 |
Comparison of the spatiotemporal characteristics of the COVID-19 and SARS outbreaks in mainland China |
Xi Zhang; Hua-Xiang Rao; Yuwan Wu; Yubei Huang; Hongji Dai |
2020 |
2020-03-26 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>34058 |
brwmpm5a |
0.375786 |
Lim_T_2012, Georgiev_2009, Wang_2014 |
|
| 56 |
Ogen_2020 |
Assessing nitrogen dioxide (NO2) levels as a contributing factor to coronavirus (COVID-19) fatality |
Ogen, Yaron |
2020 |
2020-07-15 |
PMC |
Y |
|
|
10.1016/j.scitotenv.2020.138605 |
u4doukk7 |
0.369331 |
|
|
| 57 |
Skrip_2020 |
Seeding COVID-19 across sub-Saharan Africa: an analysis of reported importation events across 40 countries |
Laura A Skrip; Prashanth Selvaraj; Brittany Hagedorn; Andre Lin Ouédraogo; Navideh Noori; Dina Mistry; Jamie Bedson; Laurent Hébert-Dufresne; Samuel V Scarpino; Benjamin Muir Althouse |
2020 |
2020-04-06 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>50203 |
vtuljhcp |
0.358702 |
|
|
| 58 |
Pullano_2020 |
Novel coronavirus (2019-nCoV) early-stage importation risk to Europe, January 2020 |
Pullano, Giulia; Pinotti, Francesco; Valdano, Eugenio; Boëlle, Pierre-Yves; Poletto, Chiara; Colizza, Vittoria |
2020 |
2020-01-30 |
COMM-USE |
Y |
PMC7001240 |
32019667.0 |
10.2807/1560-7917.es.2<IP_ADDRESS>0057 |
332ey2v1 |
0.347112 |
Lim_T_2012, Georgiev_2009, Wang_2014 |
|
| 59 |
Clark_2020 |
How many are at increased risk of severe COVID-19 disease? Rapid global, regional and national estimates for 2020 |
Andrew Clark; Mark Jit; Charlotte Warren-Gash; Bruce Guthrie; Harry HX Wang; Stewart W Mercer; Colin Sanderson; Martin McKee; Christopher Troeger; Kanyin I Ong; Francesco Checchi; Pablo Perel; Sarah Joseph; Hamish P Gibbs; Amitava Banerjee; Rosalind M Eggo |
2020 |
2020-04-22 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>64774 |
czuq8rw5 |
0.345566 |
Tan_W_2008, Wu_Z_2013, Mphande_2016 |
|
| 60 |
Akiyama_2020 |
Relationship between COVID-19 death toll doubling time and national BCG vaccination policy |
Yutaka Akiyama; Takashi Ishida |
2020 |
2020-04-17 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>55251 |
4m9ngo8g |
0.337373 |
Win_K_2011, Pearson_2020 |
|
| 61 |
Islam_2020 |
Modeling risk of infectious diseases: a case of Coronavirus outbreak in four countries |
Md. Mazharul Islam; Md. Monirul Islam; Md. Jamal Hossain; Faroque Ahmed |
2020 |
2020-04-06 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>49973 |
ca4c6b8a |
0.333454 |
Lim_T_2012, Wang_2014, Georgiev_2009 |
|
| 62 |
Cai_H_2020 |
Sex difference and smoking predisposition in patients with COVID-19 |
Cai, Hua |
2020 |
2020-04-30 |
PMC |
Y |
PMC7103991 |
32171067.0 |
10.1016/s2213-2600(20)30117-x |
v9h4rlln |
0.324486 |
Pearson_2020, Win_K_2011 |
|
| 63 |
Zahran_2020 |
The Longevity-Frailty Hypothesis: Evidence from COVID-19 Death Rates in Europe |
Sammy Zahran; Levi Altringer; Ashok Prasad |
2020 |
2020-04-17 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>65540 |
9p0dsyqx |
0.321353 |
Dowd_2020, Kulu_2020 |
|
| 64 |
Singh_2020 |
BCG VACCINES MAY NOT REDUCE COVID-19 MORTALITY RATES |
Samer Singh |
2020 |
2020-04-17 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>62232 |
x8v23r0g |
0.315876 |
Win_K_2011, Pearson_2020 |
|
| 65 |
Yuan_2020 |
Trends and prediction in daily incidence and deaths of COVID-19 in the United States: a search-interest based model |
Xiaoling Yuan; Jie Xu; Sabiha Hussain; He Wang; Nan Gao; Lanjing Zhang |
2020 |
2020-04-20 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>64485 |
zzmfhr2s |
0.308369 |
Pearson_2020 |
|


If an account with that email exists you will will receive an email with a link to reset your password shortly


Introduction
Before we do anything, we need to understand some of the components we'll be interacting with and switching between.
-
The Python interpreter: we input commands, define variables and functions, write loops, etc. The interpreter parses it all for us, line by line as we write them, and takes action.
-
Text editor: this is where we essentially chain a bunch of commands together within a single document and save it as a Python script (ending in .py).
-
The command line: Accessed via "Terminal" on OS X or "cmd.exe" (or possibly PowerShell) on Windows. It's where we run the Python interpreter, among other programs, and issue commands that allow us to navigate our computer's folders.
That's pretty much it. These three pieces give you a container for code you're writing, the program to run it and a method to get it all running. We'll be doing some switching back and forth between entering code directly into the interpreter we run from the command line and the text editor.
As we progress, we'll be dealing with two other things.
-
External libraries: Python comes with a standard library that has a ton of useful stuff. Developers have written libraries that take this functionality even further. By importing parts of these libraries into our Python script, we can do complex things, like fetch a web page, with a single line of code.
-
A virtual enviroment: The bubble where all of our code lives.
This is a crash course in some of Python's major data types and and how to tame them. We'll be doing things like mashing strings together, running through items in a list and defining very simple functions.
We'll be using iPython's interactive interpreter, which means we have access to a few extra features (which iPython modestly calls magic functions).
The files in this folder:
-
var.py: A Python script with some variables of different types like integers, lists and dictionaries.
-
exercises.py: A file that gives a list of exercises in the form of comments; guidance on how to manipulate the variables in var.py. It's also the place we can write code to save for future reference. Nearly everything we do in this task will use iPython.
-
fun_with_subs.py: A file for practicing string substitutions, which is something we'll be doing frequently. It covers the depreciated method you'll encounter frequently and covers
str.format()
. -
whitespace.py: A guide to how Python decides which blocks of code to run when; it's all controlled by indentation.
Any finished versions will appear in the completed folder.
Opening iPython is as easy as typing ipython
into PowerShell.
To get everything from var.py queued up, we can either run it from within iPython using one of the magic functions:
%run var.py
Or we can use the more traditional method of importing everything defined in a script, treating it as a module:
from var import *
This will make a little more sense as we start bringing in other Python libraries to tackle our other tasks. Either way, it's going to get us those variables defined in var.py. We can quickly test that these variables were loaded by typing lucky_number
.
Let's say I have a longer chunk of code that I want to try out in iPython to see if it works as expected.
presents = ['A brand new car', 'Socks']
for gift in presents:
if gift == 'A brand new car':
print gift, '<- Oh yeah!'
else:
print gift, '<- Meh.'
Having to type this line by line into the interpreter and pay attention to indentation can be a pain.
But I can write that block of code in a text editor instead, then select it all and copy it. Switching over to iPython, I can use the built-in paste function:
%paste
This will paste the contents of my clipboard, preserving all the indentation and other white space, and execute whatever code was there. Pretty handy.
Finally, iPython has another function that will give you a recap of commands you've typed:
%history


NFL disciplines 33 players after wild brawl
by NY Post, with staff writersThe Pittsburgh Steelers-Cleveland Browns brawl didn’t just prove costly for Myles Garrett and Mason Rudolph.
The NFL handed down 35 fines for $US732,219 ($AU1.1 million) total – including $250,000 each to the Browns and Steelers franchises — in response to last week’s fight, the league announced Sunday (AEDT).
Stream Live & On-Demand coverage of the 2019/20 NFL Season with ESPN on KAYO SPORTS. Get your 14-day free trial and start streaming instantly >
Including salary lost to suspensions, the incident’s price tag was worth more than $US2 million, according to NFL.com.
The majority of the fines came out to $3,507, levied against players for entering the area where Garrett hit the Rudolph over the head with the quarterback’s helmet a part of a late-game fight.
Garrett was fined $45,623 the incident and suspended indefinitely – at least through this postseason.
Rudolph, who went after Garrett prior to being hit, was docked $50,000 for unsportsmanlike conduct.
The Browns’ Larry Ogunjobi was suspended a game, while the Steelers’ Maurkice Pouncy was suspended two games for their roles.


We have created a game called Guess Who Twitter. The rules are simple! Just match a tweet to the Twitter user who you think sent it. The game offers multiple levels of progression. You can even play with your friends.
If you want more information on how the game is played, click the Instructions link in the navigation menu at the top. An example of what the game looks like is below. If you're ready to play, click the Start Playing button at the bottom of the screen. Have fun!
@yanceyGuessWho
@I_Like_Poop
@cuddleMeBum
@Stevens-Dog


On this website are the live, working examples of the components in my BootstrapX project, "Creative and Practical Bootstrap Examples."
Navbar Jumbotron Cards Alerts


*Coding/making websites
*Making Vids
*Exploring places
*Scout
*Table Tennis Player
*Football
*And many more.


{# This is an example of a gateway post redirect template. You can use this by configuring the `gatewayPostRedirectTemplate` config option. #} {# The page's form should submit itself to the `actionUrl` and output all hidden inputs with `inputs` #} {# We suggest inlining all css and keeping the page size to a minimum #}


| Guest House Information |
| # | Contact Person | Address | City | State | Postal | Telephone |
|---|
| 1 | Sudhakar | No.202, C Wing Queens Corner Apts.,2, Queens Road | Bangalore | Karnataka | 560001 |<PHONE_NUMBER>9 /<PHONE_NUMBER> |
| 2 | Narasaiah | 58 C/12, Singasandra Post Hosur Road Kudulu, Anekal Taluk | Bangalore | Karnataka | 560068 | 080-2573 1940 /<PHONE_NUMBER> |
| 3 | Devendran | No.91, 1st Main 4th Block, Behind Sony World | Bangalore | Karnataka | |<PHONE_NUMBER>,9341716670 |
| 4 | Shivadas | E-335, 2nd Floor Rear Flat, East of Kailash | NewDelhi | Delhi | 110068 |<PHONE_NUMBER>5 |
| 5 | Jagadish | Kingston Tower, Flat No 1503,15th floor,Parel village,Ambedkar Road,Kala Chowki | Mumbai | Maharashtra | |<PHONE_NUMBER>5,09322856847 |
| 6 | Ashok | No.7/18, 3rd Floor Crystal Garden Apts Navroji Road, Chelpet | Chennai | TamilNadu | |<PHONE_NUMBER>2 /<PHONE_NUMBER>6 |
| 7 | Ashok | Flat No.1 B 1st Floor, 423 Kilpauk Garden Road Sindhur Garden | Chennai | TamilNadu | 600010 |<PHONE_NUMBER>2 /<PHONE_NUMBER>6 |
| 8 | Desai | Royal Apartments Behind Kesarval Garden Retreat Cortal im, salcete | Goa | Goa | 403722 | |
| 9 | Sarcar | GD-12 Sector III Bidhannagar | Kolkata | West Bengal | 700106 |<PHONE_NUMBER>1 |


M/s Mproteins Established in the year 2017, We are growing rapidly due to our ability of processing, supplying, wholesaling,
exporting and trading a qualitative and comprehensive range of agro products, Whole Spices, Spices
Blends, Powdered Spices, Cereals, Pulses , Natural oil extracts, Instant Spice Mix Powder branded
in the name of Shiv spices. The products consist of in these categories are Mushrooms, Red Chilies,
Pepper, Turmeric, Coriander, Nut meg, Cinnamon, Chick Peas, and many more. Processed under hygienic
and disinfected environment, our complete product range is admired among the clients for their
high nutritional value, purity, freshness, taste and strong aroma . We ensure that our products
are in line with the quality parameters and specified standards of Food and Beverage industry.
Transparency and accuracy in all our business operations have made us a genuine as well as authorized
name in the market. From the day one of our establishment, we are consistently moving ahead to
compete with the different market challenges.
Products
We at Mproteins export a versatile range of spices and Mushrooms that find application in cooking. The
spices are processed and packed under the most hygienic conditions to ensure superior quality and
purity.


PIXELLIB’S OFFICIAL DOCUMENTATION¶
PixelLib is a library created for performing image and video segmentation using few lines of code. It is a flexible library created to allow easy integration of image and video segmentation into software solutions.
PixelLib requires python’s version 3.5-3.7, Download python
It requires pip’s version >= 19.0
Install pip with:
pip3 install pip
Install PixelLib and its dependencies:
Install the latest version of tensorflow(Tensorflow 2.0+) with:
pip3 install tensorflow
Install imgaug with:
pip3 install imgaug
Install PixelLib with:
pip3 install pixellib --upgrade
PixelLib supports the two major types of segmentation and you can create a custom model for objects’ segmentation by training your dataset with PixelLib:
1 Semantic segmentation: Objects in an image with the same pixel values are segmented with the same colormaps.
Semantic segmentation of images with PixelLib using Ade20k model
Semantic segmentation of videos with PixelLib using Ade20k model
Semantic segmentation of images with PixelLib using Pascalvoc model
Semantic Segmentation of videos with PixelLib using Pascalvoc model
2 Instance segmentation: Instances of the same object are segmented with different color maps.
Instance segmentation of images with PixelLib
Instance segmentation of videos with PixelLib
3 Implement Instance Segmentation And Object Detection On Objects By Training Your Dataset..
Inference With A Custom Model Trained With PixelLib
Implement background editing in images and videos using five lines of code. These are the features supported for background editing.
Change the background of an image with a picture
Assign a distinct color to the background of an image
Grayscale the background of an image
Blur the background of an image
Change the background of an Image
Change the background of a Video
Change the Background of A Video
- Semantic segmentation of images with PixelLib using Ade20k model
- Semantic segmentation of videos with PixelLib using Ade20k model
- Segmentation of live camera with Ade20k model
- Semantic segmentation of images with PixelLib using Pascalvoc model
- Semantic Segmentation of videos with PixelLib using Pascalvoc model
- Segmentation of live camera with pascalvoc model
- Instance segmentation of images with PixelLib
- Instance segmentation of videos with PixelLib
- Extraction of objects in videos
- Segmentation of Specific Classes in Videos
- Instance Segmentation of Live Camera with Mask R-cnn.
- Custom Training With PixelLib
- Inference With A Custom Model
- Extraction of Segmented Objects in Videos
- Image Tuning With PixelLib
- Change the Background of A Video
CONTACT INFO:


In 2017, the english wikipedia contained
1384171. Among them, only 16.5% belonged to women.
One thing you may think at this point is, "okey but not everyone appears in Wikipedia". This is actually true. Although it is possible to create your own Wikiepdia page, most biographies belong to people recognized in their field. So one detail we can explore further is the occupation of the different personalities. To do so, we group the occupations in field to get a more general view.
The most common field of occupation is Sport (30%), followed by Artist (29%) and Politics (16%). Sport field includes occupations like "footballer", "cricketer" or "swimmer". Inside the Artist field we can find occupations like "singer", "painter" or "actor/actress"
We explore also the gender by occupation. Only the field corresponding to "Model" has more female presence than male. In all other fields, the presence of men is higher. When disaggregated, we can see that the most common ocupation for females belongs to the "Artist" field and "Sport" moves to the second place.
Nevertheless, the absolute values may not be very informative. That is why we compute the ratio female:male so that you can get an idea of how gender diverse the different occupations are.
As we saw before, female biographies are less represented in all fields except Model which is associated to the mode industry. In this case, for each 5 biographies related to female characters we have one male biography.
Religion and Military are the groups where the ratio female:male becomes larger. In religion related biographies, for each female we will find 69 males. In military related ones, for each female we will find 62 males
The most balanced occupational field is Artist where the ratio female:male is of 1:3


Data Visualization
Simple Ways to Improve Your Matplotlib
Matplotlib is typically the first data visualization package that Python programmers learn. While its users can create basic figures with just a few lines of code, these resulting default plots often prove insufficient in both design aesthetics and communicative power. Simple adjustments can lead to dramatic improvements, however, and in this post, I will share several tips on how to upgrade your Matplotlib figures.
In the examples that follow, I will be using information found in this Kaggle dataset about cereals. I have normalized three features (calories, fat, and sugar) by serving size to better compare cereal nutrition and ratings. Details about these data transformations and the code used to generate each example figure can be found on GitHub.
Remove Spines
The first Matplotlib default to update is that black box surrounding each plot, comprised of four so-called “spines.” To adjust them we first get our figure’s axes via pyplot and then change the visibility of each individual spine as desired.
Let’s say, for example, we want to remove the top and right spines. If we have imported Matplotlib’s pyplot submodule with:
from matplotlib import pyplot as plt
we just need to add the following to our code:
plt.gca().spines['top'].set_visible(False)
plt.gca().spines['right'].set_visible(False)
and the top and right spines will no longer appear. Removing these distracting lines allows more focus to be directed toward your data.
Removing distracting spines can help people focus on your data.
Explore Color Options
Matplotlib’s default colors just got an upgrade but you can still easily change them to make your plots more attractive or even to reflect your company’s brand colors.
Hex Codes
One of my favorite methods for updating Matplotlib’s colors is directly passing hex codes into the color argument because it allows me to be extremely specific about my color choices.
plt.scatter(..., color='#0000CC')
This handy tool can help you select an appropriate hex color by testing it against white and black text as well as comparing several lighter and darker shades. Alternatively, you can take a more scientific approach when choosing your palette by checking out Colorgorical by Connor Gramazio from the Brown Visualization Research Lab. The Colorgorical tool allows you to build a color palette by balancing various preferences like human perceptual difference and aesthetic pleasure.
xkcd Colors
The xkcd color library provides another great way to update Matplotlib’s default colors. These 954 colors were specifically curated and named by several hundred thousand participants of the xkcd color name survey. You can use them in Matplotlib by prefixing their names with “xkcd:”.
plt.scatter(..., color='xkcd:lightish blue')
Matplotlib's default colors can easily be updated by passing hex codes or referencing the xkcd library.
Layer Graph Objects
Matplotlib allows users to layer multiple graphics on top of each other, which proves convenient when comparing results or setting baselines. Two useful properties should be utilized while layering: 1) alpha
for controlling each component’s opacity and 2) zorder
for moving objects to the foreground or background.
Opacity
The alpha property in Matplotlib adjusts an object’s opacity. This value ranges from zero to one with zero being fully transparent (invisible 👀) and one being entirely opaque. Reducing alpha will make your plot objects see-through, allowing multiple layers to be seen at once as well as allowing overlapping points to be distinguished, say, in a scatter plot.
plt.scatter(..., alpha=0.5)
Decreasing alpha reduces opacity and can help you visualize overlapping points.
Order
Matplotlib’s zorder property determines how close objects are to the foreground. Objects with smaller zorder values appear closer to the background, while those with larger values present closer to the front. If I’m making a scatter plot with an accompanying line plot, for example, I can bring the line forward by increasing its zorder.
plt.scatter(..., zorder=1) #background
plt.plot(..., zorder=2) #foreground
Plot objects can be brought to the foreground or pushed to the background by adjusting zorder.
Annotate Main Points or Examples
Many visuals can benefit from the annotation of main points or specific, illustrative examples because these directly convey ideas and boost the validity of results. To add text to a Matplotlib figure, just include annotation code specifying the desired text and its location.
plt.annotate(TEXT, (X_POSITION, Y_POSITION), ...)
The cereal dataset used to produced this blog’s visuals contains nutritional information about several brand name cereals along with a feature labeled as “rating.” One might firstly assume that “rating” is a score indicating cereals that consumers prefer. In the zorder figure above, however, I built a quick linear regression model showing that the correlation between calories per cup and rating is practically non-existent. It seems unlikely that calories would not factor into consumer preference, so we may already be skeptical about our initial assumption about “rating.”
This misconception becomes even more obvious when examining the extremes: Cap’n Crunch is the lowest rated cereal while All-Bran with Extra Fiber rates the highest. Annotating the figure with these representative examples immediately dispels false assumptions about “rating.” This rating information more likely indicates a cereal’s nutritional value. (I have also annotated the cereal with the most calories per cup; Grape Nuts is likely not meant to be consumed in such large quantities! 😆)
Annotating your visuals with a few examples can improve communication and add legitimacy.
Baseline and Highlight
Adding a baseline to your visuals helps set expectations. A simple horizontal or vertical line provides others with appropriate context and often speeds along their understanding of your results. Highlighting a specific region of interest, meanwhile, can further emphasize your conclusions and also facilitates communication with your audience. Matplotlib offers several options for baselining and highlighting, including horizontal and vertical lines, shapes such as rectangles, horizontal and vertical span shading, and filling between two lines.
Horizontal and Vertical Lines
Let’s now consider the interplay between fat and sugar in our cereal dataset. A basic scatter plot of this relationship doesn’t appear interesting at first, but after exploring further, we find the median fat per cup of cereal is just one gram because so many cereals contain no fat at all. Adding this baseline helps people arrive at this finding much more quickly.
A horizontal or vertical baseline can help set the stage for your data.
In other cases you may want to completely remove the default x- and y-axes that Matplotlib provides and create your own axes based on some data aggregate. This process requires three key steps: 1) remove all default spines, 2) remove tick marks, and 3) add new axes as horizontal and vertical lines.
#1. Remove spines
for spine in plt.gca().spines.values():
spine.set_visible(False)
#2. Remove ticks
plt.xticks([])
plt.yticks([])
#3. Add horizontal and vertical lines
plt.axhline(Y_POSITION, ...) #horizontal line
plt.axvline(X_POSITION, ...) #vertical line
You can also create new axes for your data by removing spines and ticks and adding custom lines.
Rectangle
Now that we have plotted the cereals’ fat and sugar contents on new axes, it appears that very few cereals are low in sugar but high in fat. That is, the upper-left quadrant is nearly empty. This seems reasonable because cereals typically are not savory. To make this point abundantly clear, we could direct attention to this low-sugar, high-fat area by drawing a rectangle around it and annotating. Matplotlib provides access to several shapes through its patches module, including a rectangle or even a dolphin. Begin by importing code for the rectangle:
from matplotlib.patches import Rectangle
Then to create a rectangle on the figure, grab the current axes and add a rectangular patch with its location, width, and height:
plt.gca().add_patch(Rectangle((X_POSITION, Y_POSITION), WIDTH, HEIGHT, ...)
Here, the x- and y-positions refer to the placement of the lower-left corner of the rectangle.
To direct people toward a particular part of your visual, consider adding a rectangle.
Shading
Shading provides an alternative option for drawing attention to a particular region of your figure, and there are a few ways to add shading with Matplotlib.
If you intend to highlight an entire horizontal or vertical area, just layer a span into your visual:
plt.axhspan(Y_START, Y_END, ...) #horizontal shading
plt.axvspan(X_START, X_END, ...) #vertical shading
Previously discussed properties like alpha
and zorder
are critical here because you will likely want to make your shading transparent and/or move it to the background.
Shading also provides an effective way to highlight a particular region of your plot.
If the area you would like to shade follows more complicated logic, however, you may instead shade between two user-defined lines. This approach takes a set of x-values, two sets of y-values for the first and second lines, and an optional where
argument that allows you to use logic to filter down to your region of interest.
plt.gca().fill_between(X_VALUES, Y_LINE1, Y_LINE2, WHERE=FILTER LOGIC, ...)
To shade the same area that was previously highlighted with a rectangle, simply define an array of equally spaced sugar values for the x-axis, fill between the median and max fat values on the y-axis (high fat), and filter down to sugar values less than the median (low sugar).
sugars = np.linspace(df.sugars_per_cup.min(), df.sugars_per_cup.max(), 1000)
plt.gca().fill_between(sugars, df.fat_per_cup.median(), df.fat_per_cup.max(),
WHERE=sugars < df.sugars_per_cup.median(), ...)
More complex shading logic is accomplished by filling between two lines and applying a filter.
Conclusion
Matplotlib often gets a bad reputation due to its poor defaults and the shear amount of code needed to produce decent looking visuals. Hopefully, the tips provided in this blog will help you address the first issue, though I’ll admit that the final few example figures required many updates and subsequently a sizable amount of code. If the required bulk of code bothers you, the Seaborn visualization library is an excellent alternative to Matplotlib. It comes with better defaults overall, demands fewer lines of code, and supports customization via traditional Matplotlib syntax if needed.
The main thing to keep in mind when you visualize data–no matter which package you choose–is your audience. The suggestions I’ve offered here aim to smooth out the data communication process by 1) removing extraneous bits like unnecessary spines or tick marks, 2) telling the data story quicker by setting expectations with layering and baselines, and 3) highlighting main conclusions with shading and annotations. The resulting aesthetics also improve, but the primary goal is stronger and more seamless data communication.
I recently shared content similar to this in a data visualization talk at ODSC NYC. You can access my original conference materials as well as the code that powers each example figure in the links below.
| Check out this code on GitHub! | Check out my ODSC conference materials with Google Colab! |
VISUALIZATIONS


|
A game I made in 48 hours using Unity for Ludum Dare 42 with the theme Running out of Space. A combination of Tetris and Blokus, Break the Board is a game where you place randomly spawned pieces onto a game board. Filling entire columns or rows causes them to break and the board size to shrink.  It's only available on Windows right now, but I'd like to polish it up more and release it on the Google Play Store. To play it, you can view it on the Ludum Dare page or download the game executable directly.
|


Are you sure?
Are you sure you want to delete the simple model "A simple charfield"? All of the following related items will be deleted:
- Simple model: A simple charfield
Are you sure you want to delete the simple model "A simple charfield"? All of the following related items will be deleted:


{% extends "wiki/article.html" %} {% load wiki_tags i18n humanize %} {% load url from future %} {% block wiki_pagetitle %}{% trans "Delete" %} "{{ attachment.current_revision.get_filename }}"{% endblock %} {% block wiki_contents_tab %} {% if attachment.article == article %}
{% blocktrans with attachment.original_filename as filename %} The file may be referenced on other articles. Deleting it means that they will loose their references to this file. The following articles reference this file: {% endblocktrans %}
{% blocktrans with attachment.original_filename as filename %} You can remove a reference to a file, but it will retain its references on other articles. {% endblocktrans %}
{% endif %} {% endblock %}


Make sure you have Impala running on a cluster with the Common Data Model data loaded as described in https://github.com/OHDSI/CommonDataModel/tree/master/Impala.
Before running Achilles, create a database for the Achilles tables:
Download the Impala JDBC drivers from the Cloudera website, and install them in a local directory called ~/impala-drivers/Cloudera_ImpalaJDBC4_2.5.36.
In R, use the following commands to install the Impala development version of Achilles. I used Docker to get all the pre-reqs (and ran docker run -it --rm -v ~/impala-drivers/:/impala-drivers achilles_achilles bash
).
install.packages("devtools")
library(devtools)
install_github("tomwhite/SqlRender", ref="impala-timestamp")
library(SqlRender)
install_github("ohdsi/DatabaseConnector")
install_github("ohdsi/Achilles")
To run Achilles for a single, simple analysis, try:
library(Achilles)
connectionDetails <- createConnectionDetails(dbms="impala",
server="bottou01.sjc.cloudera.com",
schema="omop_cdm_parquet",
pathToDriver = "/impala-drivers/Cloudera_ImpalaJDBC4_2.5.36")
achillesResults <- achilles(connectionDetails, cdmDatabaseSchema="omop_cdm_parquet",
resultsDatabaseSchema="achilles", sourceName="Impala trial", runHeel = FALSE,
cdmVersion = "5", vocabDatabaseSchema="omop_cdm_parquet", analysisIds = c(1))
Have a look at the output:
The following is a more complex analysis:
achillesResults <- achilles(connectionDetails, cdmDatabaseSchema="omop_cdm_parquet",
resultsDatabaseSchema="achilles", sourceName="Impala trial", runHeel = FALSE,
cdmVersion = "5", vocabDatabaseSchema="omop_cdm_parquet", analysisIds = c(105))
This is how you can run a set of analyses, by exclusion:
allAnalyses=getAnalysisDetails()$analysis_id
subset1=setdiff(allAnalyses,c(110))
subset2=Filter(function(X) { X > 0 }, subset1)
achillesResults <- achilles(connectionDetails, cdmDatabaseSchema="omop_cdm_parquet",
resultsDatabaseSchema="achilles", sourceName="Impala trial", runHeel = FALSE, createTable = FALSE,
cdmVersion = "5", vocabDatabaseSchema="omop_cdm_parquet",
analysisIds = subset2)
Note that createTable
has been set to FALSE
so that the initial tables are not created, to save time.
Finally, run all of the analyses with:
achillesResults <- achilles(connectionDetails, cdmDatabaseSchema="omop_cdm_parquet",
resultsDatabaseSchema="achilles", sourceName="Impala trial", runHeel = FALSE,
cdmVersion = "5", vocabDatabaseSchema="omop_cdm_parquet")
You can uninstall packages with
If you want to delete all the Achilles results use the following:
Heel implementation for Impala
Currently only Achilles precomputations are fully developed for Impala. No work was done on the Heel component of Achilles


@ProviderType
public interface JobManager
| Modifier and Type | Interface and Description |
|---|---|
static class |
JobManager.QueryType
The requested job types for the query.
|
| Modifier and Type | Method and Description |
|---|---|
Job |
addJob(String topic,
Map<String,Object> properties)
Add a new job
If the topic is
null or illegal, no job is created and null is returned. |
Job |
addJob(String topic,
String name,
Map<String,Object> properties)
Deprecated.
|
JobBuilder |
createJob(String topic)
Fluent API to create, start and schedule new jobs
|
org.osgi.service.event.Event |
findJob(String topic,
Map<String,Object> template)
Deprecated.
|
Collection<Job> |
findJobs(JobManager.QueryType type,
String topic,
long limit,
Map<String,Object>... templates)
Return all jobs of a given type.
|
void |
forceRemoveJob(String jobId)
Deprecated.
|
Job |
getJob(String topic,
Map<String,Object> template)
Find a job - either scheduled or active.
|
Job |
getJobById(String jobId)
Return a job based on the unique id.
|
Job |
getJobByName(String name)
Deprecated.
|
Queue |
getQueue(String name)
Return a queue with a specific name (if running)
|
Iterable<Queue> |
getQueues()
Return an iterator for all available queues.
|
Collection<ScheduledJobInfo> |
getScheduledJobs()
Return all available job schedules.
|
Collection<ScheduledJobInfo> |
getScheduledJobs(String topic,
long limit,
Map<String,Object>... templates)
Return all matching available job schedules.
|
Statistics |
getStatistics()
Return statistics information about all queues.
|
Iterable<TopicStatistics> |
getTopicStatistics()
Return statistics information about job topics.
|
boolean |
isJobProcessingEnabled()
Deprecated.
This method always returns true
|
JobsIterator |
queryJobs(JobManager.QueryType type,
String topic,
long limit,
Map<String,Object>... templates)
Deprecated.
|
JobsIterator |
queryJobs(JobManager.QueryType type,
String topic,
Map<String,Object>... templates)
Deprecated.
|
boolean |
removeJob(String jobId)
Deprecated.
|
boolean |
removeJobById(String jobId)
Removes the job even if it is currently in processing.
|
void |
restart()
Restart the job manager.
|
Job |
retryJobById(String jobId)
Retry a cancelled job.
|
void |
stopJobById(String jobId)
Stop a job.
|
Statistics getStatistics()
Iterable<TopicStatistics> getTopicStatistics()
Queue getQueue(String name)
name
- The queue namenull
void restart()
Job addJob(String topic, Map<String,Object> properties)
null
or illegal, no job is created and null
is returned.
If properties are provided, all of them must be serializable. If there are non serializable
objects in the properties, no job is created and null
is returned.
A job topic is a hierarchical name separated by dashes, each part has to start with a letter,
allowed characters are letters, numbers and the underscore.
The returned job object is a snapshot of the job state taken at the time of creation. Updates
to the job state are not reflected and the client needs to get a new job object using the job id.
If the queue for processing this job is configured to drop the job, null
is returned
as well.topic
- The required job topic.properties
- Optional job properties. The properties must be serializable.null
if the job could not be created.Job getJobById(String jobId)
jobId
- The unique identifier from Job.getId()
null
boolean removeJobById(String jobId)
jobId
- The unique identifier from Job.getId()
true
if the job could be removed or does not exist anymore.
false
otherwise.Job getJob(String topic, Map<String,Object> template)
topic
- Topic is required.template
- The map acts like a template. The searched job
must match the template (AND query).null
Collection<Job> findJobs(JobManager.QueryType type, String topic, long limit, Map<String,Object>... templates)
type
- Required parameter for the type. See above.topic
- Topic can be used as a filter, if it is non-null, only jobs with this topic will be returned.limit
- A positive number indicating the maximum number of jobs returned by the iterator. A value
of zero or less indicates that all jobs should be returned.templates
- A list of filter property maps. Each map acts like a template. The searched job
must match the template (AND query). By providing several maps, different filters
are possible (OR query).void stopJobById(String jobId)
Job retryJobById(String jobId)
null
.jobId
- The job id.null
JobBuilder createJob(String topic)
topic
- Required topicCollection<ScheduledJobInfo> getScheduledJobs()
Collection<ScheduledJobInfo> getScheduledJobs(String topic, long limit, Map<String,Object>... templates)
topic
- Topic can be used as a filter, if it is non-null, only jobs with this topic will be returned.limit
- A positive number indicating the maximum number of jobs returned by the iterator. A value
of zero or less indicates that all jobs should be returned.templates
- A list of filter property maps. Each map acts like a template. The searched job
must match the template (AND query). By providing several maps, different filters
are possible (OR query).@Deprecated Job addJob(String topic, String name, Map<String,Object> properties)
null
or illegal, no job is created and null
is returned.
If properties are provided, all of them must be serializable. If there are non serializable
objects in the properties, no job is created and null
is returned.
A job topic is a hierarchical name separated by dashes, each part has to start with a letter,
allowed characters are letters, numbers and the underscore.
This method allows to specify a job name which should uniquely identify this job. If a job with
the same name is started on different instances, the job is still processed only once. However,
the topology api in combination with the leader selection provides a better way for
dealing with this situation and as jobs with name come with a heavy processing overhead
these should be avoided.
The returned job object is a snapshot of the job state taken at the time of creation. Updates
to the job state are not reflected and the client needs to get a new job object using the job id.
If the queue for processing this job is configured to drop the job, null
is returned
as well.topic
- The required job topic.name
- Optional unique job nameproperties
- Optional job properties. The properties must be serializable.null
if the job could not be created.@Deprecated Job getJobByName(String name)
null
@Deprecated JobsIterator queryJobs(JobManager.QueryType type, String topic, Map<String,Object>... templates)
findJobs(QueryType, String, long, Map...)
type
- Required parameter for the type: either all jobs, only queued or only started can be returned.topic
- Topic can be used as a filter, if it is non-null, only jobs with this topic will be returned.templates
- A list of filter property maps. Each map acts like a template. The searched job
must match the template (AND query). By providing several maps, different filters
are possible (OR query).@Deprecated JobsIterator queryJobs(JobManager.QueryType type, String topic, long limit, Map<String,Object>... templates)
findJobs(QueryType, String, long, Map...)
type
- Required parameter for the type: either all jobs, only queued or only started can be returned.topic
- Topic can be used as a filter, if it is non-null, only jobs with this topic will be returned.limit
- A positive number indicating the maximum number of jobs returned by the iterator.templates
- A list of filter property maps. Each map acts like a template. The searched job
must match the template (AND query). By providing several maps, different filters
are possible (OR query).@Deprecated org.osgi.service.event.Event findJob(String topic, Map<String,Object> template)
getJob(String, Map)
topic
- Topic is required.template
- The map acts like a template. The searched job
must match the template (AND query).null
@Deprecated boolean removeJob(String jobId)
removeJobById(String)
jobId
- The unique identifier as found in the property JobUtil.JOB_ID
.true
if the job could be cancelled or does not exist anymore.
false
otherwise.@Deprecated void forceRemoveJob(String jobId)
removeJobById(String)
removeJob(String)
with the exception that it waits
for a job to finish. The job will be removed when this method returns - however
this method blocks until the job is finished!jobId
- The unique identifier as found in the property JobUtil.JOB_ID
.@Deprecated boolean isJobProcessingEnabled()
Copyright © 2014 The Apache Software Foundation. All rights reserved.


5 Days of Awesome Wallpapers: Fans and Enthusiasts
by TechSpot StaffA great wallpaper can start your day off on a good mood. There’s certainly no shortage of places to find something to revitalize your desktop, whether you are into beautiful landscapes, minimalism, abstract illustrations, or eye-catching designs and patterns. With the kind of hours we spend looking at our computer and smartphone screens, why not make them a little prettier?
TechSpot Wallpaper Week - 3rd edition
This is the third edition of Wallpaper Week on TechSpot which we've broken down in five categories. We're refreshing all content, keeping the best high-resolution wallpapers from previous editions and adding a ton of new content, one category per day Monday thru Friday. The idea is to share astonishing wallpapers gathered from around the web by members of the TechSpot staff. Most of these are good to use with your 4K desktop, laptop display or smartphone.
Today's theme are brand fan, enthusiasts and cult/pop culture themed wallpapers, also available as .zip file download.
- Day 1: Geometric and Architectural Wallpapers
- Day 2: Nature Photography and Amazing Scenes
- Day 3: Cars and Sports Wallpapers
- Day 4: Minimalist and Abstract Wallpapers
- Day 5: Fans and Enthusiasts
TechSpot
By Julio Franco
Resolution: 3840 x 2160
Works on: Desktop
Stormtrooper minifigure
Photo by Daniel Cheung
Resolution: 5893 x 3934
Works on: Desktop
Iron Man graffiti
Photo by Marjan Blan
Resolution: 5760 x 3840
Works on: Desktop
Deployment Fortnite
Found on Xtrafondos
Resolution: 3840 x 2160
Works on: Desktop
Bewitching Janna
Found on LOL Wallpapers
Resolution: 1920 x 1059
Works on: Desktop
Gaming Keyboard
Photo by John Petalcurin
Resolution: 5860 x 3981
Works on: Desktop
Black electric guitar
Found on freestocks.org
Resolution: 5472 x 3648
Works on: Desktop
Deadpool costume
Photo by Ayo Ogunseinde
Resolution: 3000 x 4500
Works on: Mobile
Sea of Thieves
Found on WallpaperHub
Resolution: 4500 x 3000
Works on: Desktop
Wolverine
Found on UHD Pixel
Resolution: 3840 x 2160
Works on: Desktop
Neon
Found on WallpaperHub
Resolution: 4500 x 3000
Works on: Desktop
Game of Thrones
Found on Wallpaper Access
Resolution: 3840 x 2160
Works on: Desktop
Fortnite
Found on Pinterest
Resolution: 3840 x 2160
Works on: Desktop
Desperados III
Found on GameRanx
Resolution: 3840 x 2160
Works on: Desktop
Fallout
Found on Get Wallpapers
Resolution: 3840 x 2160
Works on: Desktop
Minecraft
Found on imgur
Resolution: 3840 x 2160
Works on: Desktop
LOLing Skeletor
Resolution: 2560 x 1440
Best for: Desktop
Lumen P3
Photo by Rebecca Oliver
Resolution: 5184 × 3456
Works on: Desktop
Retro Star Wars
Resolution: 4576 x 3056
Works on: Mobile - 4K
Northern Front Swain
Found on LOL Wallpapers
Resolution: 5212 x 3075
Works on: Desktop
Kratos
Found on WallpapersCraft
Resolution: 3840 x 2160
Works on: Desktop
Tom Clancy's Rainbow Six
Found on WallpapersCraft
Resolution: 3840 x 2160
Works on: Desktop
Minecraft #2
Found on WallpapersCraft
Resolution: 2560 x 1440
Works on: Desktop
TechSpot #2
By Eliot Orejuela
Resolution: 3840 x 2160
Works on: Desktop
Nvidia logo art
Found on Nvidia
Resolution: 2560 x 1600
Works on: Desktop
Fortnite Map
Found on Wallpaper Abyss
Resolution: 7192 x 3912
Works on: Desktop
Futurama Minimal
Photo by Kamil S
Resolution: 5184 x 3456
Works on: Desktop
Super NES Controller
Photo by Kamil S
Resolution: 5184 x 3456
Works on: Desktop
Printed Circuit Board
Resolution: 2160 x 3840
Works on: Mobile - 4K
90's CDs
Photo by Natalie Perea
Resolution: 2048 × 1536
Works on: Desktop
Daft Punk with a Delorean
Resolution: 2560 x 1440
Works on: Desktop
Retro Radio
Photo by Shawn Knight
Resolution: 4576 x 3056
Works on: Desktop - 4K
Three red dices
Photo by Fabian Reitmeier
Resolution: 6000 x 4000
Works on: Desktop
Guy Fawkes and Star Wars Stormtroopers
Photo by Jon Tyson
Resolution: 3773 x 2146
Works on: Desktop
Ringwraiths From Lord of the Rings
Resolution: 3840 x 2160
Works on: Desktop
AMD Radeon RX 480 Minimal Art!
Found on DeviantArt, by MaverickGeek
Resolution: 3000 × 1688
Best for: Desktop - 4K
2020 Tesla Roadster
Found on HD Car Wallpapers
Resolution: 3840 x 2160
Works on: Desktop
TechSpot #3
By Eliot Orejuela
Resolution: 2560 x 1440
Works on: Desktop
Minecraft #3
Found on WallpapersCraft
Resolution: 2560 x 1440
Works on: Desktop
DragonBall Super
Found on DeviantArt by AubreiPrince
Resolution: 3840 x 2160
Works on: Desktop
Retro Apple Logo
Found on Reddit
Resolution: 3840 x 2160
Works on: Desktop, Mobile
Triforce
Found on Flickr, "SierpinskusIlluminantus"
Resolution: 6400 x 4267
Best for: Desktop - 4K
Batman Emblem
Found on Set as Wall
Resolution: 5500 × 3094
Works on: Desktop
Clone Wars Last Stand
Found on WallpapersWide by AndiBaze
Resolution: 3554 x 1999
Works on: Desktop
Rick and Morty Wallpaper
Found on Word of the Nerd
Resolution: 5120 x 2880
Works on: Desktop
Play it up
Photo by Hardik Sharma
Resolution: 1536 × 2304
Works on: Mobile
Ice Dragon
Found on Wallpaper Abyss by Ghulam Rouhan Samdani
Resolution: 3840 x 2160
Works on: Desktop
Surface Studio 2
Found on WallpaperHub
Resolution: 3593 x 2200
Works on: Desktop
Green Gameboy
Photo by Mike Meyers
Resolution: 6000 × 6000
Works on: Desktop, Mobile
Work Hard
Photo by Ionut Necula
Resolution: 2714 × 4086
Works on: Mobile
Evolution of the Mac
Resolution: 3840 x 2400
Works on: Desktop
Halo Infinite
Found on WallpaperHub
Resolution: 3840 x 2160
Works on: Desktop
1989 Batmobile
Found on Wallpaper Abyss
Resolution: 4096 x 2304
Works on: Desktop
Blurred Mario Box
Photo by Saulo Mohana
Resolution: 6016 × 4000
Works on: Desktop, Mobile


Pursuing Bachelor's Of Technology in Civil Engineering
Interested in programming and have done many projects in C, CPP, PHP, and in Web Development. And this website is a small part of it.
We've done CS50 psets which had the application of C, HTML5, CSS, PHP, JAVASCRIPT, SQL, Django.
One of them is pset8 MASHUP, a project of CS50.
Currently, we are working on CS75(building dynamic website) based on PHP, JS, JSON, AJAX. This website is a part of it too. We've also made several websites for college clubs and are planning to release a few apps soon. We're also working on VNIT's Student Portal


| DOSE: |
76 mg |
oral |
DiPT |
(powder / crystals) |
I wanted to write an experience report because I have experienced DiPT one time, and although I have only experienced it once, it was nothing at all like the other experience reports that Iíve read. Its similarities to DPT and the nausea I've experienced the entire trip seem to be different than what others have reported.
After remarkable results with DPT, I was looking for something to investigate that was similar in nature. So when I tried DiPT, I was not disappointed, but there were unfavorable aspects associated with my personal journey.
At 12:36:29 p.m., I swallowed a capsule containing 76 mg. of DiPT on an empty stomach. The effects came on almost instantly, after maybe fifteen minutes. The most distinguished of these effects was the lowering of pitch, and the sharpness of vision. I was watching a film, The Punisher, and the clock in my living room started to ring. I was astonished with how unexpectedly strange it sounded. The Lord Of The Rings movie came on, and then everything started sounding weird. At about 1:15:00 p.m., I went on the computer to start to listen to music. I put on some Static X, and at first it didnít sound good, but then I guess my brain started to just take the music apart somehow. All that I started hearing was mechanical noises as I stared into the monitor. Then I began to feel the monitor come out at me and start to pull me into it, like as if it were the gate to an abyss and it was opening up to me. It was a lot like DPT.
At 2:00:00 p.m., I went to my room because I was feeling some nausea accompanied with very mild diarrhea. I lied down on my bed and enjoyed the DPT-like visuals and the sounds of Type-O-Negative. There was just a spaced out stoning throughout the whole trip that I believe contributed to the nausea I felt. The nausea continued throughout the rest of the trip, as well as the slight diarrhea.
I had some very wicked auditory hallucinations. Like growling sounds and things did get scary at times. The visuals were much like DPT, but not as good (at this dose). The sounds were very demonic, and very awesome. I was amazed to put Marilyn Mansonís Holy Wood and The Golden Age Of Grotesque albums into the stereo and find out that their sound seems to be made to listen to while on DiPT! Especially Holy Wood. He is a psychedelic guide that consistently blows my mind. Nothing else sounded good until I listened to these two albums.
At 2:00:00 a.m.: Overall, I discovered that with such a high dose, came a lot of nausea. I did not feel anything positive or negative conscerning a body high. For me, this was like DPT around the edges, and I relate to the stoned/drunk feeling that has been reported. And, of course, the auditory effects. If I wanted to experience this drug only for the sound, then a lower dose is all that is necessary. But, no doubt that if a bigger dose would be ingested, it would result in taking apart Universe. For the future, I look forward to trying this valuable drug with the freebase method in combination with the Holy Wood album.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Honda affiliate to develop advanced Israeli engine
Musashi Seimitsu signs deal with Aquarius Engines to develop revolutionary linear engine of the future.
by Abigail Klein LeichmanAquarius Engines of Rosh Ha’ayin, Israel, has signed a deal with Honda Motor Corporation affiliate Musashi Seimitsu to co-develop its revolutionary linear engine.
The 10-kilo Aquarius Engine can be used as an onboard power generator in a vehicle or as a stand-alone electricity generator.
Unlike most conventional engines made of hundreds of parts, the Aquarius Engine has just 20 components and one moving part. The lightweight streamlined design makes it inexpensive and efficient with little need for maintenance.
The Japanese global auto-parts manufacturer has also invested capital in the Israeli company.
“Musashi sees clear synergy with Aquarius Engines and their new system which provides electrical mobility and charging while braking. We will begin our relationship by co-developing the Aquarius electricity systems to power 5G base stations, supply electricity to remote areas, and then progress to provide energy solutions for other markets, including the autoindustry,” said Hiroshi Otsuka, CEO, Musashi Seimitsu.
“Together with partners like Musashi Seimitsu we are beginning to realize our dream of supplying accessible, affordable electricity whenever and wherever it is needed,” said Gal Fridman, chairman of Aquarius Engines.
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Israel’s water quality to be analyzed by machine vision
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Encouraging results for new squamous cell cancer treatment
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Israeli tech helps Argentinian footballers train for Tokyo
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Moovit and Waze Carpool join forces in US, Brazil, Mexico
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2 albums from bands led by Israelis get Grammy nominations
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Israeli solar power generator going up to Space Station
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Israeli project could win global Peace & Sport Award
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Medical-device firm Lumenis to be sold for $1.2b
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limits.hh File Reference
(Revision: 3516)
#include <climits>
#include <cfloat>
Go to the source code of this file.
Namespaces | |
| namespace | Gecode |
| namespace | Gecode::Limits |
| namespace | Gecode::Limits::Int |
| namespace | Gecode::Limits::Set |
Variables | |
| const int | Gecode::Limits::Int::int_max = ((INT_MAX)>>1) - 1 |
| Largest allowed integer value. | |
| const int | Gecode::Limits::Int::int_min = -int_max |
| Smallest allowed integer value. | |
| const double | Gecode::Limits::Int::double_max =<PHONE_NUMBER>740991.0 |
| Largest double that can exactly be represented. | |
| const double | Gecode::Limits::Int::double_min = -9007199254740991.0 |
| Smallest double that can exactly be represented. | |
| const int | Gecode::Limits::Set::int_max = ((INT_MAX)>>2) - 1 |
| Largest allowed integer in integer set. | |
| const int | Gecode::Limits::Set::int_min = -int_max |
| Smallest allowed integer in integer set. | |
| const unsigned int | Gecode::Limits::Set::card_max = int_max-int_min+1 |
| Maximum cardinality of an integer set. |


MONTY PYTHON'S MEANING OF LIFE A film review by Andrew Hicks Copyright 1996 Andrew Hicks / Fatboy Productions
(1983) *** (out of four)
THE MEANING OF LIFE stands in this reviewer's opinion as the best of the three Python films, a collection of sketches tracing the journey and meaning behind this life we all lead. Pythonites John Cleese, Graham Chapman, Terry Gilliam, Eric Idle, Terry Jones and Michael Palin once again play multiple roles, male and female, beginning with a birth sequence and continuing into a family with at least fifty children.
We find out why they have fifty children during an elaborate production number detailing the Catholic view of sperm as sacred. (Isn't it though?) We also find out what else Catholics hold sacred in the next sequence, with Father Cleese teaching a class all about sex. He goes far beyond the biological details the rest of us were subjected to in high school, favoring instead a demonstration with his wife. In the grand tradition of school, though, even this doesn't interest the kids, who continue staring into space, passing notes and falling asleep while Cleese gets it on with his wife... For those of you who still harbor any doubt, this movie is rated R.
We continue through the journey with sequences handling the meaning of war, middle age and later death itself, in which the Grim Reaper claims the lives of six dinner party guests and takes them to heaven. Apparently, we will all be subjected to an eternity of lounge singers doing production numbers about Christmas. (Before the Monty Python troop told us differently, this scenario was known as hell.) Then there's the most memorable portion of the film, where the world's fattest man comes to dine and regurgitate in a restaurant. How fondly I remember the day we filmed that scene.
Preceding the movie itself is a short film about a small, family-run accounting firm that's been taken over by faceless bureaucrats. But they're taking it back, by force. Though this piece doesn't star any of the Python troupe, it definitely fits in with the rest of THE MEANING OF LIFE, and is later reprised in the feature presentation itself. As with LIFE OF BRIAN, this movie isn't for the easily-offended, but for those of us with strong stomachs (and I've definitely exercised mine enough over the years), it's mostly a very funny movie. Sure, it has its misses too, but so does any work attempting comedy. This review, for example...
--
Visit the Movie Critic at LARGE homepage at http://www.missouri.edu/~c667778/movies.html Serving America For Over 1/50 of a Century!
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


The Department of Technology Promotion and Coordination
University of Computer Studies
Fourth Year (B.C.Teach)
Final Examination
Computer Architecture (CT-404)
September, 2016
Zone III
| Number of disks (recording surface) | 14(27) |
| Number of tracks per recording surface | 4925 |
| Number of sectors on all recording surfaces | 17,755, 614 |
| Storage capacity (formatted) of disk drive | 9.09 GB |
| Disk-rotation speed | 5400 rpm |
| Average seek time | 11.5 ms |
| Internal data-transfer rate | 44 to 65 MB/s |
| Calculate the block size B and the average block access time tB. |


The Xots builder scans Domino databases for java classes that extends org.openntf.domino.xots.XotsBaseTasklet.
The builder stores information about these classes in the Database Icon Note in a multi-valued Text item called $Xots. Each tasklet has the format classname;enabled. The image below shows an example of two enabled and one disabled tasklets.


Gaurang Shetty
Chief Innovation Catalyst
Raj Dhiravani
Innovation Catalyst-IT
Prerak Gala
Operations Executive
Bhavna Pandya
Innovation Catalyst-Biology
Amit Yadav
Innovation Catalyst-Electronics
Hetal Mundra
Community Architect
Harsh Vitra
Web/Software Developer
Savin Shetty
Events Executive
Sangram Chavan
Office Admin


Announcements!
Winner of Asphalt
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Go to Event 1
Webinar dates
Amrita Center for Cyber Security Systems and Networks, located at the Amrita Vishwa Vidyapeetham's Amritapuri Campus, promotes partnership between industry, academia and the government to foster...
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Go to event 3
Cyber Security
The Technology Information, Forecasting and Assessment Council (TIFAC) of the Department of Science and Technology, Government of India, launched Mission REACH (Relevance and Excellence in Achieving New Heights in Educational Institutions) in 2000 as a part of its Technology...
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THE TRUTH ABOUT CATS & DOGS A film review by Christopher Null Copyright 1996 Christopher Null
What happens if you meet someone with whom you have almost everything in common, you find yourself falling for them, but the sparks of romance just don't seem to fly on a physical level? Maybe you need an extra body, and you can just play Cyrano in the background until that fateful moment when everything is revealed with hilarious results.
Such is the case in THE TRUTH ABOUT CATS & DOGS, a pleasantly funny romance that takes another twist on the Cyrano tale, by taking two very different women (Janeane Garofalo and Uma Thurman) and pitching them at one guy (English actor Ben Chaplin).
In this version, Garofalo plays Abby Barnes, a veterinarian who hosts the titular pet-oriented talk show. When Brian (Chaplin) calls in about his new dog's wacky antics, he is immediately smitten by Abby's voice. And when Abby describes herself, it isn't is a 5'1" brunette, but as Abby's model neighbor Noelle (Thurman)--a 5'10" blonde. A simple wig is out of the question, so Abby opts to "use" Noelle's body as a surrogate, wooing Brian on the phone while Noelle exhibits her physical charms. Meanwhile, Brian unwittingly falls for both of them.
The sweetness factor of THE TRUTH ABOUT CATS & DOGS is almost unbearable--full of oohs and aahs, largely due to literal doses of humor relating to the parade of animals that punctuates the film. Audrey Wells' debut script is also a mushy affair, but it works well enough, even though it feels a lot longer than it really is (95 minutes). In fact, everything here would work a lot better if all the best jokes hadn't been beaten to death in the rotten trailer for an otherwise pretty good movie.
That aside, Michael Lehmann's direction (he also directed HEATHERS) is fine for this story, but the real kudos go to Garofalo, who is one of the funniest comedic actresses working today and proves it here, and, surprisingly, to Chaplin, who bored me to tears in last year's period drama FEAST OF JULY, but turns out to be great in the comic medium.
I'm really pleased that the theme of romance transcending physical beauty is being explored again, but the only problem is that Garofalo is plenty cute in her own right, and in my opinion, can hold her own against the supermodels any day. But it's still great to see her get the guy.
RATING: ***1/2
\-------------------------------\ |* Unquestionably awful | |** Sub-par on many levels | |*** Average, hits and misses | |**** Good, memorable film | |***** Perfection | \-------------------------------\
-Christopher Null /<EMAIL_ADDRESS>-Screenwriter-Producer / Novelist-Publisher -Visit the Movie Emporium at http://www.filmcritic.com -and e-mail requests to join the movie review mailing list
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Dennis C. Neuman
WAUNAKEE, Wis. - Dennis C. Neuman, age 84, passed away on Friday, November 15, 2019 at the Waunakee Manor.
He was born on August 16, 1935 in Schuyler, NE the son of Arthur and Tillie (Posver) Neuman. When Dennis was a teenager his family moved to Wisconsin from Nebraska. Dennis entered into the United States Army in 1955 and served at Ft. Lewis, Washington until 1957.
On February 9th, 1957 he was united in marriage to Kathleen Zweifel at the Swiss United Church of Christ in New Glarus. Dennis worked at the Janesville Assembly Plant for General Motors until retiring in 1997.
He loved to play cards, especially cribbage, enjoyed hunting and wildflowers, and was an accomplished woodworker and wood turner. Dennis also loved to watch his grandchildren with their school and sport activities.
Dennis is survived by his son Scott (Jenni) Neuman, daughter Michelle (Randy) Naatz, grandchildren Ashley (fiancé Michael Edwards) Naatz, Zachary Naatz, Samuel Neuman, and Lilly Neuman. He is further survived by brothers Donald (Patricia) Neuman and Larry Neuman.
He was preceded in death by his parents, wife Kathleen, brother Robert, and father-in-law and mother-in-law Clarence and Eva Zweifel.
A private family inurnment service will be held at the Swiss Cemetery in New Glarus.
The Zentner-Beal Funeral Home of New Glarus is assisting the family.


public interface IDataQueryPage extends IQueryPage
Represents the definition of a query page for data queries. Results from a data query can be thought of as a two-dimensional array of returned data values. The first dimension represents a tuple comprising the fields or computed values specified in the projection clause of a query. The second dimension represents multiple occurrences of the tuple, or rows of data.
Clients will generally process a data query page by iterating through each of the data rows. For each data row, clients can access the data values using either index-based or name-based lookup.
Data query results are self describing; each field in a row is assigned a name and knows its type. Clients can use this for introspecting the shape of the query result and retrieving values from the data rows.
IQueryPage
,
IDataRow
,
IDataField
| Modifier and Type | Field and Description |
|---|---|
static IDataQueryPage |
EMPTY_RESULT
A data query page with no results.
|
| Modifier and Type | Method and Description |
|---|---|
java.util.List |
getDataFields()
Answers the list of field descriptions that compose a row of data.
|
IDataRow |
getRow(int position)
Answers the row of result data at the 0-based position, relative to the
size of this page.
|
getNextStartPosition, getResultSize, getSize, getStartPosition, getToken, hasNext
static final IDataQueryPage EMPTY_RESULT
IDataRow getRow(int position)
position
- non-negative zero-based index of the request row; must be less
than the size of this pagenull
java.lang.IndexOutOfBoundsException
- if the index is out of range (index < 0 || index >=
size()).java.util.List getDataFields()
IDataField
- will never be null
or
empty


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The Invention of the Internet - The True Story of the Internet
The creation of the Internet starts in the 1950’s with psychologist and professor JCR Licklider. Licklider was a professor at MIT whose vision was that computers would allow us to communicate better and share ideas. At the time, computers were something you would see in science fiction movies. Some even thought that flying saucers and ray guns were more plausible than the creation of something that could bring computers together.
Computers were not designed to communicate. The first computers were the size of a room and you were lucky to find one in a city.
In the late 1950’s, there was a shift away from the need of computers to communicate to getting a man on the moon. This came after America saw the need to beat Russia in getting there. The Advanced Research Project Agency, ARPA, was in charge of the space program. However, it was soon replaced by NASA.
Not long after, an MIT graduate student named Leonard Kleinrock began to bring light to Licklider’s vision- that communication between multiple computers could very well be a reality.
It wasn’t until the 1960’s, the traditional telephone circuit was seen as an issue. The telephone used a technology called circuit switching, in which a connection was made between two phones to allow a call to be made. Even if no one was speaking on the line, the connection still existed. A major flaw with this system was that if the connection was broken, a call could not be made or was disconnected. This became a matter of national security. An indestructible telecommunication network needed to be created.
Paul Baran was hired to solve this problem. He came up with the idea of a “fishnet network” in which if a connection was ever lost, there would already be another existing connection ready to take its place. The only problem was, this was just an idea and he did not know how to implement such a network. He later came up with the idea of “hot potato routing” inspired by mice in a maze. The maze could be seen as a network, and the mice inside could be seen as packets of information trying to find their way through, trying to get to their next/final destination. With this idea, Baran had successfully come up with a model of a packet switching network. And in 1962, there was enough proof that a reliable internet network could be created. It would just have to wait 7 years to be implemented.
In 1966, ARPA scientist Bob Taylor had a problem. He needed separate terminals to sign into different time-shared computers. Time-sharing was a technology in which a user would sit a terminal, type in a question and inquiry and get an immediate response. At this time, computers were still unable to talk to each other and he would have to move from terminal to terminal to get his work done. But he thought that one terminal could be suffice if they were all on a common network. Thus, came the idea of resource sharing.
Taylor was given one million dollars to make a computer network a reality. He recruited Larry Roberts for the job. Robert’s build the first experimental connection between two computers at MIT. He had learned about packet switching from Kleinrock. Robert’s soon came up with the idea to build minicomputers to form a network. Each of those minicomputers would be tied to a time-shared computer. With that, each only had to learn how to work with the standard minicomputer.
The first minicomputers became known as Interface Message Processors or IMP’s. Robert’s plan was to network 4 identical IMP’s each tied to a different computer. With this being nothing more than a trial, no one wanted to bid on the project. That was until a team from BBN headed by Frank Heart came into the picture. And by the end of 1969, all four computers were connected.
But now what? There was not much to do on the ARPA net other than transfer data files. That was until the first killer app came about- email.
Invented by Raymond Tomlinson, email started out as nothing more than a project he was doing on his own time. Its original purpose was to have a single computer send messages to someone else on the same time-shared computer. He then thought about the idea of being able to send files between multiple computers on a network. Without him, email (and the iconic @ sign to signify a user being “at” a computer), might not be what it is today.
Fast forward to 1972, ARPA net still had less than 25 sites online. Its team members were hopeful that would change with their upcoming International Conference on Computers and Communications. This would be the coming out party for networks, a way for people to get interested in the Internet. Bob Metcalfe was put in charge of putting together a list of what computers today, and he was able to find 19 different things. At the time, this was an incredible amount. Something that was once only perceived as science fiction now had the ability to do 19 different things. But it was time to put the network to the test.
Executives from AT&T were at that conference and they wanted to see what all the hype was about. Metcalfe would be the one to give a live demo of what the network could do. He fired up the computer, eager and ready to go. But then, it crashed. This may have turned off AT&T to the idea of networking, but not to the rest of the world.
The development of networks took off after the conference. In 1973, Bet Surf and Bob Khan established a set of rules known as the TCP/IP protocol that would allow different computer networks to pass packets back and forth. The protocol would allow different networks to interconnect using a technology called Gateways. All of this put together solve the issue of the need to create a single internet; and by 1983, TCP/IP was a universal standard.
In June of 1992, Congress based a bill that took the Internet into the public, which President bush then signed into law. However, the Internet was not user friendly at this point. You had to be a computer nerd to understand how to use it. But that all changed thanks to Tim Burners Lee, a physicist at the CERN Research Institute of Switzerland. He developed a software that allowed users to more easily follow threads of information. This became known as the World Wide Web.


Andrea enjoys writing test driven software and learning all there is to know about Object Oriented Programming!
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JSON Magic: The Gathering Sealed Deck Card Generator Gathering Magic IMPORTER
This page imports card data from the MTG Salvation spoilers, WotC card image gallery spoilers, and mtgjson.com to help create the Sealed Deck Card Generator pages for each release.
- 5-Apr-2021: Added import options.
- 26-Feb-2017: Rewritten to use es6 instead of es5/backbone/jQuery.
- 23-Jul-2016: Combined the settings and exceptons file into one.
- 28-Jan-2016: Updated to add/use mtgenIds and associative arrays on "cards". Added
- 4-Jul-2015: New version created.
- 11-Dec-2015: Paths adjusted for MVC5 layout.
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I-League: Punjab faces an uphill task against CCFC
The latter eyes its second consecutive victory
by PTIDefending champion Chennai City FC will look to notch up its second consecutive win of the season when it takes on Punjab FC in an I-League clash here on Tuesday.
Chennai City has played only one match and won 1-0 at home against TRAU FC. The team will look to step up a few gears to find the killer instinct in front of the goal.
Learning curve
Head coach Akbar Nawas said: “We have to improve our game in every match we play. It’s always a learning curve for us.”
Punjab FC has just one point from its opening two fixtures, having lost 3-0 to Churchill Brothers, followed by a 1-1 draw against East Bengal at home.
It led East Bengal for most part of the game, only to be denied three points by a late equaliser from Juan Mera.
Looking forward to hosting the holder, head coach Yan Law said, “we had a fantastic game against East Bengal and we dominated the entire game. We created many chances and unfortunately could not convert them.
“The late equaliser wasn’t expected as our defence was rock solid in the entire game.”
“The boys are confident about the next game against Chennai City FC. We are not going to make the same mistakes we made in the previous matches.”


RANDOM HEARTS
Reviewed by Harvey Karten Columbia Pictures Director: Sydney Pollack Writer: Kurt Luedtke, Novel by Warren Adler, adapted by Darryl Ponicsan Cast: Harrison Ford, Kristin Scott Thomas, Charles S. Dutton, Paul Guilfoyle, Dennis Haysbert, Bonnie Hunt, Richard Jenkins
Trivia quiz. Two strangers, both married, meet at a train station and find themselves drawn into a short but poignant romance. Rachmaninoff's Second Piano Concerto pumps up the amour. You're on the money if you named David Lean's 1945 "Brief Encounter," a picture that deserved four stars then but would hardly rate the same good fortune in 1999. The time is over for this time of languorous movie. Adultery then was delicious. Today it's shrugged off so much that a sitting president actually furthered his popularity by engaging in hanky panky outside his marriage. Trying to capitalize on the film and literary conventions of a bygone age may occasionally work, but in "Random Hearts," with its all-star troupe of lead performers and a superior bunch of supporting people, it does not.
I was ready to attack Todd McCarthy for what I considered his pigeonholing of movie audiences, but now I'm not so sure. McCarthy said of this film in "Variety" that it is "an ideal rainy day matinee attraction for well-to-do ladies of a certain age...pic's sobriety and deliberate pace, combined with an utter lack of allure for anyone under about 40, spells a short theatrical visit." In fact now I'd as soon amend Variety's opinion. An online critic whose 81-year-old grandmother recently saw "American Beauty" called it the best movie she'd seen this decade, making me wonder whether even the old- timers want to return to the stodgy, torpid, sentimental fantasies of yesteryear.
Director Sydney Pollack--a fine actor who justifiably injects himself into the action as a political campaign adviser--does give the picture a particular spin by throwing together people of wholly different classes as William Friedkin did in 1971 by contrasting a down-to-earth cop played by Gene Hackman with a sophisticated, classy criminal played by Fernando Rey. Harrison Ford inhabits the role of an obsessive cop, Internal Affairs Sgt. Dutch Van Den Broeck, who is as consumed by the search for truth about his wife as he is about tracking down corrupt policemen. You could never imagine that sparks would fly between him and the patrician congresswoman, Kay Chandler (Kristin Scott Thomas), because Chandler not only is a Republican accustomed to hanging out in upper-middle-class circles but seems only vaguely preoccupied with learning the truth about her straying husband.
What brings the two distinct personalities together is a plane crash that claimed the lives of Dutch's wife Peyton (Susanna Thompson) and Kay's roving husband, Cullen (an almost unrecognizable Peter Coyote). Both Kay and Dutch were unaware that their spouses were traveling together to continue their fling in Miami, but the discovery that they occupied seats 3A and 3B on a plane that neither was supposed to be taking causes diverse reactions by their surviving partners. While Dutch is determined to find out every detail of the tryst, Kay is concerned that the news would hurt her campaign for the next session of Congress.
As in virtually all romantic dramas, parties are kept apart as long as possible. In this case, the obstacle that keeps Kay and Dutch strangers is Kay's unwillingness to get close to the police officer for fear that the public will gossip and because she is in denial about her husband's affair. When they finally lunge at each other animalistically in a car, the thrust is far from believable, nor do the two exhibit much chemistry for the remainder of this overly long, albeit soberly designed and produced movie. While Pollack is interested in comparing the two careers--Kay's campaign and the kitschy ways her advisers tell her to speak to the public on TV vs. Dutch's blow-by-blow activities as a cop--the scenes involving Dutch's trailing of a guy he suspects of murder seem out of keeping in a film of this nature.
Harrison Ford does convince us of his determination to get at the truth while Kristin Scott Thomas's is best at showing her change from uptight, contained patrician to a much softer, more compassionate human being. We learn at least one additional detail that we hope we never have to experience: that when the immediate family are summoned to the airport to identify their loved ones after a crash, they do not see the actual bodies but rather a transmission of the head on a TV screen.
Rated R. Running Time: 133 minutes. (C) 1999 Harvey Karten
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Start: 01/Sep/2016
Title:
End: 05/Sep/2017
This is my task
Start: 01/Sep/2016
Title:
End: 05/Sep/2017
This is my task
Start: 01/Sep/2016
Title:
End: 05/Sep/2017
This is my task
Start: 01/Sep/2016
Title:
End: 05/Sep/2017
This is my task
Create a New Task
+


Thenable object (an object with then
method)
thenable/is
Confirms if given object is a thenable
const isThenable = require("type/thenable/is");
isThenable(Promise.resolve()); // true
isThenable({ then: () => {} }); // true
isThenable({}); // false
thenable/ensure
If given argument is a thenable object, it is returned back. Otherwise TypeError
is thrown.
const ensureThenable = require("type/thenable/ensure");
const promise = Promise.resolve();
ensureThenable(promise); // promise
ensureThenable({}); // Thrown TypeError: [object Object] is not a thenable object


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