vignettes/Optimal-trial-design.Rmd
Optimal-trial-design.Rmd
library(RMSTdesign)
We use a simple example to discuss different criteria for selecting an optimal trial and how to do it. In some cases the optimal trial design may be the one that has the smallest sample size, regardless of duration; in other cases it may be the one with the shortest duration, regardless of sample size, and in other cases, you may be looking for a balance between the two.
We consider planning a trial where we expect survival time distributions in both the control and treatment groups follow exponential distributions, which implies proportional hazards. We expect the median survival time in the control group to be 3 years and the new treatment to reduce mortality risk by 33% (hazard ratio = 0.67). In the disease we are studying, 3 years is a clinically important landmark, so we choose \(\tau\)= 3 years. We plan to do a one-sided test, and we want to estimate the sample size to achieve 80% power with a one-sided Type I error of 0.025.
con<-survdef(times = 3, surv = 0.5)
trt<-survdef(haz = 0.67*con$h(1))
plotsurvdef(con, trt, 5)
We can consult Table 2 to get a rough estimate of the number of patients we will need for a hazard ratio of 0.67 with \(\tau\) equal to median survival in the control group. We will need 552 patients (276 in each arm) to achieve 80% power. This estimate doesn’t take into account administrative censoring. This code obtains the same result.
RMSTpow(con, trt, k1 = 0, k2 = 3, tau = 3, power = 0.8)
#> $n
#> [1] 552
#>
#> $powerRMST
#> [1] 0.8008182
#>
#> $powerRMSTToverC
#> [1] 0.8008182
#>
#> $powerRMSTCoverT
#> [1] NA
#>
#> $powerLRToverC
#> [1] 0.8708721
#>
#> $powerLRCoverT
#> [1] NA
#>
#> $powerLRtauToverC
#> [1] 0.8708721
#>
#> $powerLRtauCoverT
#> [1] NA
#>
#> $pKME
#> [1] 1
The trial with all patients followed to time \(\tau\) is optimal in terms of minimal sample size. Also, you are sure to be able to estimate RMST difference at time \(\tau\) because no patients will be censored before time \(\tau\). Note that extending followup time beyong \(\tau\) provides no benefit in terms of either power or pKME (the probability RMST difference will be estimable with the Kaplan-Meier estimator), though it does increase the power of the standard log-rank test calculated with all avaiable follow-up (the quantity powerLRToverC
in the output below).
RMSTpow(con, trt, k1 = 0, k2 = 10, tau = 3, power = 0.8)
#> $n
#> [1] 552
#>
#> $powerRMST
#> [1] 0.8008182
#>
#> $powerRMSTToverC
#> [1] 0.8008182
#>
#> $powerRMSTCoverT
#> [1] NA
#>
#> $powerLRToverC
#> [1] 0.9908296
#>
#> $powerLRCoverT
#> [1] NA
#>
#> $powerLRtauToverC
#> [1] 0.8709119
#>
#> $powerLRtauCoverT
#> [1] NA
#>
#> $pKME
#> [1] 1
We may also be interested in the trial design with the shortest duration. Our function shortest_duration
can find that design, but you need to input an accrual rate. Let’s assume the accrual rate is 552/4 = 138 patients per year. Notice that under this assumption, accruing 552 patients takes 4 years, and a trial where you accrue 552 patients and follow all patients for 3 years takes 7 years to complete.
This code finds the shortest duration trial that has the specified power and pKME (default is 0.95):
shortest_duration(con, trt, 3, .8, 552/4)
#> $n
#> [1] 668
#>
#> $k1
#> [1] 4.84058
#>
#> $k2
#> [1] 0
#>
#> $duration
#> [1] 4.84058
#>
#> $powerRMSTToverC
#> [1] 0.8006054
#>
#> $powerLRToverC
#> [1] 0.8586702
#>
#> $powerLRtauToverC
#> [1] 0.8219996
#>
#> $pMKE
#> [1] 1
The shortest duration trial enrolls 668 patients and takes 4.84 years to complete.
Our shortest duration function also optimizes over sample size, within trial designs that have the shortest duration. For example, if we thought we could accrue 1000 patients per year, either a trial that recruits for the whole duration and lasts 3.013386 years, or a trial that recruits for 0.554 years, then follows patients for 2.459386 years for a total trial duration of 0.554 + 2.459386 = 3.013386 years will meet the required threshold for power and pKME, as shown below. However, the second trial needs about 1/6 as many patients (554 versus 3012). Our shortest_duration
function will return the latter trial design, the smallest sample size trial out of the trials with the shortest duration.
RMSTpow(con, trt, 3.013386, 0, 3, 3.013386*1000)
#> $n
#> [1] 3012
#>
#> $powerRMST
#> [1] 0.9996733
#>
#> $powerRMSTToverC
#> [1] 0.9996733
#>
#> $powerRMSTCoverT
#> [1] NA
#>
#> $powerLRToverC
#> [1] 0.9996469
#>
#> $powerLRCoverT
#> [1] NA
#>
#> $powerLRtauToverC
#> [1] 0.9996469
#>
#> $powerLRtauCoverT
#> [1] NA
#>
#> $pKME
#> [1] 0.9500287
RMSTpow(con, trt, 0.554, 2.459386, 3, 0.554*1000)
#> $n
#> [1] 554
#>
#> $powerRMST
#> [1] 0.8011577
#>
#> $powerRMSTToverC
#> [1] 0.8011577
#>
#> $powerRMSTCoverT
#> [1] NA
#>
#> $powerLRToverC
#> [1] 0.848365
#>
#> $powerLRCoverT
#> [1] NA
#>
#> $powerLRtauToverC
#> [1] 0.8483505
#>
#> $powerLRtauCoverT
#> [1] NA
#>
#> $pKME
#> [1] 0.9517473
shortest_duration(con, trt, 3, .8, 1000)
#> $n
#> [1] 554
#>
#> $k1
#> [1] 0.554
#>
#> $k2
#> [1] 2.459386
#>
#> $duration
#> [1] 3.013386
#>
#> $powerRMSTToverC
#> [1] 0.8011577
#>
#> $powerLRToverC
#> [1] 0.848365
#>
#> $powerLRtauToverC
#> [1] 0.8483505
#>
#> $pMKE
#> [1] 0.9517498
Sometimes a trial that is only a little longer than the shortest duration trial can offer a large reduction in sample size. One way to explore this is using our shortest_duration
function with the option altdesign = T
. You add a multiplier which defines the trial length you are willing to consider, relative to the shortest duration trial, on a multiplicative scale. For example, if you set multiplier
= 1.1, the function will find the smallest sample size trial with duration equal to 1.1 times the shorest possible duration. In other words, you are willing to consider a trial that is 10% longer than the optimal trial. In the example above, the shortest possible trial was 4.84 years, so a multiplier of 1.1 indicates considering trials of length 5.3.
shortest_duration(con, trt, 3, .8, 552/4, altdesign = T, multiplier = 1.1)
#> $n
#> [1] 568
#>
#> $k1
#> [1] 4.115942
#>
#> $k2
#> [1] 1.202079
#>
#> $duration
#> [1] 5.318021
#>
#> $powerRMSTToverC
#> [1] 0.800984
#>
#> $powerLRToverC
#> [1] 0.8894142
#>
#> $powerLRtauToverC
#> [1] 0.8410203
#>
#> $pMKE
#> [1] 1
We see that by extending the trial by about half a year, we can reduce the number of required patients by 100.
We now have several candidate trial designs. We can explore each design further by plotting power as a function of each design parameter using the plot = T
option. We focus on the trial that enrolls 552 patients over 4 years, then follows them for 3 years. In the following plots, the red lines represent the power of the log-rank test using all available followup, the blue lines represent the log-rank test using data to time tau
, and the black lines represent the power of the test for difference in RMST restricted to time tau
.
RMSTpow(con, trt, k1 = 4, k2 = 3, tau = 3, n = 552, plot = T)
#> $n
#> [1] 552
#>
#> $powerRMST
#> [1] 0.8008182
#>
#> $powerRMSTToverC
#> [1] 0.8008182
#>
#> $powerRMSTCoverT
#> [1] NA
#>
#> $powerLRToverC
#> [1] 0.9539556
#>
#> $powerLRCoverT
#> [1] NA
#>
#> $powerLRtauToverC
#> [1] 0.8709213
#>
#> $powerLRtauCoverT
#> [1] NA
#>
#> $pKME
#> [1] 1
The plots show us that we can reduce accrual time and maintain power, but that would mean we need to enroll the same number of patients in a shorter time, which may not be practical. We also see that we could reduce follow-up time by about a year without a significant loss in power.
RMSTpow(con, trt, k1 = 4, k2 = 2, tau = 3, n = 552)
#> $n
#> [1] 552
#>
#> $powerRMST
#> [1] 0.7998204
#>
#> $powerRMSTToverC
#> [1] 0.7998204
#>
#> $powerRMSTCoverT
#> [1] NA
#>
#> $powerLRToverC
#> [1] 0.9229113
#>
#> $powerLRCoverT
#> [1] NA
#>
#> $powerLRtauToverC
#> [1] 0.8597414
#>
#> $powerLRtauCoverT
#> [1] NA
#>
#> $pKME
#> [1] 1
We see that reducing \(k_2\) to 2 went slightly too far, as power has dropped below 0.8. Using trial and error we find that \(k_2\)=2.1 is adequate to preserves power and pKME, reducing total trial duration to 6.1 years.
RMSTpow(con, trt, k1 = 4, k2 = 2.1, tau = 3, n = 552, plot = T)
#> $n
#> [1] 552
#>
#> $powerRMST
#> [1] 0.8001678
#>
#> $powerRMSTToverC
#> [1] 0.8001678
#>
#> $powerRMSTCoverT
#> [1] NA
#>
#> $powerLRToverC
#> [1] 0.9270544
#>
#> $powerLRCoverT
#> [1] NA
#>
#> $powerLRtauToverC
#> [1] 0.8619928
#>
#> $powerLRtauCoverT
#> [1] NA
#>
#> $pKME
#> [1] 1
The plots show us that, under the new design, reducing follow-up time will reduce power. Increasing sample size or shortening accrual while keeping sample size constant would increase power, but may not be desirable or possible. Similar plots could be used to explore and tweak the other candidate designs.


Command-T doesn't show "spec" files on one project
-
dnagir
Hi,
I've just installed the Command-T with Vim 7.3 (using from iTerm2).
But for some reason no files under "spec" dir appear on *ONE* of the projects. I don't understand why, must be local to the project.
See the video: http://www.youtube.com/watch?v=BoAQQQod74Y
Is there any way to fix it?
Cheers, Dmytrii http://www.ApproachE.com
-
Greg Hurrell
Either:
-
your project directory has more than
g:CommandTMaxFiles
in it (defaults to 10,000); in this case you should raise the limit by adding a setting to your~/.vimrc
Or:
-
your Vim
'wildignore'
setting is set to exclude files under thespec
directory
Both of these options are described in the documentation.
There is also one final possibility:
-
if your project has less than
g:CommandTMaxFiles
in it, your project hierarchy may have a circular reference (ie. a symbolic link that points at one of its parent directories); Command-T makes no attempt to detect his kind of pathological directory structure at this time, although there is a feature request in the issue tracker to do so
-
your project directory has more than
Reply
This topic is now closed.


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There are three required fields for each search. The first is the 'Diagnosis' box which is where keywords for the search based on diagnosis goes. This is a simple search and thus a keyword of 'cancer' would return results like 'breast cancer' and 'brain cancer'. A search of 'cancer tumor' would return results such as 'brain cancer' and 'brain tumor'.
The other required fields are a start date and a end date. This will bound your search to a specific time periode.
If one would like to search 'brain tumor' but do not want results such as 'large tumor' or 'brain cancer' search using 'brain&tumor' where '&' is used to bridge two or more words that we would like to search as one unit. A seach for 'brain&tumor' will however return results containing 'large brain tumor'.


Vibrations
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Baking a cake is a very fun activity. It is very easy and requires no experience. Cake is delicious and loved by many, so click the link to learn how to make this treat at home!


Head Blacksmith Newyear:
Say, you may not have heard, but there is
a new type of armor available that's only for members of a clan academy.
Would you like to have a suit of it? I can make one for you...
Tell him you want it.


Mindy L. Mallory
Economist
Research
My research focuses on agricultural marketing and policy. I analyze prices and risk in commodity, environmental, and natural resource markets to determine if they function in an efficient manner. Also, my work investigates whether risk management activities and governmental policies are effective.
Appointments
2009-2015
University of Illinois Assistant Professor, Agriculture and Consumer Economics
2016-present
University of Illinois Associate Professor, Agriculture and Consumer Economics
Education
2005-2009
Iowa State University, Economics Ph.D.
2003-2005
Emporia State University, Mathematics M.S.
1999-2003
Emporia State University, Mathematics Education B.S.E
Publications
Peer Reveiwed Journal Articles
2017
Trujillo-Barrera, A., P. Garcia, M. Mallory. 2017. “Short-Term Price Density Forecasts in the Lean Hog Futures Market.” European Review of Agricultural Economics, Forthcoming.
Yu, J., and M.L. Mallory. 2017. “Carbon Price Interaction between Allocated Parties and Generated Offsets.” Operational Research, Available here.
Etienne, X.L., M.L. Mallory, and S.H. Irwin. 2017. “Estimating the Cost of Pre-Harvest Forward Contracting Corn and Soybeans in Illinois Before and After 2007.” Agribusiness: an International Journal 00:1-21. Available here.
2016
Shah, P., M. Mallory, A. Ando, and G. Guntenspergen. 2016. “Fine-Resolution Conservation Planning with Limited Climate-Change Information.” Conservation Biology, 31(2):278-289. Available here.
Trujillo-Barrera, A., P. Garcia, M. Mallory. 2016. “Price Density Forecasts in the U.S. Hog Market: Composite Procedures.” American Journal of Agricultural Economics Available here.
2015
Mallory, M.L., W. Zhao, and S.H. Irwin. 2015. “The cost of post-harvest
forward contracting in corn and soybeans.” Agribusiness 31(1):47–62.
Available here.
Yu, J., and M.L. Mallory. 2015. “An optimal hybrid emission control system in a multiple compliance period model.” Resource and Energy Economics 39:16–28. Available here.
2014
Mallory, M.L., and A.W. Ando. 2014. “Implementing efficient conservation portfolio design.” Resource and Energy
Economics. Available here.
Yu, J., and M.L. Mallory. 2014. “Exchange rate effect on carbon credit price via energy markets.” Journal of International Money and Finance 47:145–161. Available here.
2013
T. Zhang, M. Mallory, and P. Barry. 2013. “Determinants of
the patronage refund decision of farm credit system associations.”
Agricultural Finance Review 73(1):102–118. Available here.
Sherrick, B.J., M.L. Mallory, and T. Hopper. 2013. “What’s the ticker symbol for farmland?” Agricultural Finance Review 73(1):6–31. Available here.
2012
Ando, A.W., and M.L. Mallory. 2012a. “Optimal portfolio design to reduce
climate-related conservation uncertainty in the prairie pothole region.”
Proceedings of the National Academy of Sciences 109(17):6484–6489.
Available at: here.
Ando, A.W., and M.L. Mallory. 2012b. “Reply to dunkel and weber: Probability distributions and shortfall risk measures in conservation portfolio analysis.” Proceedings of the National Academy of Sciences of the United States of America 109(35):E2305. Available here.
Mallory, M., and K. Baylis. 2012. “The food corporation of India and the public distribution system: Impacts on market integration in wheat, rice, and pearl millet.” Journal of Agribusiness 30(2). Available here.
Mallory, M.L., S.H. Irwin, and D.J. Hayes. 2012. “How market efficiency and the theory of storage link corn and ethanol markets.” Energy Economics 34(6):2157–2166. Available here.
Mallory, M., and S. Lence. 2012. “Testing for cointegration in the presence of moving average errors.” Journal of Time Series Econometrics 4(2). Available here.
Trujillo-Barrera, A., M. Mallory, P. Garcia. 2012. “Volatility spillovers in US crude oil, ethanol, and corn futures markets.” Journal of Agricultural and Resource Economics 37(2):247. Available here.
2011
Irwin, S.H., D.L. Good, and M.L. Mallory. 2011. “Could a variable
ethanol blender’s tax credit work?” Biofuels 2(3):277–284. Available
here.
Mallory, M.L. 2011a. “Alternative biofuel policies: An economic comparison.” Biofuels 2(3):267–276. Available here.
Mallory, M.L. 2011b. “Coordination problems, strategic complementarities, and multiple equilibria in an alternative fuel market.” Environmental Economics 2(1):38–49. Available here.
Mallory, M.L., D.J. Hayes, and B.A. Babcock. 2011. “Crop-based biofuel production with acreage competition and uncertainty.” Land Economics 87(4):610–627. Available here.
2009
Du, X., D.J. Hayes, and M.L. Mallory. 2009. “A welfare analysis of the
U.S. ethanol subsidy.” Applied Economic Perspectives and Policy
31(4):669–676. Available here.
Conference Proceedings
2016
Shang, Q., M. Mallory, and P. Garcia. 2016. “The Components of the Bid-Ask-Spread: Evidence from the Corn Futures Market.” NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management. St. Louis, MO.
Shang, Q., M. Mallory, and P. Garcia. 2016. “The Electronic Live Cattle Futures Market Bid Ask Spread Behaviors and Components” Agricultural and Applied Economics Association>2016 Annual Meeting, July 31-August 2, Boston, Massachusetts
2015
Mallory, M., P. Garcia, and T. Serra. 2015. “Nearby and Deferred Quotes: What They Tell Us about Linkages and Adjustments to Information.” NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management. St. Louis, MO.
2013
Trujillo, A., P. Garcia, and M. Mallory. 2013. “Price Density Forecasts in the U.S. Hog Market: Composite Procedures.” NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management. St. Louis, MO.
2012
Trujillo, A., P. Garcia, and M. Mallory. 2012. “Density Forecasts of Lean Hog Prices.” Proceedings of the NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management. St. Louis, MO.
Mallory, M. and K. Baylis. 2012. “The Food Corporation of India and the Public Distribution System: Impacts on Market Integration in Wheat, Rice, Pearl Millet, and Corn.” Proceedings of the NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management. St. Louis, MO.
2011
Trujillo, A., M. Mallory, and P. Garcia. 2011. “Volatility Spillovers in US Crude Oil, Ethanol and Corn Futures Markets.” Proceedings of the NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management. St. Louis, MO.
2010
Mallory, M. and S. Irwin. 2010. “The Forward Contract’s Income Shifting Option and Implications on the Forward Basis.” Proceedings of the NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management. St. Louis, MO.
Books
2015
Mallory, M. Price Analysis: A Fundamental Approach to the Study of Commodity Prices. In-progress web version available here
Web Applications
2014
Mallory, M. Demonstrating Leverage in Option Markets. Available here.
Presentations
Invited Talks
2017
“Measuring Price Discovery between Nearby and Deferred Contracts in Storable and Non-Storable Commodity Futures Markets.” Seminar, Commodity Futures Trading Commission, Washington DC, August 23, 2017.
“Measuring Price Discovery between Nearby and Deferred Contracts in Storable and Non-Storable Commodity Futures Markets.” Seminar, USDA ERS, Washington DC, August 24, 2017.
“Measuring Price Discovery between Nearby and Deferred Contracts in Storable and Non-Storable Commodity Futures Markets.” Seminar, University of Wisconsen, October, 2017.
2016
“The Components of the Bid-ask Spread: Evidence from the Corn Futures Market.” Seminar, Department of Agricultural Economics and Economics, Montana State University, September 23, 2016.
2015
“Exchange Rate Effect on Carbon Credit Price via Energy Markets.” IPAM Workshop III: Commodity Markets and Their Financialization, University of California, Los Angeles, May 6, 2015.
2012
“How Market Efficiency and the Theory of Storage Link Corn and Ethanol Markets.” Invited presentation at the Agricultural Economics Department, Oklahoma State University. April 6, 2012.
“Optimal Portfolio Design to Reduce Climate-Related Conservation Uncertainty in the Prairie Pothole Region.” Invited presentation at the Center for Earth and Environmental Science, Indiana University-Purdue University Indianapolis. April 18, 2012.
“How Market Efficiency and the Theory of Storage Link Corn and Ethanol Markets.” Invited presentation at the Department of Economics, Illinois State University, October 10, 2012.
“How Market Efficiency and the Theory of Storage Link Corn and Ethanol Markets.” Invited presentation at the Department of Agricultural Economics, University of Kentucky, October 12, 2012.
Conference Presentations
2015
Mallory, M., P. Garcia, and T. Serra. “Nearby and Deferred Quotes: What They Tell Us about Linkages and Adjustments to Information.” NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management. St. Louis, MO. April 2015.
2012
Mallory M., and K. Baylis. “The Food Corporation of India and the Public Distribution System: Impacts on Market Integration in Wheat, Rice, and Pearl Millet.” NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management. St. Louis, MO. April 2012.
Ando, A., and M. Mallory. “The Perils of Shortcuts in Efficient Conservation Portfolio Design.” AAEA Annual Meeting, Seattle, WA, August 13, 2012.
Mallory M., H. Zahran, and S. Irwin. “Profit and Prices in the Biodiesel Industry.” NC-1177 Annual Meeting, Washington, D.C. October 26, 2012.
2011
Mallory, M., X. Etienne, and S. Irwin. “Estimating the Cost of Forward Contracting Corn, Soybeans, and Wheat.” WAEA Annual Meeting, Banff, Alberta, Canada, June 2011.
Mallory, M. “Energy Policy: Implications for Farmer Cash Flow and Land Values.” NC-1177 Annual Meeting, Denver, CO. October 2011.
2010
Mallory, M., and S. Irwin. “The Forward Contract’s Income Shifting Option and Implications on the Forward Basis.” NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management. St. Louis, MO. March 2010.
Mallory, M., D. Hayes, and S. Irwin. “How Market Efficiency and the Theory of Storage Link Corn and Ethanol Markets.” Biobased Industry Center Bioenergy Camp. Perry, IA. May 20, 2010.
Mallory, M., and S. Lence. “Much Ado about the Wrong Thing? Cointegration Analysis of Commodity Prices.” AAEA Annual Meeting, Denver, CO, July 2010.
2009
2008
Mallory, M., D. Hayes, and B. Babcock. “Crop Based Biofuel Production under Acreage Constraints and Uncertainty.” AAEA annual meeting, Orlando, FL, July 2008.
Posters
2013
Kim, S.W., and M. Mallory. “Trader Twitter Chatter and Corn Futures Price Volatility.” ASSA Annual Meeting, San Diego, CA, January 5, 2013.
2011
Miller, B., M. Mallory, K. Baylis, and C. Hart. “Spatial Basis Effects of Corn-Belt Ethanol Plants.” AAEA Annual Meeting, Pittsburgh, PA, July 2011.
Yu, J., and M. Mallory. “An Optimal Safety Valve for Emission Trading in a Multi-Compliance Period Model.” Heartland Environmental and Resource Economics Workshop. Urbana, IL. October 2011.
Teaching
2015-
Commodity Price Analysis, Link
2011-
Applied Economic Theory, Link
2010-2012
Food and Commdodity Markets, Link
2009-2011, 2016
Commodity Futures and Options, Link
Supervision of Graduate Students
Doctoral Students Supervised
In Progress
Quanbiao Shang
Selina Han
2015
Beth Miller (co-advisor), 2015. Placed at the Environmental Protection Agency
2014
Christina Jolejole, Ph.D. (co-advisor) 2014. “Three Essays on Food Security.” Placed at Harvard, research fellow.
2013
Jongmin Yu, Ph.D. 2013. “Three Essays on Emission Credit Markets.” Placed at Korea Capital Markets Institute, Research Fellow; currently an assistant professor at Hongik University, Korea.
2011
Tianwei Zhang, Ph.D. 2011. “Three Essays on Patronage Refunds and the Capital Structure of Farm Credit System Associations.” Placed at the University of Colorado, Denver, Instructor; currently lecturer in the Economics department at the University of Georgia.
Master’s Students Supervised
2016
Quanbiao Shang
Menas Falakos
Participation on Ph.D. Dissertation Committees
2016
Xin Li
2015
Kishore Joseph
Tisorn Songsermsawas
2013
Xiaoyang Wang
Xiaoli Liao
Payal Shah
Andres Trujillo
Participation on Master’s Examining Committees
2015
Andres Baustista
2013
Anamaria Guadencio
Hala Zahran
2011
Luis Figar
Offices in Professional Scocieties
2013
NC-1177 Agricultural and Rural Finance Markets in Transition, past president, 2013.
Agricultural Finance and Management section of AAEA, past chair, 2013.
2012
NC-1177 Agricultural and Rural Finance Markets in Transition, president, 2012.
Agricultural Finance and Management section of AAEA, chair, 2012.
2011
NC-1177 Agricultural and Rural Finance Markets in Transition, secretary, 2011.
Agricultural Finance and Management section of American Agricultural Economics Association (AAEA), chair-elect, 2011.
Grants Recieved
2015
Rausch, K., K. Baylis, M. Mallory, H. Michelson, M. Paulsen, J. Hart and G. Czapar. 2015-
- “Reduction of Post-Harvest Loss in Bihar” ADM Institute for the Prevention of Post-Harvest Loss ($447,239).
Mallory, M. 2015. “Food Price Forecasting Issues, Techniques and Opportunities Workshop.” Economic Research Service of the USDA. $35,000.
2013
Co-PIs: Ando, A., J. Fratterigo, G. Guntenspergen, and M. Mallory. 2013-2016. “Spatial Conservation and Investment Portfolios to Manage Climate-Related Risk.” National Science Foundation. $402,539. ($78,000 allocated to Mallory).
2012
Mallory, M. “Reducing Postharvest Losses of Grain and Oilseed Crops in India.” William and Flora Hewlett International Research Travel Grant. October 2012. $2,500.
Co-PIs: Baylis, K., D. Chhajed, M. Mallory, and U. Palekar. 2012-2014. “Supply Chain Policy and Strategy Analysis for Prevention of Postharvest Loss.” ADM Institute for the Prevention of Postharvest Loss. $238,000 ($56,925.27 allocated to Mallory).
Co-PIs: A. Ando and M. Mallory. United States Geological Survey. 2012. “Economic Tools for Conservation Decision Making and Land Management under Climate Change.” $34,192 ($17,096 allocated to Mallory).
2011
Mallory, M. 2011. “State of Agricultural Policy in India and Brazil.” ADM Institute for the Prevention of Postharvest Loss. $44,000.
Mallory, M. May 2011-November 2011. Office of International Programs, College of ACES. “Developing a Collaboration with Faculty and Students at the Indian Statistical Institute, Delhi on the Marketing of Grains and Oilseeds in India.” University of Illinois International Seed Grants Program. Project: $2,500.
Mallory, M. 2011. Karl E. Gardner Teaching Enhancement Grant. $2,000.
2010
Mallory, M. June 2010-December 2010. “A Comparison of the Economics of Some Alternative Biofuel Policies.” Illinois Corn Marketing Board. $25,000.


Why Choose Medic+ GH?
At Medic+ GH, patient care is our primary responsibility.  We have world class surgeons and specialists operating in each department and our outpatient clinic. Medic+ GH is dedicated to ensuring your safety during the COVID-19 pandemic. We offer free testing and treatment for COVID-19 in the newly constructed quarantined wing, specially equipped and designed to manage the illness.


bawt is a Go-based ChatOps bot for Slack.
Features
- Plugin interface for chat messages
- Plugin-based HTTP handlers
- Simple API to reply to users
- Keeps an internal state of channels, users and their state.
- Listen for Reactions; take actions based on them (like buttons).
- Simple API to message users privately
- Simple API to update a previously sent message
- Simple API to delete bot messages after a given time duration.
- Easy plugin interface, listeners with criteria such as:
- Messages directed to the bot only
- Private or public messages
- Listens for a duration or until a given
time.Time
- Selectively on a channel, or from a user
- Expire listeners and unregister them dynamically
- Supports listening for edits or not
- Regexp match messages, or Contains checks
- Built-in KV store for data persistence (backed by BoltDB and JSON serialization)
- The bot has a mood (happy and hyper) which changes randomly.. you can base some decisions on it, to spice up conversations.
- Supports listening for any Slack events (ChannelCreated, ChannelJoined, EmojiChanged, FileShared, GroupArchived, etc..)
- A PubSub system to facilitate inter-plugins (or chat-to-web) communications.


The use case I understand here is a departmental administrator who wants to download all approved requests for a given time period. If leveraging the filters we have in the report screen seems more that we want to get into I think the options to get given sets of time (last three months, last six months, year, all requests).
Fields desired are Staff member First/Last Name, trip ID number, Event Name, Event start date/end date.
deployed
by None at 2022-10-03 18:29:16.head_ref_force_pushed
by Francis Kayiwa at 2022-10-03 16:00:19.referenced
by Kate Lynch at 2022-10-03 13:37:51.merged
by Kate Lynch at 2022-10-03 13:37:51.closed
by Kate Lynch at 2022-10-03 13:37:51.closed
by Kate Lynch at 2022-10-03 13:37:52.labeled
by Chun Ly at 2022-10-03 14:48:08.assigned
by Chun Ly at 2022-10-03 14:48:09.closed
by Chun Ly at 2022-10-03 14:48:12.@regineheberlein @slogue I didn't realize that there was a subfield for display text geared toward patrons. In that case, I agree with your approach. Could we test this in Alma sandbox and staging? Also, wondering if this can be applied to the C0140 component records.
mentioned
by None at 2022-10-03 15:02:45.subscribed
by None at 2022-10-03 15:02:45.mentioned
by None at 2022-10-03 15:02:45.subscribed
by None at 2022-10-03 15:02:45.labeled
by Jane Sandberg at 2022-10-03 16:52:28.Coverage decreased (-0.04%) to 94.401% when pulling e4da93cf5206fd83f0f5aedb0d95e9e4eab4d05b on rtl_headings into adc0e1b7100d0251b8965845ab0d40952db87c56 on main.
ready_for_review
by Max Kadel at 2022-10-03 17:09:14.deployed
by None at 2022-10-03 17:48:01.convert_to_draft
by Max Kadel at 2022-10-03 19:48:31.Coverage increased (+0.05%) to 94.494% when pulling f51b0a11be402254be19332fc49a202a155dbaa0 on refactorarraylistviewinshowpage into adc0e1b7100d0251b8965845ab0d40952db87c56 on main.
head_ref_force_pushed
by Jane Sandberg at 2022-10-03 20:32:38.head_ref_force_pushed
by Jane Sandberg at 2022-10-03 20:46:00.head_ref_force_pushed
by Jane Sandberg at 2022-10-03 21:29:50.head_ref_force_pushed
by Jane Sandberg at 2022-10-03 21:34:34.head_ref_force_pushed
by Jane Sandberg at 2022-10-03 21:42:08.ready_for_review
by Jane Sandberg at 2022-10-03 21:42:09.head_ref_force_pushed
by None at 2022-10-03 12:17:20.head_ref_force_pushed
by None at 2022-10-03 12:32:16.referenced
by Bess Sadler at 2022-10-03 13:02:01.merged
by Bess Sadler at 2022-10-03 13:02:01.closed
by Bess Sadler at 2022-10-03 13:02:01.head_ref_deleted
by Bess Sadler at 2022-10-03 13:02:02.closed
by Bess Sadler at 2022-10-03 13:02:02.assigned
by Hector Correa at 2022-10-03 13:40:18.deployed
by None at 2022-10-03 18:48:18.assigned
by Hector Correa at 2022-10-03 19:09:53.https://github.com/pulibrary/pdc_describe/pull/456 resolved this.
The updates for the Views seem to have been removed during a problematic rebase.
closed
by James R. Griffin III at 2022-10-03 14:16:25.reopened
by James R. Griffin III at 2022-10-03 20:25:31.closed
by James R. Griffin III at 2022-10-03 20:25:39.reopened
by James R. Griffin III at 2022-10-03 20:28:10.The answer is yes, we can monitor disks in Datadog. Closing this ticket in favor of #2471. Please add any specific requirements (project, VM, etc.) on that ticket.
closed
by Alicia Cozine at 2022-10-03 14:18:34.Thanks @kayiwa!
mentioned
by Francis Kayiwa at 2022-10-03 14:21:22.subscribed
by Francis Kayiwa at 2022-10-03 14:21:22.referenced
by Alicia Cozine at 2022-10-03 14:21:27.merged
by Alicia Cozine at 2022-10-03 14:21:27.closed
by Alicia Cozine at 2022-10-03 14:21:27.head_ref_deleted
by Alicia Cozine at 2022-10-03 14:21:28.Renews bluemountain, cdh, papersofprinceton, and test-web_cdh.
Thanks @kayiwa!
mentioned
by Francis Kayiwa at 2022-10-03 14:22:34.subscribed
by Francis Kayiwa at 2022-10-03 14:22:34.head_ref_force_pushed
by Alicia Cozine at 2022-10-03 14:24:34.referenced
by Alicia Cozine at 2022-10-03 15:30:39.merged
by Alicia Cozine at 2022-10-03 15:30:39.closed
by Alicia Cozine at 2022-10-03 15:30:39.head_ref_deleted
by Alicia Cozine at 2022-10-03 15:30:40.referenced
by Max Kadel at 2022-10-03 14:52:05.merged
by Max Kadel at 2022-10-03 14:52:06.closed
by Max Kadel at 2022-10-03 14:52:06.head_ref_deleted
by Max Kadel at 2022-10-03 14:52:06.deployed
by None at 2022-10-03 19:19:53.referenced
by None at 2022-10-03 19:22:41.merged
by None at 2022-10-03 19:22:41.closed
by None at 2022-10-03 19:22:41.head_ref_deleted
by None at 2022-10-03 19:22:42.closed
by None at 2022-10-03 19:22:42.assigned
by Kate Lynch at 2022-10-03 13:55:51.


{% extends 'helpcentre-detail.html' %} {% block pageTitle %} Contact us {% endblock %} {% block beforeContent %} {% endblock %} {% block content %}
Thanks for the message, we will look into this for you.


Pakistan's economy has finally stabilised: PM
by News DeskPrime Minister Imran Khan on Friday said the country’s economy has finally stabilised and was moving in the right direction.
While addressing the signing ceremony of Super-6 Wind Power Projects in Islamabad, the premier said there is so much potential for hydro-electricity in Pakistan, and the new project will provide affordable and environmentally friendly clean energy.
The premier noted the benefits of wind energy, adding that global warming is a challenge for future generations and can only be resolved by taking care of the energy produced.
PM Imran also discussed the current economic situation of the country, adding that “The direction is right, now we have to move forward. We need to make our economy move forward”.
He lauded his own government for stabilising the economy and said the rupee is gaining, the stock market is showing positive sentiments and exports are increasing.
He also said the government’s policies are aimed at uplifting the poor segments of society by creating employment opportunities for the people.


The composition determines how we position the subject in out photograph. The goal is to achieve a balanced and visually appealing photo. Good guidelines to follow are the rule of thirds, the golden ratio, and symmetry.
When a subject is moving, we should position it so it is entering the frame and not leaving.


ReBench is a tool to run and document benchmark experiments. Currently, it is mostly used for benchmarking language implementations, but it can be used to monitor the performance of all kinds of other applications and programs, too.
ReBenchDB is a project started in late 2019 to provide convenient access to data recorded with ReBench. Our focus is to facilitate the software engineering process with useful performance statistics.


|
Serial Wombat Arduino Library
|
A class that runs on top of SerialWombatDebouncedInput to increment or decrement a variable based on a button. More...
#include <SerialWombatDebouncedInput.h>
Public Member Functions | |
| SerialWombatButtonCounter (SerialWombatDebouncedInput *serialWombatDebouncedInput) | |
| Constructor for SerialWombatButtonCounter. More... | |
| void | begin (long *variableToIncrement, long slowIncrement, unsigned long slow_mS_betweenIncrements, uint16_t slowToMediumTransition_mS, long mediumIncrement, unsigned long medium_mS_betweenIncrements, uint16_t mediumToFastTransition_mS, long fastIncrement, unsigned long fast_mS_betweenIncrements) |
| bool | update () |
| Called periodically to query the SerialWombatDebouncedInput and update the variable. More... | |
Public Attributes | |
| long | highLimit = LONG_MAX |
| The variable will not increment above this limit. More... | |
| long | lowLimit = LONG_MIN |
| The variable will not decrement below this limit. More... | |
A class that runs on top of SerialWombatDebouncedInput to increment or decrement a variable based on a button.
This class runs on top of a SerialWombatDebounced input. It is passed a variable reference in its begin call. The update() method is then called periodically. This method will look at how many times the debounced input has transitioned since the last call, and also if the input is currently pressed and for how long.
A high limit and low limit can be set which keeps the variable from exceeing those bounds.
Times can be specified for button hold that allows the variable to be incremented at varying rates for short, medium, and long holds.
See the example sw4b_ard_Debounce2 distributed with the Serial Wombat Arduino Library and in the video
for an example.
| SerialWombatButtonCounter::SerialWombatButtonCounter | ( | SerialWombatDebouncedInput * | serialWombatDebouncedInput | ) |
Constructor for SerialWombatButtonCounter.
| serialWombatDebouncedInput | A pointer to an already initialized SerialWombatDebouncedInput |
| void SerialWombatButtonCounter::begin | ( | long * | variableToIncrement, |
| long | slowIncrement, | ||
| unsigned long | slow_mS_betweenIncrements, | ||
| uint16_t | slowToMediumTransition_mS, | ||
| long | mediumIncrement, | ||
| unsigned long | medium_mS_betweenIncrements, | ||
| uint16_t | mediumToFastTransition_mS, | ||
| long | fastIncrement, | ||
| unsigned long | fast_mS_betweenIncrements | ||
| ) |
Initializes the SerialWombatButtonCounter
| variableToIncrement | A pointer to a signed long integer |
| slowIncrement | the amount that the variable should increment (or decrement if negative) per increment |
| slow_mS_betweenIncrements | how often an increment should happen in slow mode |
| slowToMediumTransition_mS | how long to stay in slow mode before switching to medium mode |
| mediumIncrement | the amount that the variable should increment (or decrement if negative) per increment |
| medium_mS_betweenIncrements | how often an increment should happen in medium mode |
| mediumToFastTransition_mS | how long after the initail button press start until switching to Fast mode |
| fastIncrement | the amount that the variable should increment (or decrement if negative) per increment |
| fast_mS_betweenIncrements | how often an increment should happen in fast mode |
| bool SerialWombatButtonCounter::update | ( | ) |
Called periodically to query the SerialWombatDebouncedInput and update the variable.
| long SerialWombatButtonCounter::highLimit = LONG_MAX |
The variable will not increment above this limit.
| long SerialWombatButtonCounter::lowLimit = LONG_MIN |
The variable will not decrement below this limit.


If varname contains namespace qualifiers, the local variable's name is the unqualified name of the global variable, as determined by the namespace tail command.
varname is always treated as the name of a variable, not an array element. An error is returned if the name looks like an array element, such as a(b).
proc reset {} { global a::x set x 0 }
This procedure accumulates the strings passed to it in a global buffer, separated by newlines. It is useful for situations when you want to build a message piece-by-piece (as if with puts) but send that full message in a single piece (e.g. over a connection opened with socket or as part of a counted HTTP response).
proc accum {string} { global accumulator append accumulator $string \n }


@* outline Template File *@ @(title: String, user:controllers.Security.UserInfo, epa_compare:Boolean, privilege: Privilege, content:Html)(implicit request: RequestHeader, messages: Messages)


The task of music generation has been considered as the biggest challenge in the field of MIR. In the previous work of music generation, researchers have tried to tackle the task from different point of view. In other words, aiming for different application. In this post, I'm going to list some esteemed works in music generation, and conclude with the idea of MidiNet.
The Previous Work
WaveNet(Google, Deepmind)
C-RNN-GAN(Olof Mogren)
A.I. Duet (Google, Magenta)
DeepBach(SonySCL, Flow Machines)
If we looked into these works and tried to conclude their applications, we could say that these works are solving the sub-questions of music generation, such as generating harmonic sounds(WaveNet), or composer assistant (DeepBach, A.I. Duet).
However, in the MidiNet project, we tend to give consideration in music generation in a more human nature scenario.
How we learn music?
Think of a paino teacher is trying to teach a student who have never seen or played piano before, and have no music education. The first step that a teacher is going to do might be playing some easy melody and try to guide the student from introducing the basic music theory, then, let the student try.
After the student have some clue, the teacher might started to introduce more concept, such as the relation of notes and chord, the emotion of music, or other fundamentals.
One day, the student reach an intermediate level, it's time for some challenges, such as taking performance examinations, reaching higher level by pass through higher standards.
There you go, that's the idea of MidiNet! Check out MidiNet-PART1.


Documentation
Tries to find an open transaction that has modified the object
with the given object-id and is older than the given transaction.
Returns this conflicting transaction, if there is one. Otherwise it
returns nil.
Source
(defgeneric find-conflicting-transaction (object-id cache transaction)
(:documentation
"Tries to find an open transaction that has modified the object
with the given object-id and is older than the given transaction.
Returns this conflicting transaction, if there is one. Otherwise it
returns nil."))
Source Context


· · / • experiential • / · ·
Attempting to define the visceral.
A philosophy which embraces transformative learning that emerges
from a direct experience which is designed intuitively and
intelligently for a particular group/individual, within a safe
learning environment and guided by a facilitator.
ex·peri·ence
n.
From the word expiri ‘to try ’
· ·/• The Fine Art of Facilitation •/· ·
A crafted raw performance set to an ever-changing rhythm,
with a participating audience creating waves
that wash into the organ of learning.
A fine, fine balance. Good facilitation holds an outcome in sight
while planning and designing, yet lets go of any agenda while
being present with the group/individual. There is no recipe here
that involves heirarchy, judging, rescuing or condemning. And
definitely no free/costly dispensing of advice.
There is wonderful science and psychology involved in the
choreography of guided experiences in order to make the learning
most impactful. Again, remember that there i s n o agenda forced o n
any individual. A good facilitator genuinely loves what they do
and cares for whom they are working with.-
Build trust and then act with generosity and kindness;
asserting firmness when required.


{% include head.html %}
{% include nav.html %} {% include header.html %} {% include motivation.html %} {% include about.html %} {% include objectives.html %} {% include assumptions.html %} {% include design.html %} {% include hardware_requirements.html %} {% include timeline.html %} {% include footer.html %} {% include js.html %}


Requirements
- Design and build a website of your choice
- Demonstrate the process to plan and implement the project
-
- Submit an artifact of the process at each milestone
- Use HTML to correctly structure the DOM
- Style using CSS
- Create interactivity using JavaScript
Milestones
| Due Date | Deliverable | |
|---|---|---|
| Milestone 1 | Week 5 - 04/25 | Project Proposal & Wireframes |
| Milestone 2 | Week 7 - 05/09 | Pseudocode |
| Milestone 3 | Week 8 - 05/16 | First Draft |
| Final Product | Week 10 - 06/01 | Presentations |


jQuery plugin to show images and pdf files preview
Doing click over any image the preview will be show the image or pdf file related


* Get ready. The Universal Packing List
today’s Very Short List:
tech 1 Jul 2013
Amrit Richmond is the creator of Magenta Labs
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food 28 Jun 2013
Karyn Campbell on the substitute food scene.
culture 26 Jun 2013
Joshua Neuman on exploration
tech 23 Jun 2013
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We redefine tables!
- synergize distributed infrastructures
- embrace out-of-the-box partnerships
- utilize intuitive applications
- drive back-end methodologies
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Ocean [aj03], ocean, in Crystal Sea
Last updated Turn #129
Ocean [aj03], ocean, in Crystal Sea
------------------------------------------------------------------------
20: Sirocco Traveller [ja8y], ship, 10 hulls, 1 rowing port, 30 sails,
20: 4% damaged, arrived from Ocean [ai03], owner:
20: Toulouse [h1q], Pen, with one worker, 32 sailors
21: Sirocco Traveller [ja8y], ship, 10 hulls, 1 rowing port, 30 sails,
21: 4% damaged, departed for Ocean [ak03].
Routes leaving Ocean:
North, to Ocean [ai03], 6 days
East, to Ocean [aj04], 6 days
South, to Ocean [ak03], 6 days
West, to Ocean [aj02], 6 days
The province is blanketed in fog.
No one can be seen through the fog.
Ships sighted:
Pen Explorer [gq0r], ship, 1 hull, 3 rowing ports, 3 sails,
23% damaged


class Solution: def maximumRequests(self, n: int, requests: List[List[int]]) -> int: ans = 0 r = len(requests) for bm in range(1<<r): cnts = [0]*n cand = 0 for i in range(r): if (bm >> i) & 1: cand += 1 f, t = requests[i] cnts[f] -= 1 cnts[t] += 1 if all(v == 0 for v in cnts): ans = max(ans, cand) return ans


Polling interval (milliseconds):
Connect
Stop
Show log
Show debug
Image
Log
Debug
Developer Reference
The following endpoints are available:
GET /api/events
- Poll for events, response is JSON
POST /api/image
- Update image (send binary body)


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Trust us, we've been there. Getting in and out of your home to use the elevator only to find yourself waiting minutes for the consistently slow elevator to finally reach your floor can feel like a nightmare. Slow elevators are inefficent, and are often a safety hazard when the elevator reaches it's max weight capacity.
Whatever the repair or maintennance may be, Rocket Elevators has you covered. Just reach out to whoever owns or is in charge of the building and tell them to contact us and request a quote.
We got you covered. When doing an elevator service/repair we utilize our own tools, workforce, equipment, materials, etc.
The only thing we require of you is to give us a timeslot and a place. All of that must be included when using our request a quote form.
We got you covered. When doing elevator construction we utilize our own tools, workforce, equipment, materials, etc.
The only thing we require of you is to give us a timeslot and a place. All of that must be included when using our request a quote form.
Why we outshine our competition:
Compared to our competiors, here at Rocket Elevators we have the experience and customer reviews to back up our claims. Since 1976 when we were first founded by Bob Billy Joe on his dream to make elevator service easy, fast and accessible to all people.


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Before we start digging into the fundamentals of Deep Learning, let’s take a look at the tools we will be using going forward.
- Google Colaboratory : You can run your code on GPUs hosted by Google for free.
- Seedbank : Collection of interactive machine learning examples for Colab or Jupyter.
- Create a profile on Kaggle, if are not already signed up for it.
Deep learning
|-- Supervised
| |-- MLP: Multilayer perceptron
| |-- CNN: Convolutional Neural Networks
| |-- RNN: Recurrent Neural Networks
| |-- RNN: Recursive Neural Networks
| |-- LSTM: Long short-term memory
| |-- SQS: Sequence-to-sequence models
| |-- SNN: Shallow Neural Networks
|-- Unsupervised
|-- SOM: Self Organizing Maps
|-- BM: Boltzmann Machines
|-- AE: AutoEncoders
Supervised Deep learning
DL form where an output label exists for every input example. The labels are used to compare the output of Deep Neural Nets to the ground-truth values and minimize the cost function. Other forms of Deep learning are semi-supervised and Unsupervised.
Smartest and most complex thing on this planet is human brain and with deep leaning we try to mimic how human brain works by creating “Artificial Neural Networks”.
What is an ANN?
Artificial Neural Network (ANN) is
- CNN: Convolutional Neural Networks
- RNN: Recurrent Neural Networks
Unsupervised Deep Learning
- SOM: Self Organizing Maps
- BM: Boltzmann Machines
- AE: AutoEncoders
Some useful resources
- See in action: Tensorflow Playground
- Flashcards of the interview: Deep Learning Dictionary
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BrickHack 5
February 16-17, 2019
40 schools - 590 hackers - 24 hours
BrickHack 4
January 27-28, 2018
17 schools - 510 hackers - 24 hours
BrickHack 3
February 11-12, 2017
33 schools - 500 hackers - 24 hours
BrickHack 2
March 5-6, 2016
25 schools - 360 hackers - 24 hours
BrickHack 1
April 18-19, 2015
20 schools - 320 hackers - 24 hours
The inaugural BrickHack.


TensorFlow
https://medium.com/@tensorflow
https://habrahabr.ru/search/?q=tensorflow
https://docs.devicehive.com/docs
https://ai.googleblog.com/2017/05/using-machine-learning-to-explore.html
Nilolenko DeepLearning book
https://www.amazon.com/Deep-Learning-Applications-Using-Python/dp/1484235150/ BOOK
https://medium.com/tensorflow/introducing-tensorflow-probability-dca4c304e245
http://playground.tensorflow.org/#activation=tanh&batchSize=10&dataset=circle®Dataset=reg-plane&learningRate=0.03®ularizationRate=0&noise=0&networkShape=4,2&seed=0.66230&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false
NN
https://towardsdatascience.com/the-fall-of-rnn-lstm-2d1594c74ce0 The fall of RNN / LSTM use Attention Networks
Image processing
https://habr.com/hub/image_processing/
https://habr.com/company/dataart/blog/350120/
https://rowhanm.github.io/MiniCatsDogs/
https://www.toptal.com/machine-learning/machine-learning-video-analysis
https://hackernoon.com/a-comprehensive-design-guide-for-image-classification-cnns-46091260fb92
https://sod.pixlab.io/
https://habr.com/company/binarydistrict/blog/354524/
https://heartbeat.fritz.ai/the-5-computer-vision-techniques-that-will-change-how-you-see-the-world-1ee19334354b
https://towardsdatascience.com/real-time-object-detection-api-using-tensorflow-and-opencv-47b505d745c4
https://towardsdatascience.com/is-google-tensorflow-object-detection-api-the-easiest-way-to-implement-image-recognition-a8bd1f500ea0
https://habrahabr.ru/post/354092/ Object recognition
https://medium.com/@jonathan_hui/what-do-we-learn-from-region-based-object-detectors-faster-r-cnn-r-fcn-fpn-7e354377a7c9
https://blog.paperspace.com
https://medium.com/paperspace/tutorial-on-implementing-yolo-v3-from-scratch-in-pytorch-part-1-a0054d38ec78
https://medium.com/ml-everything/how-to-actually-easily-detect-objects-with-deep-learning-on-raspberry-pi-4fd40af84fee
https://medium.com/ml-everything/offline-object-detection-and-tracking-on-a-raspberry-pi-fddb3bde130
https://www.pyimagesearch.com/2017/09/18/real-time-object-detection-with-deep-learning-and-opencv/
https://pjreddie.com/darknet/yolo/
https://pythonprogramming.net/video-tensorflow-object-detection-api-tutorial/
https://www.coursera.org/learn/convolutional-neural-networks/lecture/VgyWR/object-detection
https://aws.amazon.com/rekognition/
https://www.microsoft.com/developerblog/2017/04/10/end-end-object-detection-box/
https://towardsdatascience.com/feature-extraction-and-similar-image-search-with-opencv-for-newbies-3c59796bf774
https://software.intel.com/en-us/articles/visualising-cnn-models-using-pytorch


Created by Chris Zietlow / @aczietlow
Upstart is based on an event monitoring system. When a certain hardware condition occurs, or another process sends an event, one or more of upstarts scripts might be triggered. This allows i.e. particular actions being automatically triggered when an usb stick is inserted or removed
Upstart was written in an attempt to overcome limitations of System V. sysV was limited particularly when it came to optimization of making use of parallelism. It also struggled with handling frequent hardware changes, making it more of a server centric service.
Upstart can run a series of one of tasks (tasks) or control and monitor services and daemons (jobs)
You job files that contain daemons are placed in /etc/init and follow the syntax name.conf.
When you create a new job file you can preform all the upstart command on it. start | status | stop, (list)
initctl start name
# or use the shorthand
start name
# my upstart template file
# This script will start / stop something for me.
description "Start/stop some service"
version "1.0"
author "Chris Zietlow"
script
sleep 1000
end script
Events are notifications sent by Upstart to all interested parties, either jobs or other events. Think as events as signals, hooks, or methods. Events are very abstract, which allows them to be implemented in a variety of ways
Events are emitted
Emitted events are broadcast by upstart to all listeners to find all available matches
Administrators can manually create an event
initctl emit myevent-1
Some event names are reserved by upstart
# my upstart template file
# This script will start / stop something for me.
description "Start/stop some service"
version "1.0"
author "Chris Zietlow"
#start or stop based on events
#start/stopping based on other services, like network manager, or httpd.
start on started other-service
stop on stopping other-service
start on myevent1
stop on myevent2
script
sleep 1000
end script
Jobs are typically a task being performed or service. They are defined in a job configuration file. Tasks fall into 1 of 2 categories. task jobs, and service jobs. task jobs are usually your one off tasks, like deleted a file, where service jobs would be a daemon.
All Jobs MUST have an exec or script stanza
exec gives the path to a binary on the filesystem and optional arguments to pass to it; any special characters (e.g. quotes or ‘$’) will result in the command being passed to a shell for interpretation instead.
exec /bin/foo --opt -xyz foo bar
script instead gives shell script code that will be executed using /bin/sh. The -e shell option is used, so any command that fails will terminate the script. The stanza is terminated by a line containing just “end script”.
script
# do some stuff
if [ ... ]; then
...
fi
end script
Jobs have states which are exposed via the status command
# my upstart template file
# This script will start / stop something for me.
description "Start/stop some service"
version "1.0"
author "Chris Zietlow"
#start as soon as the network daemon starts
start on (started network-interface
or started network-manager
or started networking)
stop on (stopping network-interface
or stopping network-manager
or stopping networking)
pre-start script
mkdir -p /var/log/example
chown -R www-data /var/log/example
end script
script
echo "Example script has started" >> /var/log/example/example.log
end script
# My minecraft upstart file
# This script will start / stop the minescraft server for me.
description "Start/stop Minecraft"
version "1.0"
author "Chris Zietlow"
respawn
pre-start script
exec mkdir -p /var/log/minecraft
chown -R www-data /var/log/minecraft
end script
script
cd /var/minecraft/
exec /bin/sh -c '/usr/bin/java -Xmx2G -jar minecraft_server.jar nogui >> /var/log/minecraft/server.log 2>&1'
end script
start on starting hubot-irc
stop on stopping hubot-irc
respawn
exec su - www-data -c 'cd /home/aczietlow/Web/hubot;
export PORT=5000;
export HUBOT_IRC_SERVER="irc.freenode.net";
export HUBOT_IRC_ROOMS="#spyderbytedesign";
export HUBOT_IRC_NICK="kikihubot";
export HUBOT_IRC_UNFLOOD="true";
export HUBOT_IRC_PRIVATE="true";
export FILE_BRAIN_PATH=".";
bin/hubot --adapter irc >> /var/log/kikibot/hipchat-1.log 2>&1'


Quad City DJ's
"Space Jam," from QuadraSound/Big Beat/Atlantic recording group Quad City DJ's, again unites hit-makers Nathaniel "C.C. Lemonhead" Orange and Johnny "Jay Ski" McGowan -- the genre-breaking production team whose sizzling track record already guarantees them a place in pop music history. In 1993 the duo tapped the motherlode with an out-of-nowhere sensation called "Whoot (There It Is)" by their group 95 South. This year they repeated that success with Quad City DJ's "Ride It (The Train)," which recently became a dancefloor anthem nationwide.
Find out more about Quad City DJ's


Uses of Package
io.ebean.bean
Package
Description
Enhanced beans API and Support objects
Common non-public interfaces and implementation.
Configuration settings for Database construction
Persist and Query Event Controllers and Listeners
Provides a API for plugins.
JSON formatting and parsing objects (See JsonContext).
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ClassDescriptionLazy loading capable Maps, Lists and Sets.Loads a entity bean collection.Visitor for collecting new/old values for a bean update.Loads a entity bean.A call origin for query execution profiling to collect graph use (for query tuning).Bean that is aware of EntityBeanIntercept.Holds information on mutable values (like plain beans stored as json).Represents a next value to use for mutable content properties (DbJson with jackson beans).Collects profile information for a bean (or reference/proxy bean) at a given node.Collects the profile information.Identifies a unique node of an object graph.Represents a "origin" of an ORM object graph.Holds entity beans by there type and id.Wrapper on a bean to also indicate if a bean has been deleted.A callback that can be registered to fire on getter method calls.BeanLoader used when single beans are loaded (which is usually not ideal / N+1).A type that can participate in building toString content with ToStringBuilder.Helps build toString content taking into account recursion.
-
ClassDescriptionLazy loading capable Maps, Lists and Sets.Interface to define the addition of a bean to the underlying collection.Loads a entity bean collection.Bean that is aware of EntityBeanIntercept.A type that can participate in building toString content with ToStringBuilder.Helps build toString content taking into account recursion.
-
-
-
ClassDescriptionLoads a entity bean.Bean that is aware of EntityBeanIntercept.
-


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Respect voters’ mandate in Maharashtra: Vasan
by Special CorrespondentThe Tamil Maanila Congress on Monday said it hoped that the voice of the voters of Maharashtra would be upheld in the ongoing government formation crisis and that the concerns of the farmers and water problems in the State had to be immediately addressed.
In a statement, TMC president G.K. Vasan said, “on one side, there is a coalition formed just to grab power, while on the other side there is a government formed and in a position to prove its majority if the Supreme Court orders so”.
But, Mr. Vasan demanded that the voice of the voters must be respected and people’s welfare must be looked after.
In a separate statement, Mr. Vasan welcomed the Centre’s proposal to put on hold the Dam Safety Bill. Tamil Nadu has been opposing the Bill over the State potentially losing its rights on control and maintenance of dams located in neighbouring States.


PART I. [OLD] GENERAL PROVISIONS
§§323-1 to 7.8 REPEALED. L 1989, c 327, §11.
[PART I. GENERAL PROVISIONS]
§323-1 Contracts with territories and possessions of the United States. The governor is authorized to enter into and execute contracts in the name of the State with territories, possessions, and other areas in the Pacific Ocean region that are under the jurisdiction of the United States, regarding the use of health and public or private correctional facilities of the State on a space available basis; provided that any such contract shall provide for the payment of costs to the State. [L 1989, c 327, §10; am L 1998, c 227, §4]


This is a website that checks what type of a triangle you inqured and returns the result.
The three types of triangle you can create are:
If the sum of the lengths of any two sides of a triangle is less than or equal to the length of the third side, this CANNOT be a triangle hence NOT a triangle will be returned
enter length
enter height
enter hypotenuse


Feedback System
AdminPage
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Manage Presentation
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