Blast From the Past * * 1/2 (out of * * * * )
Directed by Hugh Wilson. Cast: Brendan Fraser, Alicia Silverstone, Christopher Walken, Sissy Spacek, Dave Foley, Jenifer Lewis. 1999 - 106 minutes Rated PG-13 (for profanity). Reviewed February 14, 1999.
"Blast From the Past" is a sunny, good-natured romantic comedy that is as light as the air, but almost always charming, and is helped immeasurably by the occasionally sharp screenplay, written by Bill Kelly and Hugh Wilson (also posing as the director), and the uniformly strong cast of relatively fresh, young talents and veteran actors.
The film starts out in Los Angeles in 1962 at a party being held by the Webbers, scientist Calvin (Christopher Walken) and the 9 months pregnant Helen (Sissy Spacek), when they hear word about the oncoming Cuban missile crisis. Quickly getting rid of their guests, Calvin and Helen desperately make their way down into their own personal fallout shelter just as a plane crashes in to their house. Thinking the Big One hit, Calvin sets the locks for 35 years because, as he says, by that time the radiation will have faded. Equipped with every possible thing they could need, Calvin loves the life down in the shelter and as the years fly by, teaches his son, Adam, as if he were in a real school. Helen, however, eventually begins to go stir-crazy, turning to any alcohol she can find, even her cooking sherry.  Losing his way back to the fallout shelter, which a slummy restuarant run by a hippie has been built on top of, all Adam really wants is a girl, which he finds in the form of Eve (Alicia Silverstone), a cute, but cynical, young woman who is at first distant with him, but agrees to help him in his quest of buying and storing as much food as possible, thanks to selling his nearly priceless 35-year-old baseball cards. Eve is not really sure why he is doing such a thing, but doesn't bother to ask since, after all, he is so unlike all of the men she has known before, and is more like an innocent boy, albeit one that is not used to the '90s world.
Although "Blast From the Past" often feels somewhat like "Pleasantville" in reverse, "Blast" mostly avoids the latter's serious commentary on the world and so it is inevitably not nearly as deep, but because of this, also does not fall into the trap of laying the emotions on too thick.
Undoubtedly a romance at heart, the film was therefore surprising how, in the first half-hour, the characters of Calvin and Helen were the main focus, and it had a wonderful start, thanks to Walken's and, especially, Spacek's bright performances. Spacek is usually seen in more dramatic pictures (as in the remarkable recent "Affliction"), but shows that she is also a wonderful comedian, and a standout in every one of her scenes as the loving wife and mother who happens to also be going into a hilarious descent into booze.
Once the now-grown Adam takes over the film, "Blast Fom the Past" is transformed into an entertaining "fish-out-of-water" story, as he starts to discover the world, including the sky and ocean, which he has never even seen before. One small, but sweet sequence shows Adam sitting at a window and intently watching the sunrise for the first time in his life.
As a budding romantic comedy, the film is also a success, due to the charming rapport between Fraser, who seems to recently be switching from indies ("Gods and Monsters") to the mainstream ("George of the Jungle"), and Silverstone, who has made her first good film since she appeared in 1995 in one of the best comedies of the decade, "Clueless." Although certainly nowhere near that level, it is a clear improvement from her last two failed excursions, the embarrassing "Batman & Robin" and the misguided "Excess Baggage."
For all of its many engaging elements, "Blast From the Past" is in no way anything more than merely good. The film is slight, but in a fun way, and makes no excuses for being anything more than what it sets out to be, a winsome, frothy comedy. Throughout the film, I laughed out loudly quite a few times, and was won over by the pleasant two lead characters whom I wanted to both be happy. The movie delves no further down than this, but that was quite enough for it to be an entertaining diversion for 106 minutes.
- Copyright 1999 by Dustin Putman Http://hometown.aol.com/FilmFan16/index.html
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UESTC
Bachelor of Engineering 2015-2019
GPA: 3.62/4.00; TOEFL 98/120; GRE: 314+2.5; First Prize Scholarship; MCM/ICM Meritorious Winner;
Project: Real-time object detection on Jetson TX2;
Internship: Virtuos Chengdu: Participate in AAA game dev: Beyond Two Soul PC Port;
Internship: China Mobile Research: Edge AI dev & SASS management system;


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| void | Write(CodeFormatter& formatter) override |
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| Comment& | operator=(const Comment&) delete |


I am a seasoned product professional with experience as a researcher, developer, ethnographer, and in user experience. As a documentary filmmaker, I researched, interviewed, did storyboarding, wrote papers and proposals, built corresponding websites, and edited the films. As a UX Designer, I perform these same tasks using different technologies. The marriage of the human component and technology is fascinating and mirrors the juxtaposition of quantitative and qualitative research. I’ve spent my education and career thinking about how to represent people, consider what people’s needs are, and enjoying the task of revealing these processes and people through technology.
With a graduate degree in Interdisciplinary Studies: Communication Studies (Electronic Media and Ethnography), I have written numerous ethnographic papers based on research, won awards for my ethnographic fieldwork, and have vast experience writing and in interviewing and testing. My specialties are Research, Usability Studies, Task Analysis, Storyboarding, Archetypes and Personas, Userflow/IA, Report Writing, Data Collection, Qualitative and Quantitative Analysis, and Journey and Empathy Mapping. My skills include Sketch, Invision, Adobe Creative Suite, Trello, Microsoft Office Suite, and Confluence. In addition to my UX skills, I am a competent developer and am proficient in HTML, CSS, JavaScript, Python, and Django. I also have some serious organizational skills, a great attention to detail, and an ability to bring teams together.
This small business website was designed to reflect the personality of the life coach- a creative and outdoorsy therapist. The different layers of the website reveal the different layers of care and opportunities to clients. I also built a corresponding blog that would allow the life coach to update her clients and write articles without needing a professional to maintain it.
This business website was for an educational service. It contains a gallery of videos and information for potential clients.
closeThis wireframe is for part of a larger game app. This was the mvp for the board of directors and is the game geared for small children. This wireframe is the path for a new user to login to the app.
closeThis business website was for an educational service. It contains a gallery of videos and information for potential clients.
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Spacefreight Crates Co. is the galaxy's number 1 cargo retrieval company. Lost your goods in a dangerous asteroid field? No problem! SCC's highly trained drivers will have no problem recovering your cargo. And you just became one of those drivers!
For your first day on the job as a driver, you travel to the Asteroid Belt 55C to recover some cargo crates.
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I am a Web Developer and a Student Chemist
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I started learning to code during my Secondary Schooling due to the fact I wanted to make a positive impact in the society. Over time, I have gained a wealth of experience designing and developing web applications
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I'm so thrilled that you are choosing foto/jenn/inc for your fine art and cinematic photography session. When choosing a custom photography experience, you are choosing professional images, digital files, prints, and folio boxes that are classic, timeless and treasured additions to your home decor.
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All clients receive a free in person consultation. Your consultation will occur at a coffee shop on the NE side of Cedar Rapids. At your consultation, we will collaborate together about your fine art session and talk about wardrobe selections, hair, and make up. We will also go over pricing, contracts and other legalities. Don't worry....it's not at all stressful! To make your session official and put it on my calendar, a signed contract and a $200 deposit (plus applicable sales tax) is required.


Mobile Internet offers wireless access to the World Wide Web which allows users to obtain digitized contents and services from the Internet via handheld mobile devices. These devices can vary from smartphones, feature phones, tablet computers, and laptops, but they all share the common characteristics of connecting to the Internet via a cellular or wireless network ("What Is").
Mobility is the key concept with mobile Internet. Users need to have the freedom to move around and still maintain connectivity to the Internet ("Mobile Internet"). Devices that are immobile and only maintain connectivity via one source (such as a cell tower or router) fall out of the definition of mobile Internet. A mobile phone, such as a smartphone, that connects to data or voice services without going through the cellular base station is not on mobile Internet. A laptop with a broadband modem and a cellular service provider subscription, that is traveling on a bus through the city is on mobile Internet ("What Can").
Mobile Internet is exploding in terms of popularity. As of July 2012, approximately 10.5% of all Web traffic occurred through mobile devices (up from 4% in December 2010). This usage percentage may appear small, but in terms of growth rate, mobile Internet is quite large (Charland). In a 2012 study done by the ITU (International Telecommunication Union), it was observed that web access by people on the go - via laptops and smart mobile devices - is likely to exceed web access from desktop computers within the next three years, if the current growth rates continue (Maya).
This shift to mobile Web access has been accelerating primarily because of the rise of larger multi-touch smartphones in 2007, and the rise of multi-touch tablet computers in 2010. Both platforms provide better Internet access, screens, and mobile applications-based user experiences that were unachievable in previous generations of mobile devices (Maya).
One of the characteristics that distinguishing mobile Internet from more conventional stationary Internet is the ability provide services anywhere and at any time. Because of this feature, mobile Internet services can be employed in a variety of use contexts, whereas the stationary Internet is mostly used in a predetermined. In other words, mobile Internet is used in a wider variety of times and places, and by a wider variety of people than the stationary users (Charland).
Additionally, research has discovered that stationary Internet is often used for exploration without regard to use context, while users of mobile Internet services generally try to accomplish specific tasks that have meaning in a specific context (Zhang). Because the context may determine the relevance of a specific service to a user’s needs, any small contextual change may cause a user to select a different mobile Internet service or disregard the service all together. Therefore, understanding the use context and using its basic properties are the first steps in creating a new mobile service or modifying an existing Web service to make it mobile (Nelle). A service that takes into account information about a specific context may enable a mobile user to achieve his or her goals more efficiently and, thus, become a favored service ("Mobile Internet").
Mobile Internet is a relatively new concept, but there has been some significant events in its short history.
The first mobile device with Internet connectivity was the Nokia 9000 Communicator, launched in Finland in 1996 (Maya). At that time, the viability of Internet services access on mobile phones was limited, and mobile Internet adoption did not begin until prices came down from that model and network providers started to develop systems and services conveniently accessible on phones. Around that same time, NTT DoCoMo in Japan launched the first mobile Internet service, i-mode, in 1999. This was significant in mobile Internet history because i-mode was considered the birth of modern the mobile phone Internet services (Rumble).
In 2001, a mobile phone email system by RIM (Research in Motion) launched in American for their flagship BlackBerry phone. To make efficient use of the small screen, tiny keypad, and one-handed operation that was becoming the typical for this generation of mobile phones, a specific document and networking model was created for mobile devices (Rumble). It was called Wireless Application Protocol (WAP) and it became the technical standard for accessing information over a mobile wireless network. For the history of Wireless Internet to progress, it needed the development of WAP to happen for efficient transfer speeds, size and readability, and navigation. The standard is not perfect with many constraints and flaws, but even today, most mobile devices still operate Internet services using WAP (Adipat).
Standards improve the interoperability, usability, and accessibility of mobile web usage. The Mobile Web Initiative (MWI) was set up by the W3C to develop the best practices and technologies relevant to the mobile Web. The goal of the initiative is to make browsing the Web from mobile devices more reliable and accessible. The main aim was to evolve standards of data formats from Internet providers that were tailored to the specifications of particular mobile devices (Adipat). The W3C published guidelines for mobile content, and is currently addressing the problem of device diversity by establishing a technology to support a repository of device descriptions (Rumble).
The birth of the smartphone marked the beginning in a fundamental shift in web development and mobile device technology (Rumble). Smartphones essentially combined several functions into one discrete device. It served as a mobile cellular telephone, a personal digital assistant, and today, it also can serve as an mp3 player, web browser, navigation system, and many other things (Maya). And although smartphones have consistently presented the challenge of smaller screen sizes and slower download speeds, they have evolved into full-fledged computing machines capable of performing the same functions with regards to the Internet as regular computers (Rumble).
The growth of mobile phone services was initially a primarily Asian phenomenon with Japan, South Korea, and Taiwan all finding the majority of their Internet users accessing resources by phone rather than by PC (Ismail). Developing countries followed, with India, South Africa, Kenya, Philippines, and Pakistan all reporting the majority of their domestic users accessed the Internet from a mobile phone rather than a PC. The European and North American use of the Internet was influenced by a large installed base of personal computers, and the growth of mobile phone Internet access was more gradual, but had reached national penetration levels of 20–30% in most Western countries. The cross-over occurred in 2008, when more Internet access devices were mobile phones than personal computers. Furthermore, in many parts of the developing world, the ratio is as much as 10 mobile phone users to one PC user ("The Takeover").
Mobile Internet has radically changing how businesses and users connect to the Internet. More and more web connections are being done on mobile Internet devices, and predictions forecast these trends to continue to grow ("What Can").
The development of mobile devices amidst the booming popularity of Internet usage made mobile internet popularity inevitable to happen ("What Is"). Since online access has become one of the primary key features of mobile devices, mobile internet is predicted to maintain its growing force within the online market. Additionally, the advent of apps and programming platforms has allowed the mobile market to essentially become a one-stop-shop for all modern conveniences ("What Can"). The device can be used as a GPS navigation system, a scanner, a fax machine, a PDA, a phone, and Internet access device all bundled into one.
Mobile Internet has not only optimized websites and application use on multi-device platforms, but it has also changing the way users experience the web from mobile devices as well ("The Takeover"). Today, it is no longer critical that device screens are smaller and Internet access speeds are slower because the majority of mobile web developers have optimized their sites to work around these issues. Mobile-ready websites offer more features than a traditional website could because of the device’s touch screens, accelerometers, and GPS locating – giving the user a fuller, more enriched web viewing experience ("Mobile Internet").
Optimizing websites for the mobile market is predicted to be highly demanded in the next decade. Sites without mobile optimization could lose up to 13% of their traffic if the site is difficult to view on mobile devices (Nelle). Because of mobile Internet, experts forecast that all websites will eventually need a clean mobile experience that allows users to read text, see images, and watch videos without having to continuously scroll in all directions to view the entirety of the content ("Mobile Internet"). Sites that lack these qualities will be punished with higher bounce rates, lower page views per user, and most importantly, lower conversions. Sites that can successfully adapt to the mobile market will be rewarded with increased page views and increased conversions (Nelle).
Below are some infographics about Mobile Internet.
The marketplace for mobile devices is increasing every year. As of 2012, over 120 million people in the United States owned a smartphone with over 48 million owning a tablet. This increase in users jumped over 300% from the previous year, but more importantly; this percentage increase is expected to continue to grow even larger in the upcoming years.
The expansion in the mobile marketplace is not the only trend happening in the mobile internet landscape. Advertising industries are cashing in this mobile growth by ‘pushing’ users information relevant to their interests. This is made possible because of target marketing techniques made possible from online tracking in coordination with the GPS and accelerator capacities on the mobile devices.
The rapidly-evolving mobile market is a booming industry. Some skeptics relate its fast-pace growth to the Dot-com boom and are convinced that the booming mobile industry will eventual crash just like the Dot-com craze which happened only a decade earlier. However one thing is for certain, the mobile internet hype is a very profitable industry with over $7.19 billion in eMarketing revenues. Furthermore, many corporations planning on deploying five or more mobile apps by 2013 so the job market for the mobile industry is expected to continue to grow as well ("Mobile - Changing").
In 2012, the mobile webspace appeared to be an unstoppable force. Mobile search increased at a rate of over 500% in just two year, 45% of the world’s population was covered in a 3G network, and the average usage of mobile online access was nearly tripling from the previous year. Mobile data traffic was also increased eight times in size from the previous year, and this led to many experts to predict the eventual death of the desktop, or stationary, internet usage by 2015.
Even though this forecast seemed over-speculated and exaggerated, its supporting arguments are not too farfetched. In 2005, desktops and notebooks reigned champions in the online marketplace – outperforming smartphone and tablet sales by nearly fourfold. This marketplace was not consistent through the years; however, as the smartphone and table market eventually overtook the desktop and laptop market in 2012.
Physical device sales were not the only thing to have changed in the past couple of years. A change in online data traffic has also occurred because of the accelerated popularity of mobile devices and mobile web usage. By 2012, mobile web had become far more superior and prevalent than stationary web globally. Mobile devices were easier to obtain and use than previous generations of online devices, and this is dramatically changed the way the internet has evolved. Content on the web now has to be tailored to the new growing demographic of mobile devices. Mobile internet is surpassing stationary internet, and even though the death of the desktop may not be for certain, the death of stationary-web design is expected to certainly happen ("The Takeover").
The ‘Mobile Influence Factor’ is a relatively new concept which characterizes how mobile internet is impacting business strategies and the online marketplace operations. It stresses the overarching effect smartphones and tablets have on in-store purchases. In terms of mobile internet, retail sales are projected to increase to $689 billion by 2016. This equates to over 19% of total store sales, and will significantly change how businesses will need to conduct operations and communicate to their customers. Additionally, if a business does already have a mobile-friendly online store, it has been observed that 57% of consumers will not recommend a business purely because of a poorly designed mobile website. The ‘Mobile Influence Factor’ is a prominent force in the commercial industry, and business who do not realize this will most likely experience suffering profit (Maya).
The expectations and demands for mobile internet are not limited to the leisure and pleasures of daily activities. Mobile users enjoy using their devices to obtain information and services online, and this enjoyment is currently being expected in the workplace as well. 32% of employees globally rely on more than one mobile device during their typical working day, 78% of smart phone users access email from their mobile devices, 65% of mobile workers use a tablet, and 88% of people mentioned they use their PC tablets while in transit. The mobile internet influence is too great to overlook. More and more employees are being dependent on their devices, and in a 2013 survey, it was realized that a majority of employees worldwide said they do not need to be in the office to be productive and that they value mobility and freedom of devices more than a higher salary ("The Mobile").
The mobile internet influence is sweeping the workplace. People are demanding to bring, to use, and to work on their mobile devices while at work, but many organizations are hesitant to this demand. Currently, 59% of companies have a Bring Your Own Device (BYOD) policy that allows employees the freedom of using any mobile device they like. However, 79% of companies currently do not support the idea of BYOD.
This resistance towards BYOD at the workplace is prominent because there are many corporate and security issues with the device-freedom policy. Some of the biggest concerns and threats IT professionals see with allowing BYOD to be implement in their networks are: lost or stolen devices, loss of company or customer data, employee misuse of the mobile device, and mobile malware threats. Even with these threats, employees still continue to demand BYOD polices and companies are continued pressured into complying with their demands (Peterson).
One of the major impacts from the growth in mobile internet is its effect on the World Wide Web. Today, websites have to be mobile-ready in order to meet the ever increasing demand to have their online content visible, usable, and easily accessible on mobile devices. Mobile websites can be built with applications that conform to the unique device, but new standards are currently being adopted which will ease the learning curve and development of mobile-ready website.
One of the more prominent solutions to mobile websites is Responsive Website Design templates. These templates use specialized cascading style sheets, JavaScripts, and jQueries to correctly display the webpage on any device, any screen, and any browser. Its intuitive templates allow for quick mobile-ready website creation, and it also helps reduce costs because the web owner does not need to finance expensive mobile application to operate on multiple platforms. Responsive Website Design is still in its infancy, and there are some uncertainties as to whether or not it will be fully adopted. It is, however, a step in the right direction towards making multiple platform online access simplistic (Brown).
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Welcome To
Santos Homes & Renovations
By
Nery Santos
What we do
- Project Management
- 2-5-10 Year Home Warranty For New Home Constructions
- Licenced Builder
- Bonded and Insured
- Complete Home Renovations
- Plumbing, Electrical, Roofing, Tiling, Kitchen Renovations, Painting, Landscaping
- Licenced Trade Personnel for Home Renovations


The annotated simulation script and the annotated helper functions provide the detail that this summary leaves out.
library(knitr)
opts_chunk$set(fig.width = 12, fig.height = 12, cache = T, warning = F, message = F, cache = F)
source("0_helpers.R")
loadRDS("cycle_sims.rds")
format_digits = function(x) { format(round(x, 2), nsmall = 2)}
sims = cycle_sims
table(round(sims$p.value - sims$p.value_KR, 10)) # exactly the same, always
##
## 0
## 7836798
sims %>%
select(-p.value_KR) ->
sims
print_unique = function(x) {
print(unique(x))
1
}
sims %>%
select(fertility_effect, nr_of_people, nr_days, dayspan, miss_window, trait_effect, predictor) %>%
summarise_each(funs(print_unique)) -> x
## [1] 0.00 0.05 0.10 0.20 0.30 0.50 0.80
## [1] 25 50 70 100 150 250 500
## [1] 2 10 30
## [1] 1:38 17-19,4-6
## Levels: 1:38 17-19,4-6
## [1] 0 1 2 3 4 6 8
## [1] 0.0 0.3
## [1] prc_stirn_b_m fertile_broad_m fertile_narrow_m
## Levels: prc_stirn_b_m fertile_broad_m fertile_narrow_m
sims %>%
group_by(fertility_effect, nr_of_people, nr_days, dayspan, miss_window, trait_effect, predictor) %>% # ,covariate_used, outcome, predictor, stopping.n
summarise(
usable_days = mean(usable_days, na.rm = T),
not_converged = sum(is.na(p.value)),
converged = sum(!is.na(p.value)),
power_001 = sum(p.value <= 0.001, na.rm = T)/converged,
power_01 = sum(p.value <= 0.01, na.rm = T)/converged,
power_05 = sum(p.value <= 0.05, na.rm = T)/converged,
power_10 = sum(p.value <= 0.10, na.rm = T)/converged,
capture_95CI = sum(fertility_effect > conf.low & fertility_effect < conf.high, na.rm = T)/converged,
misestimation = mean(estimate / fertility_effect, na.rm = T),
misestimation_lo = quantile(estimate / fertility_effect, probs = 0.025, na.rm = T),
misestimation_hi = quantile(estimate / fertility_effect, probs = 0.975, na.rm = T),
abs_misestimation = mean(abs(estimate - fertility_effect), na.rm = T),
misestimation_over_se = mean((estimate - fertility_effect) / std.error, na.rm = T)
) ->
power
power %>% filter(miss_window == 0, nr_days == 30, nr_of_people %in% c(150,500), fertility_effect == 0.20, fertility_effect < 0.80, dayspan == "1:38", trait_effect == 0.3) %>%
ungroup() %>%
select(predictor, fertility_effect, nr_of_people,nr_days, power_01, power_05) %>% arrange(fertility_effect, predictor, nr_of_people, nr_days) %>% pander()
| predictor | fertility_effect | nr_of_people | nr_days | power_01 | power_05 |
|---|---|---|---|---|---|
| prc_stirn_b_m | 0.2 | 150 | 30 | 0.993 | 0.999 |
| prc_stirn_b_m | 0.2 | 500 | 30 | 1 | 1 |
| fertile_broad_m | 0.2 | 150 | 30 | 0.843 | 0.949 |
| fertile_broad_m | 0.2 | 500 | 30 | 1 | 1 |
| fertile_narrow_m | 0.2 | 150 | 30 | 0.839 | 0.94 |
| fertile_narrow_m | 0.2 | 500 | 30 | 1 | 1 |
power %>% filter(miss_window == 0, nr_days == 30, nr_of_people %in% c(150,500), fertility_effect == 0.10, fertility_effect < 0.80, dayspan == "1:38", trait_effect == 0.3) %>%
ungroup() %>%
select(predictor, fertility_effect, nr_of_people,nr_days, power_01, power_05) %>% arrange(fertility_effect, predictor, nr_of_people, nr_days) %>% pander()
| predictor | fertility_effect | nr_of_people | nr_days | power_01 | power_05 |
|---|---|---|---|---|---|
| prc_stirn_b_m | 0.1 | 150 | 30 | 0.445 | 0.689 |
| prc_stirn_b_m | 0.1 | 500 | 30 | 0.972 | 0.994 |
| fertile_broad_m | 0.1 | 150 | 30 | 0.199 | 0.414 |
| fertile_broad_m | 0.1 | 500 | 30 | 0.746 | 0.895 |
| fertile_narrow_m | 0.1 | 150 | 30 | 0.205 | 0.435 |
| fertile_narrow_m | 0.1 | 500 | 30 | 0.741 | 0.895 |
power %>% filter(miss_window == 0, nr_days == 30, nr_of_people %in% c(150,500), fertility_effect == 0.05, fertility_effect < 0.80, dayspan == "1:38", trait_effect == 0.3) %>%
ungroup() %>%
select(predictor, fertility_effect, nr_of_people,nr_days, power_01, power_05) %>% arrange(fertility_effect, predictor, nr_of_people, nr_days) %>% pander()
| predictor | fertility_effect | nr_of_people | nr_days | power_01 | power_05 |
|---|---|---|---|---|---|
| prc_stirn_b_m | 0.05 | 150 | 30 | 0.095 | 0.248 |
| prc_stirn_b_m | 0.05 | 500 | 30 | 0.365 | 0.594 |
| fertile_broad_m | 0.05 | 150 | 30 | 0.041 | 0.137 |
| fertile_broad_m | 0.05 | 500 | 30 | 0.183 | 0.387 |
| fertile_narrow_m | 0.05 | 150 | 30 | 0.046 | 0.141 |
| fertile_narrow_m | 0.05 | 500 | 30 | 0.195 | 0.392 |
What matters for power seems to be number of days in total, no matter whether this was achieved by sampling more people or more days per person. High sample sizes should be aimed for to show generalizability and if interindividual moderators are to be tested. However, to show a main effect, it is probably considerably cheaper to ask the same people more than twice. Costs are saved on time spent on interindividual questionnaires, screening, overhead per person etc.
power$n_days = power$nr_of_people * power$nr_days
power %>%
filter(fertility_effect == 0.10, dayspan == "1:38", miss_window == 2, trait_effect == 0.3, predictor == "prc_stirn_b_m") %>%
ggplot(aes(x = n_days, y = power_01, color = factor(nr_of_people))) +
geom_text(aes( label = paste0(nr_of_people, "*", nr_days))) +
scale_x_sqrt()
power$n_days = power$nr_of_people * power$nr_days
power %>%
filter(fertility_effect == 0.10, dayspan == "1:38", miss_window == 2, predictor == "prc_stirn_b_m") %>%
ggplot(aes(x = n_days, y = power_05, color = factor(nr_of_people))) +
geom_text(aes( label = paste0(nr_of_people, "*", nr_days))) +
facet_wrap(~ trait_effect) +
scale_x_log10()
power %>%
filter(fertility_effect == 0.10, dayspan == "1:38", miss_window == 2, predictor == "prc_stirn_b_m", trait_effect == 0.3) %>%
ggplot(aes(x = n_days, y = abs_misestimation, color = factor(nr_of_people))) +
geom_text(aes( label = paste0(nr_of_people, "*", nr_days))) +
scale_x_log10() + scale_y_log10()
I.e. when do we systematically under/overestimate. The main simulated contributor to underestimation is the number of days by which the day of ovulation is missed on average. Not simulated, but likely having a similar effect are
power %>%
filter(fertility_effect == 0.30, dayspan == "1:38", nr_of_people == 100, trait_effect == 0.3) %>% ggplot(aes(x = predictor, y = capture_95CI)) +
geom_text(aes( label = format_digits(capture_95CI))) +
facet_grid(miss_window ~ nr_days) + theme_bw()
power %>%
filter(fertility_effect == 0.30, dayspan == "1:38", nr_of_people == 100, trait_effect == 0.3) %>% ggplot(aes(x = predictor, y = misestimation)) +
geom_text(aes( label = format_digits(round(misestimation,2)))) +
facet_grid(miss_window ~ nr_days) + theme_bw() +
ylab("% of true effect size") +
ggtitle("Underestimation", subtitle = "left-to-right: nr. of days, top-to-bottom: avg. distance to true window")
for 30 days, 500 participants, missing the fertile window on average by four days
power %>%
filter(miss_window == 4, nr_days == 30, dayspan == "1:38", nr_of_people == 500, trait_effect == 0.3) %>% ggplot(aes(x = predictor, y = misestimation)) +
geom_text(aes( label = paste0(form(misestimation), " [", form(misestimation_lo), ";", form(misestimation_hi),"]"))) +
facet_grid(fertility_effect ~ .) + theme_bw() +
ylab("% of true effect size") +
ggtitle("Underestimation", subtitle = "top-to-bottom: true fertility effect")
power %>%
filter(miss_window == 4, nr_days == 30, dayspan == "1:38", nr_of_people == 500, trait_effect == 0.3) %>% ggplot(aes(x = predictor, y = abs_misestimation)) +
geom_text(aes( label = format_digits(round(abs_misestimation,2)))) +
facet_grid(fertility_effect ~ .) + theme_bw() +
ylab("distance to true effect size") +
ggtitle("Underestimation", subtitle = "top-to-bottom: true fertility effect")
sims %>%
filter(fertility_effect != 0, nr_of_people == 500, dayspan == "1:38", nr_days == 30, trait_effect == 0.3, predictor == 'prc_stirn_b_m') %>%
group_by( nr_days, dayspan, miss_window, trait_effect, predictor) %>% # ,covariate_used, outcome, predictor, stopping.n
summarise(
misestimation = form(mean(estimate / fertility_effect, na.rm = T)),
misestimation_lo = form(quantile(estimate / fertility_effect, probs = 0.1, na.rm = T)),
misestimation_hi = form(quantile(estimate / fertility_effect, probs = 0.9, na.rm = T))
) ->
misest
pander(misest %>% ungroup() %>% select(miss_window, starts_with("misestimation")))
| miss_window | misestimation | misestimation_lo | misestimation_hi |
|---|---|---|---|
| 0 | 1.00 | 0.81 | 1.18 |
| 1 | 0.98 | 0.79 | 1.18 |
| 2 | 0.90 | 0.68 | 1.14 |
| 3 | 0.79 | 0.54 | 1.06 |
| 4 | 0.67 | 0.40 | 0.95 |
| 6 | 0.46 | 0.14 | 0.79 |
| 8 | 0.30 | -0.03 | 0.64 |


Windows users must set the environment variable
$PROX_HOME
to the root of the local Proximity
installation; Linux and Mac users may wish to set this environment
variable for convenience.
Proximity shell scripts and batch files are located in
$PROX_HOME/bin
.
Run Proximity applications from the Proximity Database Browser or from the command line using shell scripts or batch files.
All Proximity shell scripts and batch files require a
host:port
parameter.
The init-mserver.mil
script sets the
port to 30000
. You can change
this value using
--set port=
nnnnn
when starting Mserver.
Proximity 4.3 uses the port number to select the
appropriate protocol for the version of MonetDB being used. You
must use a port
number
Proximity
DTD
files are located in
$PROX_HOME/resources
.
Copy the appropriate DTD file to directories containing query files, XML import data files, and model files.
If you edit or add source code, you must recreate
proximity.jar
to have those changes available
when you use the Proximity shell scripts or batch files.
Define shortcuts to frequently used files and directories in the Open dialog.
Choose the appropriate log4j configuration file to set the level of detail included in logging messages.
Keep up to date with future Proximity news and releases by subscribing to the proximity-announce mailing list.
Additional information
See Appendix A, Proximity Quick Reference for a summary of important Proximity commands.
See Chapter 3, Importing and Exporting Proximity Data for information on using the database management utilities and importing and exporting XML or plain text data.
See Chapter 5, Querying the Database for information on using the query runner.
See Chapter 6, Using Scripts for information on using the Python script runner.


Your reservation at {{reservationModel.name}}, on {{ reservationModel.date | date }} at {{reservationModel.time | hours}} for {{reservationModel.number | people}}, has been made.
Your confirmation number is #{{reservationModel.conf_number}}, please present this number upon arrival.


#include <vector.h>
Public Member Functions | |
| vector (const point &s, const point &e, const color &col) | |
| Constructor. | |
| void | dl_gen (const screen &s) |
| Display-list generator. | |
Public Attributes | |
| color | c |
| The color of the the point. | |
| point | start |
| The endpoints of the vector. | |
| point | end |
| void glot::vector::dl_gen | ( | const screen & | s | ) | [virtual] |
Display-list generator.
| s | - the screen specs |
Implements glot::primitive.
The endpoints of the vector.


statsmodels.tsa.statespace.varmax.VARMAXResults.test_heteroskedasticity¶
-
VARMAXResults.
test_heteroskedasticity
(method, alternative='two-sided', use_f=True)¶ Test for heteroskedasticity of standardized residuals
Tests whether the sum-of-squares in the first third of the sample is significantly different than the sum-of-squares in the last third of the sample. Analogous to a Goldfeld-Quandt test.
Parameters: method : string {‘breakvar’} or None
The statistical test for heteroskedasticity. Must be ‘breakvar’ for test of a break in the variance. If None, an attempt is made to select an appropriate test.
alternative : string, ‘increasing’, ‘decreasing’ or ‘two-sided’
This specifies the alternative for the p-value calculation. Default is two-sided.
use_f : boolean, optional
Whether or not to compare against the asymptotic distribution (chi-squared) or the approximate small-sample distribution (F). Default is True (i.e. default is to compare against an F distribution).
Returns: output : array
An array with (test_statistic, pvalue) for each endogenous variable. The array is then sized (k_endog, 2). If the method is called as het = res.test_heteroskedasticity(), then het[0] is an array of size 2 corresponding to the first endogenous variable, where het[0][0] is the test statistic, and het[0][1] is the p-value.
Notes
The null hypothesis is of no heteroskedasticity. That means different things depending on which alternative is selected:
- Increasing: Null hypothesis is that the variance is not increasing throughout the sample; that the sum-of-squares in the later subsample is not greater than the sum-of-squares in the earlier subsample.
- Decreasing: Null hypothesis is that the variance is not decreasing throughout the sample; that the sum-of-squares in the earlier subsample is not greater than the sum-of-squares in the later subsample.
- Two-sided: Null hypothesis is that the variance is not changing throughout the sample. Both that the sum-of-squares in the earlier subsample is not greater than the sum-of-squares in the later subsample and that the sum-of-squares in the later subsample is not greater than the sum-of-squares in the earlier subsample.
For , the test statistic is:
where is the number of periods in which the loglikelihood was burned in the parent model (usually corresponding to diffuse initialization).
This statistic can be tested against an distribution. Alternatively, is asymptotically distributed according to ; this second test can be applied by passing asymptotic=True as an argument.
See section 5.4 of [R115] for the above formula and discussion, as well as additional details.
TODO
- Allow specification of
References
[R115] (1, 2) Harvey, Andrew C. 1990. Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.


Before Change
for i in range(10):
env.reset()
for _ in range(1000):
state, reward, terminal, _ = env.step(command)
def editor_multi_agent_example():
This editor example shows how to interact with holodeck worlds that have multiple agents.
After Change
for i in range(10):
env.reset()
start = time.time()
for _ in range(1000000):
env.set_state("uav0", [0, 0, 0], [0, 0, 40], [0, 0, 0], [0, 0, 0])
end = time.time()
print("Finished 10000 steps in ", end-start, " seconds. ", 1000/(end-start), " frames per second.")
def editor_multi_agent_example():
This editor example shows how to interact with holodeck worlds that have multiple agents.


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{% extends "bootstrap_layout.html" %} {% set active_page = "sternuserinput" %} {% block content %}
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---
layout: default
---
{% include base.html %}
Up-and-coming researchers
illuminate (why)
Modeling Matter(s)
LISTEN: how & why to preserve your podcast
{% for episode in site.data.content.episodes %}
{% if episode.episode_file %}
{% assign episode_path = 'details/'| append: episode.episode_file | append: '.md' %}
{% capture episode_content %}{% include_relative {{episode_path}} %}{% endcapture %}
{{ episode.name }}
Download episode{% if episode.episode_file %} | Read episode details{% endif %}{% if episode.exercises_link %} | Do the exercises{% endif %}
Ask our guests questions live during streamed seminars.
{{ event.when }}{% if event.when and event.where %} | {% endif %}{{ event.where }}
{{ event.when }}{% if event.when and event.where %} | {% endif %}{{ event.where }}


|
Project X Ecobot
|
#include <StateMachine.hpp>
Public Types | |
| enum | State : int { placeholderState = -1, initialScanForDebris = 0, approachDebris = 1, turnTowardsBin = 2, driveTowardsBin = 3, turnTowardsTarget = 4, done = 5 } |
| Enum state elements. More... | |
Public Member Functions | |
| StateMachine (bool startImmediately=false, bool useLowXAlgorithm=false) | |
| Constructor for StateMachine. More... | |
| void | imageRGBCallback (const sensor_msgs::ImageConstPtr &message) |
| Callback function to obtain the images. More... | |
| void | odometryCallback (const nav_msgs::Odometry::ConstPtr &message) |
| Callback function to obtain the odometry. More... | |
| void | depthCallback (const sensor_msgs::ImageConstPtr &depthMessage) |
| Callback function to obtain depth information from the images. More... | |
| double | readDepthData (unsigned int heightPos, unsigned int widthPos, sensor_msgs::ImageConstPtr depthImage) |
| Function for obtaining depth information at certain image pixel position. More... | |
| bool | pickupDebris (State endState=placeholderState, State startState=initialScanForDebris, double registeredDepth=0.0, double fakeDepth=-1.0) |
| Function for running the main ros loop. More... | |
| double | getRawDepth () |
| Getter function for taking depth as input. More... | |
| int | getState () |
| Getter function for reading robot's state. More... | |
| cv::Mat | getImage () |
| Getter function for taking in the image. More... | |
| double | getRobotXPos () |
| Getter function for obtaining robot's x position. More... | |
| double | getRobotYPos () |
| Getter function for obtaining robot's y position. More... | |
| double | getRobotYaw () |
| Getter function for obtaining robot's yaw angle (orientation) More... | |
| double | getDepth () |
| Getter function for obtaining depth read from camera. More... | |
| double | verifyAngle (double rawAngle) |
| Function for casting angle between 0 and 2PI. More... | |
| geometry_msgs::Twist | stop (geometry_msgs::Twist velocity) |
| Function to stop robot's motion. More... | |
| geometry_msgs::Twist | moveStraight (geometry_msgs::Twist velocity) |
| Function to move the robot forward (straight) More... | |
| geometry_msgs::Twist | turnRight (geometry_msgs::Twist velocity) |
| Function to turn the robot to the right. More... | |
| geometry_msgs::Twist | turnLeft (geometry_msgs::Twist velocity) |
| Function to turn the robot to the left. More... | |
| enum StateMachine::State : int |
|
explicit |
Constructor for StateMachine.
| Boolean | for starting immediately or waiting |
| Boolean | for choosing which algorithm to use |
| void StateMachine::depthCallback | ( | const sensor_msgs::ImageConstPtr & | depthMessage | ) |
Callback function to obtain depth information from the images.
| A | ros message which is the depth reading |
| double StateMachine::getDepth | ( | ) |
Getter function for obtaining depth read from camera.
| None |
| cv::Mat StateMachine::getImage | ( | ) |
Getter function for taking in the image.
| None |
| double StateMachine::getRawDepth | ( | ) |
Getter function for taking depth as input.
| None |
| double StateMachine::getRobotXPos | ( | ) |
Getter function for obtaining robot's x position.
| None |
| double StateMachine::getRobotYaw | ( | ) |
Getter function for obtaining robot's yaw angle (orientation)
| None |
| double StateMachine::getRobotYPos | ( | ) |
Getter function for obtaining robot's y position.
| None |
| int StateMachine::getState | ( | ) |
Getter function for reading robot's state.
| None |
| void StateMachine::imageRGBCallback | ( | const sensor_msgs::ImageConstPtr & | message | ) |
Callback function to obtain the images.
| A | ros message which is the RGB image |
| geometry_msgs::Twist StateMachine::moveStraight | ( | geometry_msgs::Twist | velocity | ) |
Function to move the robot forward (straight)
| Geometry | message velocity |
| void StateMachine::odometryCallback | ( | const nav_msgs::Odometry::ConstPtr & | message | ) |
Callback function to obtain the odometry.
| A | ros message which is odometry message |
| bool StateMachine::pickupDebris | ( | State | endState = placeholderState , |
| State | startState = initialScanForDebris , |
||
| double | registeredDepth = 0.0 , |
||
| double | fakeDepth = -1.0 |
||
| ) |
Function for running the main ros loop.
| State | enum for a placeholderState |
| State | enum for the end state |
| Depth | parameter, added for testing purposes |
| Depth | parameter, added for testing purposes |
| double StateMachine::readDepthData | ( | unsigned int | heightPos, |
| unsigned int | widthPos, | ||
| sensor_msgs::ImageConstPtr | depthImage | ||
| ) |
Function for obtaining depth information at certain image pixel position.
| Image | pixel height |
| Image | pixel wdith |
| Depth | image from RGB-D camera |
| geometry_msgs::Twist StateMachine::stop | ( | geometry_msgs::Twist | velocity | ) |
Function to stop robot's motion.
| Geometry | message velocity |
| geometry_msgs::Twist StateMachine::turnLeft | ( | geometry_msgs::Twist | velocity | ) |
Function to turn the robot to the left.
| Geometry | message velocity |
| geometry_msgs::Twist StateMachine::turnRight | ( | geometry_msgs::Twist | velocity | ) |
Function to turn the robot to the right.
| Geometry | message velocity |
| double StateMachine::verifyAngle | ( | double | rawAngle | ) |
Function for casting angle between 0 and 2PI.
| An | angle in radians |


This is the estimated number of people in modern slavery globally. Here are the 20 countries with the highest estimated proportion of slaves among their pupulation. Together they account for 20.221.900 modern slaves, more than half of the total amount of modern slaves in the World. Data from The Global Slavery Index.


Discrete Dynamical Systems
Negative Binomial Dynamical System. The details of the model can be found in R. Kalantari and M. Zhou, "Graph Gamma Process Generalized Linear Dynamical Systems," arXiv preprint arXiv:2007.12852, July 2020.
© August 2020 Mingyuan Zhou


About
A programming team from Fu Jen University.
Team Members
|
roy4801 Fightだよ！
|
|||
|
ICEJJ 穩定單身中
|
|||
|
SunTalk 不做讓自己後悔的決定
|
|||
|
halloworld 📚Learning...
|
Projects
- RISH OJ
- A Online Judge system for competitive programming
- Group project of Web Funamental
- screenshot
- A Online Judge system for competitive programming
- Course Helper


Merit aims to be the world's friendliest digital currency.
You can send Merit to anyone in the world, whether they have a wallet or
not.
Merit is simple.
With easy-to-use Web Wallets, as well as Desktop and Mobile downloadable
applications for every major operating
system, Merit is everywhere you are.
Merit is safe.
You can create a Vault to keep your Merit even more secure. Both vaults and
wallets use cutting-edge security.
Remember, you control your Merit; no one else has access to it (not even us!)


Once initialized, you can use the following functions to create, edit and delete salesforce records. Refer to the Rest API Guide and the Object Reference for Salesforce for the correct field names and validation rules.
//edit function cnv.editSalesforce(object,request,callback) //delete function cnv.deleteSalesforce(object, id, callback)
If the Id property is set in the request object, then the function will PATCH the specified record with the request object. If the Id property is not included in the request object, then a new record will be created via POST.
//create a new Task for tomorrow var due = new Date(); //for tasks, we just pass the date part of the string. due.setDate(due.getDate()+1).toISOString(); var request = {'ActivityDate':due.substring(0,10), 'Subject':'Test Task From the canvas-starter-kit'} cnv.editSalesforce('Task', request, callback);


Rule sections such as input/output/params/..., cannot be used after run/shell/script/wrapper/cwl/notebook/template_engine sections.


Football: Icardi strikes again to edge PSG past Brest
PARIS: Mauro Icardi came off the bench to score a late winner as Paris Saint-Germain extended their Ligue 1 lead to 10 points despite another unconvincing performance in a 2-1 victory at Brest on Saturday (Nov 9).
The reigning French champions, who were beaten by then-bottom club Dijon last week before scraping past Club Brugge in the Champions League, needed Icardi's ninth goal in eight games to secure the three points.
Brest, promoted from the second tier last season, caused PSG problems throughout and had levelled with 18 minutes remaining through Samuel Grandsir.
Second-placed Angers visit Reims later on Saturday, looking to cut the gap to seven points.
PSG coach Thomas Tuchel saw his long injury list extended before kick-off as goalkeeper Keylor Navas pulled out of the game after suffering a thigh problem in the warm-up.
The Costa Rican joined Neymar, Kylian Mbappe, defenders Thomas Meunier and Thilo Kehrer and midfielder Ander Herrera on the sidelines.
Edinson Cavani made his first start since August, but struggled in front of goal.
The visitors claimed a 39th-minute lead as Angel Di Maria raced clear before clipping a chipped finish over Brest goalkeeper Gautier Larsonneur to score his eighth goal of the campaign.
His effort survived a VAR review for offside to send PSG into the interval ahead.
Cavani wasted two clear-cut chances in the space of three minutes just after the hour mark, and Brest made him pay.
Substitute Grandsir collected the ball inside the area and fired a low effort through goalkeeper Sergio Rico, making his Parisian debut after signing on-loan from Sevilla.
But the Spaniard redeemed himself shortly afterwards, keeping out Haris Belkebla's long-range strike.
On-loan Inter Milan striker Icardi had the final say, though, pouncing inside the six-yard box in the 85th minute to scramble the ball over the line after Eric Maxim Choupo-Moting's cross was only partially cleared.


Create your QR code for free with your settings QR Code Settings The QR Code Level of error correction Level H (High) Level Q (Quartile) Level M (Medium) Level L (Low) Width Height Line color Background color Generate QR Code Download Print


Clownfish or anemonefish are fishes from the subfamily Amphiprioninae in the family Pomacentridae. Thirty species are recognized: one in the genus Premnas, while the remaining are in the genus Amphiprion.
They are overall yellow, orange, or a reddish or blackish color, and many show white bars or patches.


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Quantitative data in PRIDE XML
When the PRIDE XML format was developed quantitative methods for MS based proteomics experiments were not widely used. Therefore, the PRIDE XML format does not inherently support the storage of quantitative results. Using a feature present in PRIDE XML that allows one to store additional data for proteins and peptides we defined away that enables us to nevertheless store simple quantitative results in PRIDE XML files.
New methods to quantify proteins in MS based proteomics are constantly being developed. Therefore, it is very difficult to define a common way to represent this heterogeneous data. The PRIDE database is focused on storing proteomics data that is part of a manuscript or publication. Therefore, we decided to build the support for quantitative data in PRIDE XML files around the requirements for this specific task.
Supported quantitative data
PRIDE XML only supports the storage of final quantitative results but not any intermediate results generated during the processing of the raw data. For every protein and peptide one single value can be stored per multiplexed sample (if applicable). In a 4-plex iTRAQ experiment, for example, it is thus possible to report one value per reporter ion per peptide and protein. This thus results in 4 quantification values per protein and peptide.
The stored quantitative values can either be the final calculated ratios (with the quantitative value for the reference sample set to 1) or the raw measured reporter ion intensities. This primarily depends on which of these numbers are considered "final" by the submitter.


#include "sha1.h"
#include <assert.h>
#include <string.h>
Go to the source code of this file.
Data Structures | |
| union | CHAR64LONG16 |
Defines | |
| #define | SHA1HANDSOFF |
| #define | rol(value, bits) (((value) << (bits)) | ((value) >> (32 - (bits)))) |
| #define | blk0(i) |
| #define | blk(i) |
| #define | R0(v, w, x, y, z, i) z+=((w&(x^y))^y)+blk0(i)+0x5A827999+rol(v,5);w=rol(w,30); |
| #define | R1(v, w, x, y, z, i) z+=((w&(x^y))^y)+blk(i)+0x5A827999+rol(v,5);w=rol(w,30); |
| #define | R2(v, w, x, y, z, i) z+=(w^x^y)+blk(i)+0x6ED9EBA1+rol(v,5);w=rol(w,30); |
| #define | R3(v, w, x, y, z, i) z+=(((w|x)&y)|(w&x))+blk(i)+0x8F1BBCDC+rol(v,5);w=rol(w,30); |
| #define | R4(v, w, x, y, z, i) z+=(w^x^y)+blk(i)+0xCA62C1D6+rol(v,5);w=rol(w,30); |
Functions | |
| void | SHA1_Transform (uint32_t state[5], const uint8_t buffer[64]) |
| void | SHA1_Init (SHA1_CTX *context) |
| void | SHA1_Update (SHA1_CTX *context, const uint8_t *data, size_t len) |
| void | SHA1_Finish (SHA1_CTX *context, uint8_t digest[20]) |
| #define blk | ( | i | ) |
| #define blk0 | ( | i | ) |
| #define R0 | ( | v, | |||
| w, | |||||
| x, | |||||
| y, | |||||
| z, | |||||
| i | ) | z+=((w&(x^y))^y)+blk0(i)+0x5A827999+rol(v,5);w=rol(w,30); |
Definition at line 64 of file sha1.c.
Referenced by des_cipher(), and SHA1_Transform().
| #define R1 | ( | v, | |||
| w, | |||||
| x, | |||||
| y, | |||||
| z, | |||||
| i | ) | z+=((w&(x^y))^y)+blk(i)+0x5A827999+rol(v,5);w=rol(w,30); |
Definition at line 65 of file sha1.c.
Referenced by des_cipher(), and SHA1_Transform().
| #define R2 | ( | v, | |||
| w, | |||||
| x, | |||||
| y, | |||||
| z, | |||||
| i | ) | z+=(w^x^y)+blk(i)+0x6ED9EBA1+rol(v,5);w=rol(w,30); |
Definition at line 66 of file sha1.c.
Referenced by SHA1_Transform().
| #define R3 | ( | v, | |||
| w, | |||||
| x, | |||||
| y, | |||||
| z, | |||||
| i | ) | z+=(((w|x)&y)|(w&x))+blk(i)+0x8F1BBCDC+rol(v,5);w=rol(w,30); |
Definition at line 67 of file sha1.c.
Referenced by SHA1_Transform().
| #define R4 | ( | v, | |||
| w, | |||||
| x, | |||||
| y, | |||||
| z, | |||||
| i | ) | z+=(w^x^y)+blk(i)+0xCA62C1D6+rol(v,5);w=rol(w,30); |
Definition at line 68 of file sha1.c.
Referenced by SHA1_Transform().
| #define rol | ( | value, | |||
| bits | ) | (((value) << (bits)) | ((value) >> (32 - (bits)))) |
Definition at line 247 of file sha1.c.
References _DIAGASSERT, SHA1_CTX::count, SHA1_Update(), and SHA1_CTX::state.
| void SHA1_Init | ( | SHA1_CTX * | context | ) |
Definition at line 202 of file sha1.c.
References _DIAGASSERT, SHA1_CTX::count, and SHA1_CTX::state.
Definition at line 132 of file sha1.c.
References _DIAGASSERT, R0, R1, R2, R3, and R4.
Referenced by SHA1_Update().
Definition at line 220 of file sha1.c.
References _DIAGASSERT, SHA1_CTX::buffer, SHA1_CTX::count, SHA1_Transform(), and SHA1_CTX::state.
Referenced by SHA1_Finish().


Trains a go-go
Chatanooga cHoO cHoO!
Train name
Destination
First train time
Frequency
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Minutes remaining
Add a new train route below!
Train
Destination
First train time (HH:mm - military time)
Frequency (min)
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Vernal Summer leaves fall
May 17, 2009
-- The world is not hard to see,
for each one of us cannot help but be a part of it.
We are each a participle for a world, the predicate of our actions.
The world we are, and so worldly we be.
We spawn worldly places,
villages, cultures, concepts and ideas.
We pass like the days we change with the seasons.
not out of touch with human nature can we ever be.
Summer’s triumphs always fall as the leaves do.
Winter’s over only to rise again in the spring where vernal life
turns over vernal death to the other side- [undo the knots]
How each spring does pertain to much more than warm light;
for each spring is a beginning
that has no future beyond that one summer.
Is not each summer the one and only?
As if it did happen again and again over and over.
Always changing the figures the objects
It all never was anything more than faces in the clouds.


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| Away Team |
Home Team |
Time (CST) |
Forecast |
| Minnesota Vikings |
New Orleans Saints |
Friday, December 25 3:30 PM |
|
| Tampa Bay Buccaneers |
Detroit Lions |
Saturday, December 26 12:00 PM |
|
| San Francisco 49ers |
Arizona Cardinals |
Saturday, December 26 3:30 PM |
|
| Miami Dolphins |
Los Vegas Raiders |
Saturday, December 26 7:15 PM |
|
| New York Giants |
Baltimore Ravens |
Sunday, December 27 12:00 PM |
|
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Kansas City Chiefs |
Sunday, December 27 12:00 PM |
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Monday, Decmeber 28 7:15 PM |
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Defiance
A percentage of all damage inflicted by enemies will be revisited back upon them for a short duration after using Rally.
${value} Energy Cost
8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38
${value}% Damage Reflected
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80
${value}% Elemental Resistance
10 14 17 21 24 28 31 35 38 42 45 50 53 56 60 65
MaxLevel
12
UltimateLevel
16


[ aws . storagegateway ]
This operation activates the gateway you previously deployed on your VMware host. For more information, see Downloading and Deploying AWS Storage Gateway VM . In the activation process you specify information such as the region you want to use for storing snapshots, the time zone for scheduled snapshots and the gateway schedule window, an activation key, and a name for your gateway. The activation process also associates your gateway with your account (see update-gateway-information ).
 You can obtain the activation key by sending an HTTP GET request with redirects enabled to the gateway IP address (port 80). The redirect URL returned in the response provides you the activation key for your gateway in the query string parameter activationKey . It may also include other activation-related parameters, however, these are merely defaults -- the arguments you pass to the activate-gateway API call determine the actual configuration of your gateway.
A unique identifier for your gateway. This name becomes part of the gateway Amazon Resources Name (ARN) which is what you use as an input to other operations.
One of the values that indicates the time zone you want to set for the gateway. The time zone is used, for example, for scheduling snapshots and your gateway's maintenance schedule.
One of the values that indicates the region where you want to store the snapshot backups. The gateway region specified must be the same region as the region in your Host header in the request. For more information about available regions and endpoints for AWS Storage Gateway, see Regions and Endpoints in the Amazon Web Services Glossary .
Valid Values : "us-east-1", "us-west-1", "us-west-2", "eu-west-1", "ap-northeast-1", "ap-southest-1", "sa-east-1"
One of the values that defines the type of gateway to activate. The type specified is critical to all later functions of the gateway and cannot be changed after activation. The default value is STORED .


Rizzo Fitness
By Carlo Rizzo
Results start when you do
When it comes to starting a diet and working out, we always tell ourselves we'll start tomorrow or we'll start on Monday. This mindset will not get you far when it comes to achieving the results you want.
Results start when you do
When it comes to starting a diet and working out, we always tell ourselves we'll start tomorrow or we'll start on Monday. This mindset will not get you far when it comes to achieving the results you want.


{% extends "!layout.html" %} {% block content %}
Warning
These docs are a work in progress, and are not ready for public consumption yet. Please stay tuned for a release announcement regarding these docs at https://forum.qiime2.org/c/announcements. This banner will be removed when the announcement is made.


xpmtoppm(1) xpmtoppm(1) NAME xpmtoppm - convert an X11 pixmap into a portable pixmap SYNOPSIS xpmtoppm [--alphaout={alpha-filename,-}] [-verbose] [xpmfile] DESCRIPTION Reads an X11 pixmap (XPM version 1 or 3) as input. Produces a PPM file as output. OPTIONS --alphaout=alpha-filename xpmtoppm creates a PBM file containing the transparency mask for the image. If the input image doesn't contain transparency infor‐ mation, the alpha-filename file contains all white (opaque) alpha values. If you don't specify --alphaout, xpmtoppm does not gener‐ ate an alpha file, and if the input image has transparency infor‐ mation, xpmtoppm simply discards it. If you specify - as the filename, xpmtoppm writes the alpha output to Standard Output and discards the image. See pnmcomp(1) for one way to use the alpha output file. --verbose xpmtoppm prints information about its processing on Standard Error. LIMITATIONS The support to XPM version 3 is limited. Comments can only be single lines and there must be for every pixel a default colorname for a color type visual. SEE ALSO ppmtoxpm(1), pnmcomp(1), ppm(5) AUTHOR Copyright (C) 1991 by Jef Poskanzer. Upgraded to support XPM version 3 by Arnaud Le Hors<EMAIL_ADDRESS>Tue Apr 9 1991 06 May 2001 xpmtoppm(1)


Wife of disgraced Tory sexting MP says she won't accept his support after replacing him
Kate Griffiths said her relationship with disgraced husband Andrew ended “on the day” he was exposed by the Sunday Mirror after sending a barmaid and her pal £700 and offering to rent a flat to meet for sex
by Oliver Milne, https://www.mirror.co.uk/authors/oliver-milne/The estranged wife of a former minister exposed by the Sunday Mirror for sending thousands of sexts while she was at home looking after their daughter has said she won’t accept his support after replacing him as a Tory candidate.
Kate Griffiths said her relationship with disgraced husband Andrew ended “on the day” he was exposed by the Sunday Mirror after sending a barmaid and her pal £700 and offering to rent a flat to meet for sex.
In a seeming act of revenge, she has replaced him as the Conservative’s candidate in Burton and Uttoxeter.
Mrs Griffiths said in a statement: “The divorce is being finalised.
“I am not able to say more about this now as legal proceedings are ongoing but I want to make it clear that I have not sought, nor do I accept, Andrew’s offer of political support.”
On Monday, Mr Griffith’s association voted on readopting him as their candidate, but it ended in a tie 117-117. His wife was not allowed to speak in that meeting.
A constituency insider told the Mirror: “She is furious, and quite rightly, and he was terrified of what she would reveal about him. It was like an episode of Emmerdale.”
The decision not to back him triggered a selection contest, which saw Mrs Griffiths add herself to the shortlist.
Mr Griffiths then announced he was standing down, hours before his wife won the selection.
Tory campaign insiders described the result as “vengeance” and said that the former MP had been told to pull out before the second meeting.
Speaking yesterday, Mrs Griffiths said she had emerged from the episode stronger.
“In the past 18 months I have found strength and resilience which I didn’t know I had, borne out of self-respect, dignity and the fierce desire to protect my daughter,” she said.
Mr Griffiths sent 2,000 texts in all, referring to himself as “Daddy” and outlining sex acts.


Maya¶
To setup Maya so ZLayerManager can export and import meshes from it, you have to edit the userSetup.py file located in a scripts folder under Maya application directory.
This is usually located here: C:\Users\{username}\Documents\maya\scripts.
If you can’t find this folder you can execute this mel code in Maya to retrieve it.
getenv "MAYA_APP_DIR"
Locate ZStartup\ZPlugs64\ZlmData\app\maya folder under Zbrush installation folder. Copy the path, replace it in the code bellow then add it to the userSetup.py file. (The file might not exists so you’ll need to create one)
# put you path between the quotes. ex: zlm_maya_app = r"YOUR PATH HERE"
zlm_maya_app = r"C:\Program Files\Pixologic\ZBrush 2018\ZStartup\ZPlugs64\ZlmData\app\maya"
# add zlm maya script folder to sys path
import sys
sys.path.append(zlm_maya_app)
import zlm
# open communication port so maya can receive command
zlm.open_port()
Restart maya.
You are now setup to use Maya with Zbrush and Zlm.


| Contract Entity | Contract Name | Contract ID | Version | Category | Start Date | End Date | |
|---|---|---|---|---|---|---|---|
| Caremark MCO | CVS Caremark MCO 2015 | 1 | Active | 1/1/2015 | 7/31/2015 | ||
| Caremark MCO | CVS Caremark MCO 2015 | 2 | Active | 8/1/2015 | 12/31/2015 | ||
| Caremark MCO | Caremark 2016 MCO | 1 | Active | 1/1/2016 | 12/31/2016 |


What is going on?
Things you might want to know.Oh, Hello!
Hello you! I'm Elli and this is a web site where I upload my little web practices. I created this site to keep all the little practice works I do together so they are easy to find, use and look at. Bigger projects you can find from my Github profile.
Here is some places you can find me and might want to check out:
Or send me email yksisarvellinen[at]gmail.com


Data for this analysis has been taken from Election
Commission of India
TN Lokshaba 2019 Highlights Visualization
- Constituency with most number of party candidates is ChennaiCentral with 13 candidates
- Constituency with most number of independent candidate is Karur with 31 candidates
- Constituency with most contesting candidates is Karur with 43 candidates
- Candidate with closest win margin is THOL THIRUMAAVALAVAN of VCK with difference of 3219 votes
- Candidate with wide margin is VELUSAMY, P. of DMK with a difference of 538972 votes. He has got 64.35% of 1160046 total vote casted
There were 57 parties contesting in this election
Some stats on how well each party performed


Eagles guard Brooks reveals anxiety 'setback'
by OmnisportPhiladelphia Eagles guard Brandon Brooks has revealed that a bout of anxiety prevented him from returning to the field in a defeat against the Seattle Seahawks on Sunday.
Brooks departed in a 17-9 loss at Lincoln Financial Field and was unable to return.
The Super Bowl champion and two-time Pro Bowler explained his absence on Monday after the Eagles stated that he was unable continue due to "illness".
"I'd like to address what happened yesterday. I woke up and did my typical routine of morning vomiting. It didn't go like it normally does, but I figured it would calm down once I got to the stadium. It did, but I felt exhausted," he tweeted.
"The nausea came back, and I tried to battle through it and went out for the first drive. The nausea and vomiting came back until I left the field and I tried everything I could to get back for my team-mates but just wasn't able to do it.
"Make no mistake, I'm NOT ashamed or embarrassed by this nor what I go through daily. I've had this under control for a couple of years and had a setback yesterday.
"The only thing I'm upset about is that when my team needed me, I wasn't able to be out there with and for them. Lastly, I appreciate the support of my coaches, team-mates and fans. It doesn't go unnoticed."
Head coach Doug Pederson said the Eagles will do all they can to support Brooks.
"I'll just say this, I'm not going to get into a lot of detail with that. Because, listen, this is a real-life issue. This is not a football issue with Brandon," he told SportsRadio WIP.
"This is a real-life issue that he has come out and publicly acknowledged and kind of shared his story a few years back. It's something that he's dealing with each and every day of his life. You never really know what triggers it.
"We're here to support him, we love him.  We're just going to continue to support him."


User Experience Design
The term "user experience" was first coined by Don Norman in the early 1990s when he was working at Apple (NORMAN, 2013, P. XIII F.). User experience comprises all aspects of a user’s interaction with a company, its services, and its products. The focus and most important goal of good user experience is to meet the user’s needs in every aspects. Close cooperation is required between different disciplines, such as engineering, marketing, graphic design, industrial design, and interface design (NORMAN & NIELSEN, N.D.).
»Good design is actually a lot harder to notice than poor design, in part because good designs fit our needs so well that the design is invisible, serving us without drawing attention to itself. Bad design, on the other hand, screams out its inadequacies, making itself very noticeable.«
- Don Norman
User experience captures everything a user experiences while using a given product, from the visuals, the haptics, and the audio to how the product feels. User experience of a product comprises three primary factors: its look, feel, and usability (IXDF - UX DESIGN, N.D.) (→ FIG. 04).
User Experience
User Experience Design requires a solid understanding of the psychology and behavior of the user. Understanding how users perceive a product allows user experience designers to iterate their concepts and better achieve the user’s goals. According to Marc Hassenzahl and Sarah Diefenbach’s paper Well-being, Need Fulfillment, and Experience Design, any positive experience is based on meeting psychological needs. The fulfillment of psychological needs, such as competence, relatedness, popularity, stimulation and security, contributes to the well-being of the user and leads to a pleasant user experience. In experience design, these needs are taken as guidelines and inspiration in the design process (HASSENZAHL & DIEFENBACH, 2012, P. 1).
Some of these needs have already been discussed in Chapter 2.1, but in the following, they are considered from the perspective of emotion, while additional needs are addressed.
- Competence
- Competence is the feeling that one’s actions are perceived as capable and effective instead of thinking that one is incapable and incompetent (HASSENZAHL & DIEFENBACH, 2012, P. 1).
- Relatedness
- Relatedness is the feeling of having intimate and personal relationships with the people in the environment, rather than feeling lonely or unnoticed (HASSENZAHL & DIEFENBACH, 2012, P. 1).
- Popularity
- Popularity is the feeling of being respected and influencing others, rather than feeling like a person whose advice and opinion are ignored (HASSENZAHL & DIEFENBACH, 2012, P. 1).
- Stimulation
- Stimulation is the feeling of experiencing much of joy and pleasure instead of feeling bored and understimulated (HASSENZAHL & DIEFENBACH, 2012, P. 1).
- Security
- Security is the feeling of having stability and control over one’s own life, rather than feeling uncertain and threatened by circumstances one cannot control (HASSENZAHL & DIEFENBACH, 2012, P. 1).
In the context of user experience design, needs are also called “user needs”. In addition to the approach of Hassenzahl and Diefenbach, other approaches define user needs for good user experience. Design leader Stephen P. Anderson, for example, developed the “User Experience Hierarchy of Needs” model (→ FIG. 05). Rather than focusing on the psychological side of the user needs, this model is based on a product’s maturity process. Anderson proposes that products proceed through six stages of maturity, during which they can grow from a purely functional product, to a product that is meaningful to the user (ANDERSON, 2011, P. 10F.).
| Main Focus | Need | Note |
| Experience | Meaningful | personal significance |
| Pleasurable | memorable experiences | |
| Convinient | super easy to use, works like I think | |
| Tasks | Usable | can be used without difficulty |
| Reliable | is available and accurate | |
| Functional | works as programmed |


{% extends "common/base.html" %} {% block navbar %}{% include "common/navbar.html" %}{% endblock %} {% block content %}
Thank you for your response! We have closed the submission form. Please email<EMAIL_ADDRESS>if you need to make any changes.


I am a Ph.D. candidate in physics at the University of Texas at Austin. My research interests are in primordial cosmology and cosmological probes of the dark sector. I am a determined individual, always willing to learn and explore new research topics.
My work as a researcher centered on the analysis and classification of the latest cluster catalog from the Atacama Cosmology Telescope (ACT) collaboration, with the goal of detecting the lensing induced cluster signature through a CMB gradient oriented stacking.
As a teaching assistant, I supported the labs and homework administration and grading for the physics introductory course in mechanics (Fall) and electromagnetism (Spring).
My work centered on the development of a machine learning (ML) algorithm using a partial convolutional neural network (PCNNs) to in-paint masked images of the cosmic microwave background (CMB). The network we developed can reconstruct both the maps and the power spectra to a few percent for circular and irregularly shaped masks covering up to ~10% of the image area.
Finally, our model also outperforms the other recently developed ML methods that inpaint pure CMB maps. For more info click on the link below:
Inpainting CMB maps using Partial Convolutional Neural NetworksI worked as a research assistant in the photo-electronics group for the DarkSide-20k experiment, a 20-ton liquid argon detector to search for WIMP dark matter particles held at LNGS. Specifically, I was in charge of testing and analyzing different Silicon PhotoMultipliers (SiPMs) variants and their integrated electronics.
I consulted over 30 students from a variety of disciplines to develop an individualized and strategic approach to learning that enables the student to make the most of lectures, precepts, and readings; to manage their time; and to achieve their goals while maintaining a healthy balance.
GPA: 4.0
GPA: 3.75
Score: 93/100
1-semester student exchange program
In my free time, I enjoy cooking typical Italian dishes. Whenever I can, I spend time outdoors, running, hiking and riding my motorcycle.


We don't track your online behaviour, we won’t show you ads or promoted tweets. We respect you and your freedom.
We are using Nitter behind the scenes. No Twitter account required. No collection of personal data. We care about you, not your personal data.
Fast and distraction-free Twitter experience 10x smaller script Mobile First, optimized for the Web
Yes! Decent is open-source on GitHub You can opt for self-hosting Decent You are free to contribute!


PropWare
C++ objects and CMake build system for Parallax Propeller
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{% if endroute == 0 %}
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Brands:
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The solar system consists of:
a star - the Sun
planets and dwarf planets in orbit around the Sun
satellite moons in orbit around most of the planets
comets and asteroids in orbit around the Sun
There are eight planets, including the Earth, and smaller dwarf planets, such as Pluto, Ceres and Eris.
The Sun's gravity keeps the planets, dwarf planets, comets and asteroids in orbit. The gravity of a planet keeps its own satellites in orbit.
The planets take different amounts of time to go around the Sun. A single orbit is called the planet's year, and the further out a planet is the longer its year takes.
The orbits of the planets in the solar system are almost circular – with the Sun near the centre. Many diagrams - including these here - show the orbits very squashed from top to bottom. This is to give a sense of perspective or to fit the diagram to a page.


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1. Open this test file with a browser other than Safari (See the note below) 2, Click the "Date" input field 3. Press the tab key 4. See if the focus moves to the "Text" field Note: When the user pressed the tab key, Safari moves the focus to the next text input element, ignoring buttons, checkboxes/radio buttons. For that reason, the issue does not occur on Safari.


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Hello, I'm Sohan Lal Gupta.
I'm a MERN stack web developer.
Hello! My name is Sohan Lal Gupta. I am a MERN stack web developer. I love to build websites. I help others bring their ideas to life.
I am an undergraduate student at Indian Institute of Information Technology Guwahati, studying Computer Science. I also love to solve algorithmic problems. So, I am a competitive programmer.
I also write blogs on Medium. I am a sincere & hardworking person. I read books in my spare time.


{% extends "layout.html" %}
{% block title %}Privacy{% endblock %}
{% block body %}
Copyright & Attribution
- Search results come from 3 locations:
- Google Custom Search API
- Affiliate feed data provided by lenders as part of an affiliate relationship/network.
- Spidering the web for deals to augment the existing results.
- Site content besides the results is copyright Doug Bromley.
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Although under copyright the source code for the site is
released under the open source MIT license. More information can be found
here
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Note
This tutorial was generated from a Jupyter notebook that can be downloaded here.
Eclipsing binary: pymc3
solution for the maps¶
In this notebook, we’re going to do MCMC to infer the surface maps of two stars in an eclipsing binary given the light curve of the system. We generated the data in this notebook. Note that here we assume we know everything else about the system (the orbital parameters, the limb darkening coefficients, etc.), so the only unknown parameters are the maps of the two stars, which are expressed in starry
as vectors of spherical harmonic coefficients. In a
future tutorial we’ll explore a more complex inference problem where we have uncertainties on all the parameters.
Let’s begin with some imports. Note that in order to do inference with pymc3, we need to enable lazy evaluation. That’s because pymc3
requires derivatives of the likelihood function, so we need to use the fancy theano
computational graph to perform backpropagation on the starry
model. All this means in practice is that we’ll have to call .eval()
in some places to get numerical values out of the parameters.
[3]:
import matplotlib.pyplot as plt
import numpy as np
import pymc3 as pm
import exoplanet as xo
import os
import starry
from corner import corner
np.random.seed(12)
starry.config.lazy = True
starry.config.quiet = True
Load the data¶
Let’s load the EB dataset:
[5]:
data = np.load("eb.npz", allow_pickle=True)
A = data["A"].item()
B = data["B"].item()
t = data["t"]
flux = data["flux"]
sigma = data["sigma"]
Next, we instantiate the primary, secondary, and system objects. Recall that we assume we know the true values of all the orbital parameters and star properties, except for the two surface maps. Note that we are instantiating the starry
objects within a pm.Model()
context so that pymc3
can keep track of all the variables.
[6]:
with pm.Model() as model:
# Primary
pri = starry.Primary(
starry.Map(ydeg=A["ydeg"], udeg=A["udeg"], inc=A["inc"]),
r=A["r"],
m=A["m"],
prot=A["prot"],
)
pri.map[1:] = A["u"]
# Secondary
sec = starry.Secondary(
starry.Map(ydeg=B["ydeg"], udeg=B["udeg"], inc=B["inc"]),
r=B["r"],
m=B["m"],
porb=B["porb"],
prot=B["prot"],
t0=B["t0"],
inc=B["inc"],
)
sec.map[1:] = B["u"]
# System
sys = starry.System(pri, sec)
Here’s the light curve we’re going to do inference on:
[7]:
fig, ax = plt.subplots(1, figsize=(12, 5))
ax.plot(t, flux, "k.", alpha=0.5, ms=4)
ax.set_xlabel("time [days]", fontsize=24)
ax.set_ylabel("normalized flux", fontsize=24);
Define the pymc3
model¶
Now we define the full pymc3
model. If you’ve never used pymc3
before, Dan Foreman-Mackey’s exoplanet package documentation has lots of nice tutorials on how to use pymc3
to do inference. The basic idea here is we define our variables by assigning priors to them; we use a pm.MvNormal
for both the primary and secondary maps. This is a multi-variate normal (Gaussian) distribution, which happens to be a convenient prior to place on spherical
harmonic coefficients because of its close relationship to the power spectrum of the map. In particular, if the Gaussian prior is zero-mean and its covariance is diagonal with constant entries for each degree \(l\) (as we assume below), this is equivalent to an isotropic prior whose power spectrum is given by those entries on the diagonal. Note that for simplicity we are assuming a flat power spectrum, meaning we place the same prior weight on all spatial scales. So the covariance of our
Gaussian is as simple as it can be: it’s just \(\lambda I\), where \(\lambda = 10^{-2}\) is the prior variance of the spherical harmonic coefficients and \(I\) is the identity matrix. The scalar \(\lambda\) is essentially a regularization parameter: by making it small, we ensure that the spherical harmonic coefficients stay close to zero, which is usually what we want for physical maps.
You’ll note there’s also a call to pm.Deterministic
, which just keeps track of variables for later (in this case, we’ll have access to the value of flux_model
for every iteration of the chain once we’re done; this is useful for plotting). And finally, there’s a call to pm.Normal
in which we specify our observed
values, their standard deviation sd
, and the mean vector mu
, which is our starry
flux model. This normal distribution is our chi-squared term: we’re telling
pymc3
that our data is normally distributed about our model with some (observational) uncertainty.
[8]:
with pm.Model() as model:
# The amplitude of the primary
pri.map.amp = pm.Normal("pri_amp", mu=1.0, sd=0.1)
# The Ylm coefficients of the primary
# with a zero-mean isotropic Gaussian prior
ncoeff = pri.map.Ny - 1
pri_mu = np.zeros(ncoeff)
pri_cov = 1e-2 * np.eye(ncoeff)
pri.map[1:, :] = pm.MvNormal("pri_y", pri_mu, pri_cov, shape=(ncoeff,))
# The amplitude of the secondary
sec.map.amp = pm.Normal("sec_amp", mu=0.1, sd=0.01)
# The Ylm coefficients of the secondary
# with a zero-mean isotropic Gaussian prior
ncoeff = sec.map.Ny - 1
sec_mu = np.zeros(ncoeff)
sec_cov = 1e-2 * np.eye(ncoeff)
sec.map[1:, :] = pm.MvNormal("sec_y", sec_mu, sec_cov, shape=(ncoeff,))
# Compute the flux
flux_model = sys.flux(t=t)
# Track some values for plotting later
pm.Deterministic("flux_model", flux_model)
# Save our initial guess
# See http://exoplanet.dfm.io/en/stable/user/api/#exoplanet.eval_in_model
flux_model_guess = xo.eval_in_model(flux_model)
# The likelihood function assuming known Gaussian uncertainty
pm.Normal("obs", mu=flux_model, sd=sigma, observed=flux)
Now that we’ve specified the model, it’s a good idea to run a quick gradient descent to find the MAP (maximum a posteriori) solution. This will give us a decent starting point for the inference problem.
[9]:
%%time
with model:
map_soln = xo.optimize()
optimizing logp for variables: [sec_y, sec_amp, pri_y, pri_amp]
494it [00:02, 186.49it/s, logp=6.285776e+03]
CPU times: user 5.31 s, sys: 1.35 s, total: 6.65 s
Wall time: 43.2 s
message: Desired error not necessarily achieved due to precision loss.
logp: -33047316.911008343 -> 6285.775857961193
Note the dramatic increase in the value of the log posterior! Let’s plot the MAP model alongside the data and the initial guess (note that we’re doing quite well).
[10]:
plt.figure(figsize=(12, 5))
plt.plot(t, flux, "k.", alpha=0.3, ms=2, label="data")
plt.plot(t, flux_model_guess, "C1--", lw=1, alpha=0.5, label="Initial")
plt.plot(
t, xo.eval_in_model(flux_model, map_soln, model=model), "C1-", label="MAP", lw=1
)
plt.legend(fontsize=10, numpoints=5)
plt.xlabel("time [days]", fontsize=24)
plt.ylabel("relative flux", fontsize=24);
We can also plot the corresponding maps: note that we recover the spots really well!
[11]:
map = starry.Map(ydeg=A["ydeg"])
map.inc = A["inc"]
map.amp = map_soln["pri_amp"]
map[1:, :] = map_soln["pri_y"]
map.show(theta=np.linspace(0, 360, 50))
[12]:
map = starry.Map(ydeg=B["ydeg"])
map.inc = B["inc"]
map.amp = map_soln["sec_amp"]
map[1:, :] = map_soln["sec_y"]
map.show(theta=np.linspace(0, 360, 50))
MCMC sampling¶
We have an optimum solution, but we’re really interested in the posterior over surface maps (i.e., an understanding of the uncertainty of our solution). We’re therefore going to do MCMC sampling with pymc3
. This is easy: within the model
context, we just call pm.sample
. The number of tuning and draw steps below are quite small since I wanted this notebook to run quickly; try increasing them by a factor of a few to get more faithful posteriors.
You can read about the get_dense_nuts_step
convenience function (which really helps the sampling when degeneracies are present) here.
[13]:
%%time
with model:
trace = pm.sample(
tune=500,
draws=500,
start=map_soln,
chains=4,
step=xo.get_dense_nuts_step(target_accept=0.9),
)
Sequential sampling (4 chains in 1 job)
NUTS: [sec_y, sec_amp, pri_y, pri_amp]
Sampling chain 0, 40 divergences: 100%|██████████| 1000/1000 [03:40<00:00, 4.53it/s]
Sampling chain 1, 13 divergences: 100%|██████████| 1000/1000 [03:01<00:00, 5.51it/s]
Sampling chain 2, 5 divergences: 100%|██████████| 1000/1000 [03:26<00:00, 4.83it/s]
Sampling chain 3, 26 divergences: 100%|██████████| 1000/1000 [03:13<00:00, 5.17it/s]
There were 40 divergences after tuning. Increase `target_accept` or reparameterize.
The acceptance probability does not match the target. It is 0.7576036604307868, but should be close to 0.9. Try to increase the number of tuning steps.
There were 53 divergences after tuning. Increase `target_accept` or reparameterize.
The acceptance probability does not match the target. It is 0.8024727673914226, but should be close to 0.9. Try to increase the number of tuning steps.
There were 58 divergences after tuning. Increase `target_accept` or reparameterize.
The acceptance probability does not match the target. It is 0.8290602045619805, but should be close to 0.9. Try to increase the number of tuning steps.
There were 84 divergences after tuning. Increase `target_accept` or reparameterize.
The acceptance probability does not match the target. It is 0.8304876716026008, but should be close to 0.9. Try to increase the number of tuning steps.
The estimated number of effective samples is smaller than 200 for some parameters.
CPU times: user 15min 27s, sys: 1min 44s, total: 17min 12s
Wall time: 13min 41s
We can look at pm.summary
to check if things converged. In particular, we’re looking for a large number of effective samples ess
for all parameters and a value of r_hat
that is very close to one.
[14]:
varnames = ["pri_amp", "pri_y", "sec_amp", "sec_y"]
display(pm.summary(trace, var_names=varnames).head())
display(pm.summary(trace, var_names=varnames).tail())
| mean | sd | hpd_3% | hpd_97% | mcse_mean | mcse_sd | ess_mean | ess_sd | ess_bulk | ess_tail | r_hat | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| pri_amp | 0.996 | 0.016 | 0.966 | 1.025 | 0.001 | 0.001 | 219.0 | 217.0 | 219.0 | 109.0 | 1.02 |
| pri_y[0] | -0.019 | 0.034 | -0.086 | 0.042 | 0.002 | 0.001 | 296.0 | 296.0 | 321.0 | 131.0 | 1.01 |
| pri_y[1] | -0.112 | 0.003 | -0.118 | -0.106 | 0.000 | 0.000 | 468.0 | 468.0 | 480.0 | 265.0 | 1.01 |
| pri_y[2] | 0.065 | 0.003 | 0.060 | 0.071 | 0.000 | 0.000 | 875.0 | 875.0 | 898.0 | 401.0 | 1.01 |
| pri_y[3] | 0.003 | 0.024 | -0.042 | 0.047 | 0.001 | 0.001 | 882.0 | 683.0 | 895.0 | 449.0 | 1.00 |
| mean | sd | hpd_3% | hpd_97% | mcse_mean | mcse_sd | ess_mean | ess_sd | ess_bulk | ess_tail | r_hat | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| sec_y[30] | 0.009 | 0.023 | -0.032 | 0.051 | 0.001 | 0.000 | 2008.0 | 1265.0 | 2007.0 | 1820.0 | 1.00 |
| sec_y[31] | 0.033 | 0.051 | -0.072 | 0.117 | 0.002 | 0.001 | 634.0 | 634.0 | 640.0 | 513.0 | 1.01 |
| sec_y[32] | -0.011 | 0.041 | -0.095 | 0.061 | 0.001 | 0.001 | 1921.0 | 1160.0 | 1905.0 | 1408.0 | 1.00 |
| sec_y[33] | 0.045 | 0.049 | -0.041 | 0.140 | 0.001 | 0.001 | 2272.0 | 1501.0 | 2283.0 | 1647.0 | 1.00 |
| sec_y[34] | -0.047 | 0.063 | -0.170 | 0.061 | 0.002 | 0.001 | 1741.0 | 1405.0 | 1735.0 | 1090.0 | 1.00 |
The number of effective samples for some of the parameters is quite small, so in practice we should run this chain for longer (an exercise for the reader!) But let’s carry on for now, keeping in mind that our posteriors will be quite noisy.
Let’s plot the model for 24 random samples from the chain. Note that the lines are so close together that they’re indistinguishable!
[15]:
plt.figure(figsize=(12, 5))
plt.plot(t, flux, "k.", alpha=0.3, ms=2, label="data")
label = "samples"
for i in np.random.choice(range(len(trace["flux_model"])), 24):
plt.plot(t, trace["flux_model"][i], "C0-", alpha=0.3, label=label)
label = None
plt.legend(fontsize=10, numpoints=5)
plt.xlabel("time [days]", fontsize=24)
plt.ylabel("relative flux", fontsize=24);
Let’s compare the mean map and a random sample to the true map for each star:
[16]:
# Random sample
np.random.seed(0)
i = np.random.randint(len(trace["pri_y"]))
map = starry.Map(ydeg=A["ydeg"])
map[1:, :] = np.mean(trace["pri_y"], axis=0)
map.amp = np.mean(trace["pri_amp"])
pri_mu = map.render(projection="rect").eval()
map[1:, :] = trace["pri_y"][i]
map.amp = trace["pri_amp"][i]
pri_draw = map.render(projection="rect").eval()
map[1:, :] = A["y"]
map.amp = A["amp"]
pri_true = map.render(projection="rect").eval()
map = starry.Map(ydeg=B["ydeg"])
map[1:, :] = np.mean(trace["sec_y"], axis=0)
map.amp = np.mean(trace["sec_amp"])
sec_mu = map.render(projection="rect").eval()
map[1:, :] = trace["sec_y"][i]
map.amp = trace["sec_amp"][i]
sec_draw = map.render(projection="rect").eval()
map[1:, :] = B["y"]
map.amp = B["amp"]
sec_true = map.render(projection="rect").eval()
fig, ax = plt.subplots(3, 2, figsize=(8, 7))
ax[0, 0].imshow(
pri_true,
origin="lower",
extent=(-180, 180, -90, 90),
cmap="plasma",
vmin=0,
vmax=0.4,
)
ax[1, 0].imshow(
pri_mu,
origin="lower",
extent=(-180, 180, -90, 90),
cmap="plasma",
vmin=0,
vmax=0.4,
)
ax[2, 0].imshow(
pri_draw,
origin="lower",
extent=(-180, 180, -90, 90),
cmap="plasma",
vmin=0,
vmax=0.4,
)
ax[0, 1].imshow(
sec_true,
origin="lower",
extent=(-180, 180, -90, 90),
cmap="plasma",
vmin=0,
vmax=0.04,
)
ax[1, 1].imshow(
sec_mu,
origin="lower",
extent=(-180, 180, -90, 90),
cmap="plasma",
vmin=0,
vmax=0.04,
)
ax[2, 1].imshow(
sec_draw,
origin="lower",
extent=(-180, 180, -90, 90),
cmap="plasma",
vmin=0,
vmax=0.04,
)
ax[0, 0].set_title("primary")
ax[0, 1].set_title("secondary")
ax[0, 0].set_ylabel("true", rotation=0, labelpad=20)
ax[1, 0].set_ylabel("mean", rotation=0, labelpad=20)
ax[2, 0].set_ylabel("draw", rotation=0, labelpad=20);
Looks pretty good! There are obvious artifacts (there are tons of degeneracies in this problem), but we’ve definitely recovered the spots, with some uncertainty. Recall that our chains weren’t well converged! Run this notebook for longer to get more faithful posteriors.
Finally, here’s a corner plot for the first several coefficients of the primary map. You can see that all the posteriors are nice and Gaussian, with some fairly strong correlations (the degeneracies I mentioned above):
[17]:
fig, ax = plt.subplots(9, 9, figsize=(7, 7))
labels = [r"$\alpha$"] + [
r"$Y_{%d,%d}$" % (l, m)
for l in range(1, pri.map.ydeg + 1)
for m in range(-l, l + 1)
]
samps = np.hstack((trace["pri_amp"].reshape(-1, 1), trace["pri_y"][:, :8]))
corner(samps, fig=fig, labels=labels)
for axis in ax.flatten():
axis.xaxis.set_tick_params(labelsize=6)
axis.yaxis.set_tick_params(labelsize=6)
axis.xaxis.label.set_size(12)
axis.yaxis.label.set_size(12)
axis.xaxis.set_label_coords(0.5, -0.6)
axis.yaxis.set_label_coords(-0.6, 0.5)
That’s it! While sampling with pymc3
is fairly fast, the problem of inferring a surface map when all other parameters are known is a linear problem, which means it actually has an analytic solution! In the following notebook, we show how to take advantage of this within starry
to do extremely fast inference.