Type Aliases
The following type aliases are available globally.
-
Code block to execute upon completion of fetching a number messages for a conversation.
Declaration
Parameters
error
Error that is optionally set if an error condition occurred during the operation.
messages
List of fetched CPMessage objects.
-
Code block to execute upon completion of fetching a single message for a conversation.
Declaration
Parameters
error
Error that is optionally set if an error condition occurred during the operation.
message
The fetched CPMessage object.
-
Code block to execute upon completion of sending a message for a conversation.
Declaration
Swift
public typealias SendCompletionBlock = (_ error: CPError?, _ outboundMessage: CPOutboundMessage?) -> ()
Parameters
error
Error that is optionally set if an error condition occurred during the operation.
outboundMessage
The sent CPOutboundMessage object.
-
Code block to execute after fetching a list of all conversations.
Declaration
Swift
public typealias FetchBlock = (_ error: CPError?, _ fetchResult: FetchResult?) -> ()
Parameters
error
If operation fails returns as CPError, otherwise returns nil
fetchResult
The result of fetch in type of fetched objects(Can be conversations or messages).
-
Code block to execute after indicating composition state.
Declaration
Swift
public typealias ComposingCompletionBlock = (_ error: CPError?, _ isComposingMessage: CPIsComposingMessage?) -> ()
Parameters
error
If operation fails returns as CPError, otherwise returns nil
isComposingMessage
Information about the generated isComposing notification that will be sent to the participant.
-
Code block to execute after creating a chat group.
Declaration
Swift
public typealias CreateChatGroupBlock = (_ error: CPError?, _ group: CPChatGroup?) -> ()
Parameters
error
If operation fails returns as CPError, otherwise returns nil
group
The newly created chat group.
-
Code block to execute after fetching all chat groups.
Declaration
Swift
public typealias FetchAllChatGroupsBlock = (_ error: CPError?, _ group: [CPChatGroup]?) -> ()
Parameters
error
If operation fails returns as CPError, otherwise returns nil
group
An array of chat groups.
-
Code block to execute after fetching a chat group.
Declaration
Swift
public typealias FetchChatGroupBlock = (_ error: CPError?, _ group: CPChatGroup?) -> ()
Parameters
error
If operation fails returns as CPError, otherwise returns nil
group
The requested chat groups.
-
Code block to execute after uploading a file.
Declaration
Swift
public typealias UploadCompletionBlock = (_ error: CPError?, _ filePart: CPFilePart?) -> ()
Parameters
error
If operation fails returns as CPError, otherwise returns nil
filePart
Object representing upload URL of the file.
-
Code block to execute after downloading a file.
Declaration
Swift
public typealias DownloadCompletionBlock = (_ error: CPError?, _ file: URL?) -> ()
Parameters
error
If operation fails returns as CPError, otherwise returns nil
file
URL of the downloaded file.
-
Code block to execute after creating a PresenceSource.
Declaration
Swift
public typealias CreatePresenceSourceCompletionBlock = (_ error: CPError?, _ presenceSource: CPPresenceSource?) -> ()
Parameters
error
If operation fails returns as CPError, otherwise returns nil
presenceSource
Newly created PresenceSource object.
-
Code block to execute after fetching a PresenceSource.
Declaration
Swift
public typealias FetchPresenceSourceCompletionBlock = (_ error: CPError?, _ presenceSource: CPPresenceSource?) -> ()
Parameters
error
If operation fails returns as CPError, otherwise returns nil
presenceSource
Fetched PresenceSource object.
-
Code block to execute after fetching a PresenceSource list.
Declaration
Swift
public typealias FetchPresenceSourceListCompletionBlock = (_ error: CPError?, _ presenceSourceList: [CPPresenceSource]?) -> ()
Parameters
error
If operation fails returns as CPError, otherwise returns nil
presenceSource
Fetched array of PresenceSource objects.
-
Code block to execute after creating PresentityList.
Declaration
Swift
public typealias CreatePresentityListCompletionBlock = (_ error: CPError?, _ presenceSource: CPPresentityList?) -> ()
Parameters
error
If operation fails returns as CPError, otherwise returns nil
presenceSource
Newly created PresentityList object.
-
Code block to execute after fetching all PresentityList objects.
Declaration
Swift
public typealias FetchPresentityListsCompletionBlock = (_ error: CPError?, _ presentityLists: [CPPresentityList]?) -> ()
Parameters
error
If operation fails returns as CPError, otherwise returns nil
presenceSource
Fetched array of PresentityList objects.
-
Code block to execute after fetching a PresentityList.
Declaration
Swift
public typealias FetchPresentityListCompletionBlock = (_ error: CPError?, _ presentityList: CPPresentityList?) -> ()
Parameters
error
If operation fails returns as CPError, otherwise returns nil
presenceSource
Fetched PresentityList object.


A search is a means of accessing information quickly. If an amount of data is too large for users to find something just by scanning through it, you should consider providing a search function.
Use a search field if you want to enable users to enter text to search for information. The search field is also the control of choice for filtering down a given amount of information.


Tourism Database
Home
Customer
Bookings
Assignments
Tour Guides
Locations
Ratings
Payment
Manage Tour Guides
First Name
John
Jane
Mary
Last Name
Doe
Brown
Search
Add New Tour Guide
Tour Guide ID#
First Name
Last Name
# Assignments
# Locations
1
Abe
Epstein
14
5
Edit
2
Ross
Gellar
NULL
NULL
Edit


This page contains and example of how to add a button to your webpage which will automatically fetch the latest thebe version, load it and bootstrap the library. This is standalone code, please view the page source to see the example.
Once Thebe is active this cell will turn into the live cell:
%matplotlib inline import numpy as np import matplotlib.pyplot as plt x = np.linspace(0,10) plt.plot(x, np.sin(x)) plt.plot(x, np.cos(x))
Note: this page also contains the built in status widget - see demo-status-widget.html for more info.


New machine learning algorithms offer safety and fairness guarantees
New framework for fairer, safer algorithms
| Source: | University of Massachusetts Amherst |
| Summary: | Scientists have introduced a new framework for designing machine learning algorithms that make it easier for users of the algorithm to specify safety and fairness constraints. |
Seventy years ago, science fiction writer Isaac Asimov imagined a world where robots would serve humans in countless ways, and he equipped them with built-in safeguards ¬now known as Asimov's Three Laws of Robotics, to prevent them, among other goals, from ever harming a person.
Guaranteeing safe and fair machine behavior is still an issue today, says machine learning researcher and lead author Philip Thomas at the University of Massachusetts Amherst. "When someone applies a machine learning algorithm, it's hard to control its behavior," he points out. This risks undesirable outcomes from algorithms that direct everything from self-driving vehicles to insulin pumps to criminal sentencing, say he and co-authors.
Writing in Science, Thomas and his colleagues Yuriy Brun, Andrew Barto and graduate student Stephen Giguere at UMass Amherst, Bruno Castro da Silva at the Federal University of Rio Grande del Sol, Brazil, and Emma Brunskill at Stanford University this week introduce a new framework for designing machine learning algorithms that make it easier for users of the algorithm to specify safety and fairness constraints.
"We call algorithms created with our new framework 'Seldonian' after Asimov's character Hari Seldon," Thomas explains. "If I use a Seldonian algorithm for diabetes treatment, I can specify that undesirable behavior means dangerously low blood sugar, or hypoglycemia. I can say to the machine, 'while you're trying to improve the controller in the insulin pump, don't make changes that would increase the frequency of hypoglycemia.' Most algorithms don't give you a way to put this type of constraint on behavior; it wasn't included in early designs."
"But making it easier to ensure fairness and avoid harm is becoming increasingly important as machine learning algorithms impact our lives more and more," he says.
However, "a recent paper listed 21 different definitions of fairness in machine learning. It's important that we allow the user to select the definition that is appropriate for their intended application," he adds. "The interface that comes with a Seldonian algorithm allows the user to do just this: to define what 'undesirable behavior' means for their application."
In Asimov's Foundation series, Seldon is in the same universe as his Robot series. Thomas explains, "Everything has fallen apart, the galactic empire is collapsing, partly because the Three Laws of Robotics require certainty. With that level of safety required, robots are paralyzed with indecision because they cannot act with certainty and guarantee that no human will be harmed by their actions."
Seldon proposes fixing this by turning to reasoning probabilistically about safety. "That's a good fit to what we're doing, Thomas says. The new approach he and colleagues provide allows for probabilistic constraints and requires the algorithm to specify ways the user can tell it what to constrain. He says, "The framework is a tool for the machine learning researcher. It guides them toward creating algorithms that are easier for users to apply responsibly to real-world problems."
To test the new framework, they applied it to predict grade point averages in a data set of 43,000 students in Brazil by creating a Seldonian algorithm with constraints. It successfully avoided several types of undesirable gender bias. In another test, they show how an algorithm could improve the controller in an insulin pump while guaranteeing that it would not increase the frequency of hypoglycemia.
Thomas says, "We believe there's massive room for improvement in this area. Even with our algorithms made of simple components, we obtained impressive results. We hope that machine learning researchers will go on to develop new and more sophisticated algorithms using our framework, which can be used responsibly for applications where machine learning used to be considered too risky. It's a call to other researchers to conduct research in this space."
make a difference: sponsored opportunity
Story Source:
Materials provided by University of Massachusetts Amherst.
Note: Content may be edited for style and length.
Journal Reference:
- Philip S. Thomas, Bruno Castro Da Silva, Andrew G. Barto, Stephen Giguere, Yuriy Brun, Emma Brunskill. Preventing undesirable behavior of intelligent machines. Science, 2019 DOI: 10.1126/science.aag3311
Cite This Page:
| MLA | University of Massachusetts Amherst. "New machine learning algorithms offer safety and fairness guarantees: New framework for fairer, safer algorithms." ScienceDaily. ScienceDaily, 21 November 2019. <www.sciencedaily.com/releases/2019/11/191121141325.htm>. |
| APA | University of Massachusetts Amherst. (2019, November 21). New machine learning algorithms offer safety and fairness guarantees: New framework for fairer, safer algorithms. ScienceDaily. Retrieved November 21, 2019 from www.sciencedaily.com/releases/2019/11/191121141325.htm |
| Chicago | University of Massachusetts Amherst. "New machine learning algorithms offer safety and fairness guarantees: New framework for fairer, safer algorithms." ScienceDaily. www.sciencedaily.com/releases/2019/11/191121141325.htm (accessed November 21, 2019). |


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Instructions
Conjunction (And): ^ (caret) or ∧
Disjunction (Or): v (lower v) or ∨
Negation (Not): ! (bang) or ¬
Implication: -> or →
Equivalence: <-> or ↔
True: 1
False: 0
You can use ( parenthesis ) to group parts of a formula.
Example: !A v B <-> (A -> B)
Warning
Analyzing large formulae can take some time and may freeze your browser or at least this tab.
The Simplified CNF and Simplified DNF are not always the smallest possible formulae but the Simplified Formula should be.
| Normal Form | Formula |
|---|---|
| CNF | |
| Simplified CNF | |
| DNF | |
| Simplified DNF | |
| NNF | |
| Simplified Formula | |


D’Tigress to face Mali after Mozambique win
by The Eagle OnlineD’Tigress took a bold step to picking a ticket to the Olympics Qualifying Tournament in 2020 after beating host Mozambique 57-48 points to go top of Group A.
The reigning African champions dominated the first half 21-8, 17-11 to win 38-19 points and will face Mali in the semifinals of the FIBA Africa pre-Olympics qualifiers.
Mozambique took the second half winning the third and fourth quarters by slim margins, 16-8, 13-11, but it was a case of too little too late as Nigeria stood their ground for a win.
The top performer for Nigeria was captain, Adaora Elonu who had 12 points, one assist and nine rebounds.
Ezinne Kalu and Evelyn Akhator scored 12 points each as Akhator also emerged the highest rebounder for the team with a total of 12.
Head Coach, Otis Hughley, said the game against Mozambique gave the team the opportunity to play together as a team since their Afrobasket win.
He said: “We are focused on the bigger picture of picking the olympics ticket which is the ultimate target for us as a team.”
Hughley promised that the game against Mali in the semifinal will be different as they hope to take the pre-qualifiers one game at a time with the 2020 Olympics in Tokyo the ultimate target.
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System Architect Definitions, Encyclopedia:"D:\DATA\SYSARCH\HCM\GEN_DATA\SUBJECT\V01\CURRENT\"
associated phenomenon (Association)
- From Class:
"Subject Phenomenon"
- To Class:
"Phenomenon Under Heading"
Associative Category (Class)
- Abstract:
F
- Derived:
F
- Allow Shared:
F
- Override Shared:
F
- Default Shared:
F
- Allow Transactional:
F
- Override Transactional:
F
- Default Transactional:
F
- Allow Monitored:
F
- Override Monitored:
F
- Default Monitored:
F
- Allow Distributed:
F
- Override Distributed:
F
- Default Distributed:
F
- Normalize:
F
- Description:
"Category" capable of classifying associations between "Objects".
- Source:
CCC
- State Type:
F
- &Abstract:
F
- Value:
F
Associative Subject Relation (Class)
- Description:
Peer to peer "Inter Subject Relationship".
- Source:
post-CBS
- State Type:
F
- &Abstract:
F
- Value:
F


I used to be really, really wary about the world of research chemicals. I've always been hesitant to order them, and for a long time was simply on a slightly hypocritical 'pure drug' trip. I say hypocritical because while I've always been heavily into the classic tryptamines - DMT, psilocybin/psilocin/baeocystin, and LSD, I really have enjoyed MDMA and MDA. This year I've been learning a lot more about Alexander Shulgin, and gained a deeper respect for him while attending the Psychedelic Science in the 21st Century conference and decided it was time to broaden my horizons and enjoy some of his work. This began back in April when I tried 2c-b for the first time; I've since tried 2c-b a number of times and really enjoyed it a great deal.
Frankly, though, I've always been more of a tryptamine man than a phenethylamine fan. So I've been very curious to try some of Shulgin's tryptamine work, especially 4-AcO-DMT which I plan on getting around to soon. Friday night, though, I dipped into the 4-HO-MiPT pool and let me tell you, I think 4-HO-MiPT is an experience well worth undertaking.
I met up with some friends to go see the New York Philarmonic play a free concert in Prospect Park. It was a hot, humid night and we spread out a blanket under the boughs of a tree. We drank a little bit of wine - I had about a half a glass of white - and while my friends ate some cheese, bread and bosc pears, I ate 20mg of 4-HO-MiPT. I wasn't feeling much at all about fifteen to twenty minutes later and was handed another 20mg capsule of the tryptamine - this one I dumped between my knuckles and insufflated. Within the next 5-10 minutes the onset of the drug began to hit.
At first a restlessness built, and a feeling of general bodily discomfort. I couldn't find a comfortable position to lay in, found myself suddenly concerned over whether or not I was getting dirty laying in the grass beneath this tree. Constantly shifting, I tried following the two separate conversations my two pairs of friends were having but was increasingly lost in my thoughts. I stood up, walking slowly away from the blanket and feeling a bit disoriented. I felt mildly drunk, but knew it was definitely the tryptamine and not the bit of wine I'd had.
I sat down again, and a feeling of agitation was swelling up in me. At the same time, the sky looked more filled with stars than it had minutes before. Not stars, I realized, but small flickering points of light that were definitely visual perturbations brought on by the 4-HO-MiPT and not the sky as it would normally be perceived. I turned to my friends, feeling the need to narrate my experience. I told them I was definitely high and that I felt really good but that I felt really agitated. One of them laughed and pointed out that I'd just said I felt agitated in a completely calm voice. That was true - I was feeling an agitation that was mainly a body load but really took a backseat to an overwhelming feeling of euphoria. Another friend offered me a pear, and I took it more just to hold than to eat.
While I held the pear, the drug really hit. There's one word that describes each and every sensory aspect of the experience: LUSH. Visuals, auditory input, the feeling of my skin touching itself as I touched my body, the grass under my feet, the general feeling of being alive was lush. Juicy. Incredible fractal geometry stitched through and through with rainbow lines that coursed with purple/magenta/green energy were spread out throughout the sky. Within minutes I was having some of the most salient, rich visuals I had ever had outside of smoking n,n-DMT. They completely surpassed any visuals I had ever had with eyes open on either mushrooms or LSD. The park folded and swelled, the ground unfolding before my eyes to reveal itself in endless layers.
I took a bite of the pear, and felt immediately nauseous. I handed the pear back, announcing that I no longer wished to hold it, and swallowed the bite I had taken with great difficulty, feeling incredibly nauseous. I thought about vomiting, but realized I just really didn't feel like it. Others in the group went to the porta-potties far away and I lay with my friend Birgit describing the experience to her.
She asked how this tryptamine differed from mushrooms which was a totally relevant question and I loved that she asked it. It seemed nearly impossible, on this 4-HO-MiPT, to enter those dark corners of the psyche that mushrooms often took me to. Now, that is actually a quality of mushrooms that I enjoy, but it's also something that keeps me from eating mushrooms too often and approaching them with a healthy respect. 4-HO-MiPT, it seemed obvious to me, was just a lot more fun and something I could do without as heavy a heart as mushrooms. I found myself wishing I could express my creativity in different forms, especially paint. I wanted to cover my hands in paint and smear them over a large white canvas.
Our other friends came back and one of them asked me, referring to a quote from Shulgin's entry on 4-HO-MiPT in TiHKaL, if I was experiencing the 'sexual energy of a large jungle cat.' In the state of mind I was in, that seemed completely reasonable. The experience, again, was so LUSH and the visuals on the trees were so rich and juicy and jungle-like, that the mere suggestion of a jungle cat brought to mind images of myself running across an open field and diving into the grass. In general I found that I could think of many, many things I wanted to do but knew that if I were to actually begin doing any of them (playing with the hula hoops a friend had brought, running across a field, dancing) I would immediately regret having expended the energy and want to sit down again.
The rest of the group wanted to get some wine, and the shops were soon to close, so we stood to go. Standing was like entering a totally new world. Frankly, I was tripping really hard. Harder than I had expected to. Everything shifted as I moved, the ground not sitting still for a moment. I approached our tree, setting foot on it, feeling myself melt into it briefly before stepping away. We walked out of the park, and I was really worried I didn't look sober at all because I definitely didn't feel sober at all. One of my friends had taken some 4-HO-MiPT as well, the same amount as me, and was barely feeling anything except high. I, meanwhile, was having one of the most enjoyably visual experiences of my life.
The wine shops were closed; I got into the front car of a cab and my friends piled in the back. Strapped to the passenger seat, I felt like I was inside a transdimensional intergalactic mexican flea spaceship. High pitched voices speaking Spanish buzzed from the cab's radio while music played from the radio, and the Brooklyn cityscape zoomed by. Our driver ran two red lights, and was very unsafe in most of his turns. I was holding on for dear life, watching the street lights and roads ooze in a way that was verging on sexual. When we finally got back to my apartment, I was relieved.
There, the others drank beer and wine, and eventually broke out some MDMA which they insufflated. I was feeling down from my tryptamine high after about 4 hours, and snorted 20mg more 4-HO-MiPT. This was so incredibly pleasant. For the next 3-4 hours, I didn't trip nearly as hard as I did off of the 40mg dose, but had incredibly beautiful visuals that suffused everything around me with a rainbow glow. It seemed that wisps of language were floating in the air all throughout my apartment, and when I waved my hands through the air I could distort the visuals I was having, even paint with them. The same magenta-purple-green energy coursed from various nodes suspended in reality, concentrated around my friends. I resisted the urge to partake in the MDMA as well, having overdone it on that substance the past 3 weekends. I got a lot of vicarious enjoyment out of watching my friends roll.
The comedown off both the 40mg and the later 20mg dose of the 4-HO-MiPT were incredibly manageable, I felt fantastic in fact. This is a substance I look forward to exploring more in the future, and at even higher doses. And if not at higher doses, then definitely continually insufflating it throughout a long night as a friend of mine has done. I haven't really conveyed it in this report but the cerebral quality of the drug was perhaps the most enjoyable, beyond the erotic, lush, sensual energy that coursed through me and the glorious visuals. I felt I was really able to work through complicated thought processes and have really elaborate, expansive conversation that the tryptamine consciousness lent itself to.
4-HO-MiPT. Thumbs Up.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


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The Power of Trusted Relationships
Throughout the history people have used word-of-mouth as the easiest way to make decisions. Jobs big or small, tasks, errands, gigs or personal services – they all require someone who is the right fit, and whom you can trust, especially if you need to let them into your personal space.
In today’s society, everyone - you, me, our favorite hairdresser or accountant - are fighting for attention, trying to break through the noise and clutter of the crowded and brutally competitive online space. Getting noticed in the noise is incredibly hard, if not impossible.
Everyone is having it tough.
At ZippyCrowd, we believe there is a better way. Our easy to use, online recommendation tool and platform helps you find people you need, and get recommended by people you trust.
Here are a few of the many things that you can expect:
ZippyCrowd is a game changer, a leap forward. Far easier to sift through and get to the right person than social sites with their collections of selfies, likes and “eyeballs”. Far more personal than various marketplaces that view people as prospects, bids, and one off faceless transactions.
We founded ZippyCrowd to unlock the power of your trusted relationships .
We designed ZippyCrowd to be friendly, smart and helpful. To cut through the noise, eliminate the risk, give you an edge, and make your life a little less stressful.
We are cracking the code of personal recommendations, and adding more features as we go, to help you focus on the people that matter.
We are a team of passionate thinkers, askers, doers and recommenders. At our core, we believe:
We’re just getting started, and we want to invite you to join us on a change-making journey.
Your Zippy friends, Allie, Andriy, Yulia and Mario


--- title: '"Nested" random factors in mixed (multilevel or hierarchical) models' author: Jeff Walker date: '2019-11-30' slug: nested-random-factors-in-mixed-multilevel-or-hierarchical-models categories: - stats 101 tags: - generalized linear models - linear mixed model keywords: - tech output: blogdown::html_page: toc: true ---
This is a skeletal post to show the equivalency of different ways of thinking about “nested” factors in a mixed model. The data are measures of life history traits in lice that infect salmon. The treatment is the source of lice – from farmed raised or wild salmon. The response variable analyzed here is the date of production of the egg string in units of “days post-infection”. A second fixed-factor is the reproductive event number (the lice went through five successive rounds of reproduction following infection). The salmon were raised in 30 tanks in each of two rooms. There were multiple measures for each tank, so there is non-independence within tanks. There were also differences in setup between rooms so we expect non-independence within rooms. There are two ways to think about this non-independence
Source data Data from: Invest more and die faster: the life history of a parasite on intensive farms
library(janitor)
library(data.table)
library(lmerTest)
library(here)
here <- here::here
folder <- "Data from Invest more and die faster - the life history of a parasite on intensive farms"
fn <- "timing_ES1to5.txt"
file_path <- here('data',folder, fn)
file_path <- paste('../data', folder, fn, sep="/")
timing <- clean_names(fread(file_path))
setnames(timing, old="exp", new="room")
I follow the authors of the source paper and use a generalized linear mixed model with Poisson family and log-link. The column “tank” contains the tank ID 1-30 in each room. As a consequence a tank with id “6” is not unique but occurs in both room 1 and room 2. The column “tank2” has a unique name for every tank. The value is simply the combination of “room” and “tank”.
m2 <- glmer(days_pi ~ status*es + (1|room/tank), family = "poisson", data = timing)
coef(summary(m2))
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.981916916 0.072950431 54.583871 0.00000000
## statuswild -0.037870115 0.018418902 -2.056046 0.03977806
## es 0.135835163 0.003406919 39.870382 0.00000000
## statuswild:es 0.006370614 0.005207202 1.223424 0.22116972
m3 <- glmer(days_pi ~ status*es + (1|room) + (1|room:tank), family = "poisson", data = timing)
coef(summary(m3))
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.981916916 0.072950431 54.583871 0.00000000
## statuswild -0.037870115 0.018418902 -2.056046 0.03977806
## es 0.135835163 0.003406919 39.870382 0.00000000
## statuswild:es 0.006370614 0.005207202 1.223424 0.22116972
The room:tank
specification may create combinations that do not exist in the data. A safer way to do this is to create a column that combines “room” and “tank” so that each tank has a unique name. This is the same concept as “tank2” in the original data.
# to emphasize that this only works if room and tank are factors
timing[, room:=factor(room)]
timing[, tank:=factor(tank)]
timing[, tank_id := factor(room:tank)] # same concept as "tank2"
m4 <- glmer(days_pi ~ status*es + (1|room) + (1|tank_id), family = "poisson", data = timing)
coef(summary(m4))
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.981916916 0.072950431 54.583871 0.00000000
## statuswild -0.037870115 0.018418902 -2.056046 0.03977806
## es 0.135835163 0.003406919 39.870382 0.00000000
## statuswild:es 0.006370614 0.005207202 1.223424 0.22116972
“tank2” is the author’s version of the combination, created using something like paste0(room,tank)
. This is effectively like the “safe” way to create the combinations above.
m1 <- glmer(days_pi ~ status*es + (1|room) + (1|tank2), family = "poisson", data = timing)
coef(summary(m1))
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.981916916 0.072950431 54.583871 0.00000000
## statuswild -0.037870115 0.018418902 -2.056046 0.03977806
## es 0.135835163 0.003406919 39.870382 0.00000000
## statuswild:es 0.006370614 0.005207202 1.223424 0.22116972
This doesn’t work because the column “tank” contains the same set of tank IDs in each room, that is, tank ID is not unique. This implies that tank “2” is the same in both rooms or that “2” has the “same” meaning.
m5 <- glmer(days_pi ~ status*es + (1|room) + (1|tank), family = "poisson", data = timing)
coef(summary(m5))
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.981048359 0.073559994 54.119748 0.00000000
## statuswild -0.035657150 0.018364165 -1.941670 0.05217705
## es 0.135696317 0.003407198 39.826363 0.00000000
## statuswild:es 0.006427319 0.005201244 1.235727 0.21655993


|
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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||
| SUMMARY: INNER | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
This is the interface that should be implemented by all attack classes. It contains the basic methods called by the battle system. How these methods are implemented doesn't matter as long as they do what they're supposed to.
| Method Summary | |
int |
Draw()
Returns amount of MP needed to attack |
int |
Exe(DGCharacter attacker,
DGCharacter defender)
Executes the attack. |
boolean |
IsMagic()
Whether this is a magic or physical based attack. |
boolean |
IsTeam()
Returns whether or not attack attacks a whole team or just one person |
java.lang.String |
Name()
Gets the name of the attack |
int |
Pow()
Returns relative power of the attack |
java.lang.String |
Status()
Returns a string representation (the name) of any DGStatus
possibly inflicted by this attack. |
int |
Type()
Returns the type of the attack as given in the DGTypeHandle class. |
| Method Detail |
public int Draw()
public int Exe(DGCharacter attacker, DGCharacter defender)
attacker
- DGCharacter
making the attackdefender
- DGCharacter
defending against the attackpublic boolean IsMagic()
public boolean IsTeam()
public java.lang.String Name()
public int Pow()
public java.lang.String Status()
DGStatus
possibly inflicted by this attack. Multiple statuses should be
seaperated by carrage returns ('\n'). "None" should be returned if there
are none.public int Type()
DGTypeHandle
class.
|
||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||
| SUMMARY: INNER | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


Oh Goodness
Cheesecake & Delivery. Sweet and simple.
Sweet! (pun intended)
Tasting. Party Planning. Decoration & Cake Setup & Cleanup
Kid In a Cake Store
Tasting. Party Planning. Rentals. Decor. Cake Setup & Cleanup
425 El Camino Real, Menlo Park, CA 94025 /<PHONE_NUMBER>
M - F : 10am - 6pm / Weekend : Pick up by appointment
+1<PHONE_NUMBER> /<EMAIL_ADDRESS>Please pre-order 48 business hours in advance.
Cakes and slices available for walk-ins. *Limited quantity.
A layer of graham cracker crust, a cream cheese layer, topped with a layer of sour cream and sugar. Each layer is specially created according to the original recipe over a secret two day process. The result? The most memorable cheesecake experience you have ever had.
6 inches:
$25
Gluten Free - $30
9 inches:
$35
Gluten Free - $42
6 inches
$28
Gluten Free - $33
9 inches
$40
Gluten Free - $47
6 inches
$30
Gluten Free - $35
9 inches
$45
Gluten Free - $52
Includes cutting, tray, and assortment of fresh fruit and candy toppings
1" x 1" piece / 60 bites (2 - 3 bites for a person)
1 square cake - $75
1 square cake gluten Free - $85
Serving as many people as your heart desires!
We take the work out of fun. If you simply need a little more sweetness in life, we've got you covered.
This Cheesecake is magic.
Wedding, birthday, baby shower, engagement, hallowing party, and more.
Order Cheesecake for your Event!
Email<EMAIL_ADDRESS>Cheesecake & Delivery. Sweet and simple.
Tasting. Party Planning. Decoration & Cake Setup & Cleanup
Tasting. Party Planning. Rentals. Decor. Cake Setup & Cleanup
On El Camino Real between College Ave and Middle Ave. Next to Shell Station.
Phone
+1<PHONE_NUMBER>
Email
<EMAIL_ADDRESS>Hours
Monday to Friday: 10am - 6pm
Weekend: Pick up by appointment
Cherith Spicer and Mrs. Cherith Lorraine Rickey at her birth day dinner. 2001
Cherith Spicer when she inherited the cheesecake recipe in 2004 and and Mrs. Cherith Lorraine Rickey.
Hi! My name is Cherith Spicer and I started Namesake Cheesecake to honor my dear family friend, Cherith Lorraine, for whom I am named.
Cherith Lorraine was a wife, mother, grandmother, friend and hostess. Along with her husband, John Rickey, she co-owned several Bay Area restaurants, including Rick’s Swiss Chalet, Dinah’s Shack and Rickey’s Hyatt House in Palo Alto. Over the years she perfected a cheesecake recipe that was shared by friends, family and patrons of her restaurants.
I was one of the lucky friends who enjoyed the cheesecake on many occasions. Her cheesecake was the highlight of holiday parties and family get togethers. The recipe was kept secret, even from her family.
The only person she shared the recipe with was me. She gave me the recipe when I promised that I would create a company with that recipe. I started Namesake Cheesecake to continue the tradition of excellent cheesecake for good times together, whether it’s a party at home, a dinner out, or a catered event.
Do you have cakes/slices ready for purchase at the store without placing an order?
We will always do our best to have available products for walk-ins. It is however, a first come first serve bases. We ask for a 48 business hour advance and it is highy suggested. Each layer is specially created according to the original recipe over a secret two day process. The result? The most memorable cheesecake experience you have ever had.
Do you deliver cake?
We deliver depending on quantity of cakes.
Where is the location I can pick up cake?
Our store - 425 EL CAMINO REAL, MENLO PARK, CA 94025
How can I place an order?
Please call +1<PHONE_NUMBER> or email at<EMAIL_ADDRESS>What is hours?
Monday to Friday: 10am - 6pm
Weekend: By appointment only


Jan 19, 2014
Final updates and conclusion for this course
This update is more a summary of the status of Arks - the unattended installation framework for Archlinux
So far the following new features have been implemented and is working in Arks:
- Progrium/keychain.io support
- Email one-time passwords.
- Puppet provisioning.
- Notify your iPhone when installation is complete.
- Notify via email when an error occur.
Besides this Arks has great error handling letting the user besides being notified when it has an error letting the user fix the problem right there in the middle of the installation. You don't have to start over. All nodes and users have their own profiles. That way you can specify all users for one node and all users can have individual settings. To help installation Arks tries to follow Beginners' Guide. Arks installs a node below one minute and the time Arks spend is dependant on how much you cram in your machine.
Upcoming features
- Efi support.
Conclusion
These weeks I have been focusing on Arks and it has been intense. Alongside Arks I have been experimenting with Apache Mesos and Marathon; diving into more advanced scripting techniques for installation. I really enjoy reading peoples code and this have helped me writing better code myself. Its not only about writing code, but all the different ways one simple application can be written just for the purpose of doing one simple thing.


Falcons attempted to troll the Saints on Twitter; Saints came back with a punch
by KNOENEW ORLEANS, La. (WVUE) - After last night’s win, the Falcons tweeted that they’ve scored more overall points during the two games in the following tweet:
The Saint’s replied back with a clip of Shy Tuttle strong-arming Matt Ryan after intercepting his throw.
Copyright 2019 WVUE. All rights reserved.


 That you can Edit?!
"Integrating the awesome CodeMirror into Deck.js:"
This Code is in a Text Area!
You can edit it and run it again! Try it!
That code was created with a text area that had the following attributes:
- class - "code"
- mode - "javascript"
- runnable - "true"
The rest just HAPPENS
Code blocks aren't runnable by default. Because you might want to put crazy non runnable code in there. Like, html.
You can specify a codemirror theme as long as you include the stylesheet for it by setting the theme attribute
This Code is in a div!
// codemirror demo party!
var obj = { text : "Hello all!"};
console.log("HI THERE");
It looks like this:
<div id="code" class="code" name="code" mode="javascript" style="display: none;">
// codemirror demo party!
var obj = { text : "Hello all!"};
console.log("HI THERE");
</div>
HTML Syntax?! Sweet.
Note that it's defined by being put inside a textarea, that way it doesn't get evaluated. Replicate that however you see fit. Also, set the mode="text/html", not just "html".
Two runnable code blocks?! CRAZY!
First
Second
Repo:
https://github.com/iros/deck.js-codemirrorBy:
Irene Roshttp://ireneros.com
@ireneros
/
#


Find out perfect places to hangout in your city.
Based on your location consider these awesome breweries.
Explore some of the best breweries around you.
Not sure where to begin? HopStop automatically suggests breweries based on your location.
HopStop will help you find breweries.
Create an account and log your breweries, add to your favorites and rate them.


Gallery
Portrait of Malala yousafzai
Malala yousafzai recovering in a hospital in the United Kingdom
Malala yousafzai going to school
Khyber Pakhtunkhwa, Pakistan. The region in which these events occured.
The Taliban, the terrorist group responsible for shooting Malala Yousafzai


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You have {{favoritesCount.count}} favorites.
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Home
Portfolio
About Me
This is Alan Coronado's Home Page!!!
This is my home away from home:
Below you can see some of my Assigments:
Here is a little background information about myself:
"This is where I am from"


About The Company
We are a Consulting Service, who supplies the latest of consulting offerings to the market. We focus on current and future trends that we believe have great market share potential.
- Address: 44 Avenue Road, Cork
- Telephone:<PHONE_NUMBER>
- Email<EMAIL_ADDRESS>- Opening Hours 9am-5pm, Mon-Fri
Click here to learn more about what a Consulting Service is
We Provide Expert Advice On The Following:
Blockchain
Blockchain is a system in which a register of activity made in a type of crytocurrency are maintained aross many computers that are connected in a peer-to-peer structure.
"The blockchain is an uncorruptable digital ledger of economic transactons that can be programmed to record not just financial transactions but virtually everything of value." - Alex and Don Tapscott, authors Blockchain Revolution
Autonomous Things
Artificial Intelligence is already changing the world ny developing production in the work environment. AI is changing how we undergo our day-to-day tasks and we can anticipate for the technology to grow in 2019.
- Intel performed a drone swarm for the Winter Olympics Opening Ceremony in 2018.
- Cooperative merge schemes whereby vehicles broadcast to one another enhance traffic flow.
- It is expected for 10% of vehicles to have autonomous driving proficiency by the year 2021.
How Autonomous Things are being used:
Immersive Experience
Nowadays we can build digital surroundings for humans to experience the impossible. Through a blend of artificial intelligence, science and creativity, virtual and augmented reality offers the chance to plot and train without consequences.
- Education
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- Fitness


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Our Packages:
-
Land tour
We offer the trip all around the most popular places on land of Vietnam. Customer can experience traditions and cultures from North to South side.Included meals and hotels .
Pricing: $1,000 - $3,000 per tour
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Sea tour
We offer the trip all around the most popular beaches of Vietnam. Customer can experience ocean views and marine life from North to South side.Included meals and hotels.
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We offer the trip all around the most popular places on high land of Vietnam. Customer can experience, beautiful views traditions and cultures of several ethnic minorities .Included meals and hotels.
Pricing: $2500 per tour


Slide to set value
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Changing the "readonly" HTML attribue or DOM property can let you use it.
Slide or click to toggle the readonly attribute of the above slider
Toggle this to change the readonly attribute of the toggle tool above


Select from one of the word clouds
Woman Director
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International Banks Should Bear Responsibility For State Capture – Hain
by NG EditorJOHANNESBURG – Former British politician Lord Peter Hain on Monday testified at the Zondo Commission that international banks that had granted looters access to global networks to launder money were also to blame for state capture in South Africa.
Hain testified at the commission on the role international firms played in the fraud and corruption that took place in the public sector over the past decade.
He insisted that the corporates he referred to as international actors helped corrupt individuals in South Africa create complex structures to disguise the true ownership of funds.
Hain said global banks, HSBC, Standard Charter and the Bank of Baroda, who were said to have allegedly assisted the notorious Gupta family were culpable for the capture of the state.
“For international banks to claim they have no responsibility is frankly disingenuous and they should be held accountable for the consequences of accounts which have money that is smuggled,” he said. “The banks concerned did not stop it and they are culpable, in my view, to state capture, the corruption and looting.”
He testified that although his plea was for the international community to address what he called a “sorry and sordid saga,” he would also focus on necessary domestic changes.
Hain said the international actors helped corrupt individuals to enjoy the spoils of their illegality by allowing them to move their ill-gotten gains from South Africa.


{% include head.html %}
{% include body-js.html %}
I'd vote for Hillary, but...
{{ content }} {% include footer.html %}


Form Steps
Keyboard
Try the keyboard navigation by clicking arrow left or right!
Effects
Wonderful transition effects.
Pager
The next and previous buttons help you to navigate through your content.
Form steps example
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Keyboard
Try the keyboard navigation by clicking arrow left or right!
Effects
Wonderful transition effects.
Pager
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Ditko
Ditko is an iOS/macOS/tvOS/watchOS framework that extends the AppKit, UIKit, and WatchKit frameworks.
Ditko is an iOS/macOS/tvOS/watchOS framework that extends the AppKit
, UIKit
, and WatchKit
frameworks. It includes macros, functions, categories and classes that accelerate common development tasks. For example, a category on UIColor
and NSColor
to quickly create instances given RBBA or HSBA components.
Installation
pod 'Ditko'
Authors
Kosoku


An Overlord is an advanced Orc Shaman. Possessing the qualities required to be head of a tribe, only they can lead our clan to a higher level.
Description of the Overlord
Change occupation to Overlord
Return


Academic Integrity Resources: Department of Psychology Brooklyn College
Matthew Crump
2018-04-16
Resource Documents
- Faculty Action Report
- CUNY Academic Integrity Policy
- Faculty Obligations for Syllabus Preparation
- Selling Term Papers
- Proctoring Exams
0.1 Contributing to this resource
The source code for compiling this document is in this github repository: https://github.com/CrumpLab/AcademicIntegrity
You can contribute to this resource directly by suggesting edits to the .Rmd files and submitting pull requests. You can also use the “issues” tab to leave comments and have discussions.


Lorem Ipsum is a simply dummy texts of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since.
Learn MoreLorem Ipsum is a simply dummy texts of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since.
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Learn MoreAbout Our Company
Lorem ipsum dolor sit amet, consectetur adipiscing elit sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
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Lorem Ipsum is simply dummy text of the printing and typesetting industry.
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Lorem Ipsum is simply dummy text of the printing and typesetting industry.
Lorem Ipsum is simply dummy text of the printing and typesetting industry.
Lorem Ipsum is simply dummy text of the printing and typesetting industry.
Lorem Ipsum is simply dummy text of the printing and typesetting industry.
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type.
Business / Service / Product / Template
Business / Service / Product / Template
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Business / Service / Product / Template
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Business / Service / Product / Template
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Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type.
On the other hand, we denounce with righteous indignation and dislike men who are so beguiled and demoralized by the charms of pleasure of the moment so blinded as by desire, that they cannot foresee the pain and trouble that are bound to ensues; et and equal blame belongs to those who fail in their duty through weakness.
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Learn MoreLorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type.
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Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type.
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I come from a background in restaurant management ranging from running QSRs myself, training and developing management teams, and up to overseeing up to just over 30 stores. This is where I first cut my teeth with programming, as overseeing so many restaurants and trying to give detailed feedback on numbers is very time-consuming.
I taught myself just enough Visual Basic in Excel to create a macro that could analyze any number of spreadsheets and compile a summary of a store's performance. Armed with that, I was able to discuss issues with my managers and help them lower their operation costs. Using this tool, I was able to drop food cost at some of my problem stores by over 4% (~$50k/yr)! I learned then programming can be a fun, challenging, and effective way to tackle problems.


"x" "1" "VERTEGAAL,LBJ, VOOGD,CE, MOHN,GR AND VAN DER GEN,A, FURTHER STUDIES ONTHE MUTAGENIC ACTIVITY OF FECAPENTAENE-12 ANALOGS AND CONCLUSIONS ABOUT THEIRMOLECULAR MODE OF ACTION, MUTAT. RES. 281(2):93-98, 1 __ 992"


Motivation
Exchange rates are important indicators of real and perceived economic performance. Fluctuations in a country's exchange rate directly reflect changes in economic confidence, a fact that became particularly salient during the recent global financial crisis and Brexit referendum. Furthermore, as foreign currencies are one of the most liquid assets traded in global financial markets, any relevant news or development about a country's economy is very quickly priced into exchange rates, and a belief in the strong efficient markets hypothesis would lead one to believe that exchange rates are not only very difficult to predict, but potentially impossible to predict in practice.
Objectives
In this project, we aim to predict future exchange rates at a weekly level. We apply techniques from time series analysis and natural language processing to identify trends in the exchange rate for prediction. We hypothesise that changes in business news sentiment can be used to predict movements in the exchange rate, leading to more accurate predictors when combined with predictors from time series analysis. Our goal is to build a time series prediction model that incorporates sentiment scores of news articles. We decided to focus on business news as we postulate that news in this segment is most closely related to and affected by exchange rates. Other news segments we considered were politics and world news, however we decided to focus initally on business related news. Through this project we sought to answer a few key questions:
Approach
We began our study by collecting UK/US exchange rate data and business news articles in the period 2010-2016. The subsequent analysis involved two components. The first was a univariate time series predictive model of the exchange rate data. The second was a sentiment analysis algorithm to determine the weekly business sentiment score. The results of this analyis formed the creation of a multivariate time series model incorporating sentiment scores. The predictive accuracies of the models were compared the models were used to generate a one week forecast of the exchange rate.
Daily data on the UK-US exchange rate at market closing was obtained from the Bank of England's Statistical Interactive Database for the January 2000 to October 2016 time frame. We performed a series of exploratory analyses on the data to determine the frequency at which the data is needed in order for important movements in the exchange rate to be captured (such as the financial crisis and brexit). It was determined that weekly frequency was sufficient and the data was aggregated to the weekly level. This data is plotted below:
Time periods of note are the Global Financial Crisis that occurred roughly around 2008, and the Brexit referendum in the summer of 2016.
Business news data was collected from The Guardian, a British newspaper. Weekly frequency was chosen to match the frequency of the financial data. The top 30 news articles each week in the business section in the period 2010-2016 was collected using the Guardian API and saved to a csv file. The csv was saved to AWS S3
The articles were then filtered for relevance by counting the occurence of only words related to the economic health (from a predefined list of words grounded in economic theory)
On average there are at least 10 relevant business articles each week, with the number of relevant articles increasing after 2009.
SentiWordNet Methodology
1. Selection of Relevant Articles
From all the scraped articles, we first filtered based on relevance to economic health. This involved building a set of words and phrases based in economic theory to indicate relevance. The article were searched to test for the occurence of any of the words from the relevant set. An article was considered relevant if the number of terms matched in the relevance set was above a certain threshold. We visualised the average number of relevant articles weekly under different relevance thresholds (where a higher threshold results in a lower number of articles). After considering different values, we determined the optimal threshold to be 3 words. This was decided by ensuring that a sufficient number of articles were classed as relevant weekly in order to generate weekly sentiment scores (such that no week had zero relevant articles), whilst also ensuring an excessive number of irrelevant articles were excluded.
Of the relevant business articles, the most common words can be visualized below
2. Tokenizing
Splitting article body into sentences, then into words
SentinetWordNet provides a trained index of words and their corresponding positive, negative and netural sentiments (where the sum of these sentiments equal 1). To generate sentiments at an article level, the articles were processed into count vectors. Stopwords were stripped and a dictionary of words was built using the entire corpus. The articles were then vectorised (count vectors of words in the dictionary). The mean sentiment of the words in the vector were used as the sentiment score for the entire article. A potential problem behind this method is that we may not obtain accurate sentiments for sections of the article which depend on more sophisticated understanding of the natural language at a phrase or sentence level. However, given the computation time required to use more sophisticated natural language processing techniques (such as Word2Vec or Doc2Vec) and the total number of articles to be considered across the time period, we decided to proceed with the simpler model.
3. Filtering Neutrality
Removing sentences with neutral sentiment from the original article
Given each article, we removed any sentences which had very high neutral sentiment scores. This ensured that our sentiment score generator would only focus on sentences which implied strong sentiment, resolving a previous problem of obtaining very low positive/negative sentiment scores from our model. We also investigated different neutral sentiment thresholds to see which would be the most effective for removing neutral sentiment sentences. For each of these thresholds, we calculated the sentiment scores of the articles (with neutral sentences removed) at a weekly level. This time series of positive and negative sentiment scores was compared with the exchange rate time series to obtain correlation values. We chose the optimal threshold by maximising the magnitude of correlation values. This gave an optimal threshold of 0.9 and all articles were processed using this value to remove neutral sentences
4. Weekly sentiment
Averaging article sentiments at a weekly level to obtain weekly sentiment scores.
Having developed a method to generate sentiment scores for a given article, we simply took the mean average of the articles at a given week to generate the sentiment scores associated to that week. For future work, it may be worth considering potential ways to weight different articles' sentiments based on factors such as their popularity.
5. Smoothing
Reducing the weekly variance of sentiment scores with a rolling average
Having generated weekly sentiment scores, initially the high weekly variance did not allow any visible trends to be noticed. We resolved this by smoothing the weekly sentiment scores using a rolling average of a fixed number of preceeding weeks. This idea has a natural motivation - overall sentiment on a given week is also dependant on the sentiment of the previous few weeks. The optimal number of preceding weeks used to smoothen the sentiment scores was chosen by maximising the correlation between the exchange rate and the sentiment score time series. The smoothed sentiment scores now show visible correlation with the exchange rate, especially during times such as the Brexit referendum. This is shown below.
The weekly scores were saved and used in the multivariate prediction model
Baseline Model
In building a baseline model we considered several approaches including univariate versions of ARIMA models (autoregreesive integrated moving average models involving using lagged values of the response variable as well as moving average terms), and autoregressive models as well as multivariate models incorporating Libor rates and lastly sentiment scores.
Exchange Rate Series
Let's take a look again at the exchange rate plot, zooming in on times of interest
First, lets take a look at the global financial crisis, and event that preciptated a drastic change in the UK-US exchange rate.
Next, take a look at the at the time period around the Brexit referendum, which saw the value of the GBP at a local minimum relative to a 25 year time horizon.
Stationarity
An important assumption in much of models underlying time series analysis is that the mean and variance of the data do not change over time; if the data has an underlying trend, this will certainly not be true. As such, most time series analysis occurs on data rendered stationary, traditionally by taking first differences. After differencing, we see that aside from a time of extreme variation around the global financial crisis, data is reasonably stationary with mean zero and relatively stable variance outside of the 2008-2010 time frame. Keeping the anomalies of the financial crisis in mind, we can move on to the next segment in building our model.
Autocorrelation
Let's first start by taking a deeper dive into the first lag of the first-differenced exchange rate as this should give us some information as to whether or not a simple AR(1) process can adequately model our data. If we see a non-zero slope in the correlation plot, we can infer that there is, in fact, a good relationship between this week's exchange rate and last week's exchange rate.
The first difference is almost certainly zero, with both axes having data appearing to be drawn from a roughly normal distribution with mean zero. There does not appear to be much gleaned from relying entirely on the first lag.
Let's now take a look at further lags (lags 2 through 40) to see if there is any other information available in prior lags.
The lags appear to bounce around zero - an AR process might not be able to glean all that much from the underlying data, although regularization might improve this (discussed later)
Univariate ARMA models
Although we saw from the autocorrelation plot above that an AR processes will likely perform trivially, a well-developed baseline model is required against which we will compare our sentiment score-augmented model.
ARIMA models are set of time series methods that include autoregressive components (AR), differencing (I), and moving average terms (MA) in order to capture various dimensions by which time series trends can be described.
AR model
As mentioned earlier, AR terms capture the number of lags to be used by the model. Here, we select an AR(6) process as the sixth lag is somewhat significant in both the ACF and PACF plots above. The AR(6) process appears to perform quite well (although we should remember that exchange rates do not vary all too much week over week).
MA model
We also considered a standalone moving average model (using two moving average terms) for the sake of exploration. This appeared to attenuate the magnitude of our predictions and lead to a poor model result.
ARMA model
Our final baseline model was a combined ARMA (6,5) process that yielded the lowest RSS on the data, with a plot of the prediction overlayed on the original data below.
Lasso regression model
For further improvements from the baseline model, several other models were considered including regression models with L1 and L2 regularisation. This is equivalent to an autoregressive model and would enable us to examine the magnitude of the coefficients assigned to each lag (i.e. the importance of each lag in generating a prediction).
1. Create a matrix of lagged predictors
Matrices of predictors were generated with the lagged response variable and this was used with Lasso regression to determine the coefficients of the lags. The data was of the form:
To determine the number of lags used in our model, we split the given data into training and test sets and analysed the cross validation scores. This is gave 20 lags as being the optimal choice, as shown in the matrix above.
2. Split into test and train sets and analyse model forecasts
As the predictors are given by the rows of the predictor matrix, each observation is independent i.e. sequential slices of the data are no longer required for cross validation. The data was randomly split into test (40%) and train (60%) sets
3. Use lasso regression to fit a model to the training data
Having chosen the number of lags, we now fit a lasso regression model on the training data. We choose to use the lasso because it would give sparse coefficients in our prediction lag variables, so that potential interpretations can be made after the fit. Cross validated Lasso regression was used to optimise the hyperparameters.
4a. Visualising the predictions on the test set
We first visualise the predictions from the lasso model on the test set, to obtain a sense of the overall fit compared to the actual valus
As visualised above, our predictions are close to the actual exchange rates on the test set and the R2 of the model is high (0.987)
4b. The regression coefficients of the fitted model
We can analyse which of the time lags are the most significant in our model by considering the fitted regression coefficients
Visualisation of the coefficient values shows that beyond the first lag, the subsequent lags have little predictive value. Lasso regression squashes the predictive power of some coefficients in favour of others, and in this case has put all the predictive power in the first lag.
4c. Residual errors of the model
We can analyse the distribution of the residual errors of the model to determine if there is a predictive bias
The residual errors have a distribution with mean approximately zero. This means on average our prediction model does not under or overestimate the actual values.
The univariate model is seen to have high predictive accuracy in determining the exchange rate for the following week, however forecasts further in the future are likely to have much lower accuracy (discussed later) and in these situations, the effect of news sentiment may be useful
Multivariate Lasso regression
A predictor matrix containing 20 lags of preceeding exchange rate, 20 lags of preceeding positive sentiment scores, and 20 lags of preceeding negative sentiment scores was constructed of the form
Again we can analyse which of the time lags and the positive/ negative sentiment lags are the most significant in our model, by considering the fitted regression coefficients
The lasso regression model has assigned high positive predictive value to the first lag as before, but the coefficients of lags of the sentiment scores are also seen to have non-zero magnitude. The positive sentiment score approximately 5 weeks prior, as well as the negative sentiment score approximately 3 weeks prior are both seen to have large coefficient values. The coefficients vary for different testing and training sets indicating that the high values for sentiment in certain weeks may be random - i.e. the important lags of sentiment are not the same over time. This will be investigated further in our future work.
Residual errors of the model
We can again analyse the distribution of the residual errors of the model
The residual errors have a distribution with mean approximately zero. This means on average our prediction model does not under or overestimate the actual values.
Model Comparison
Finally, we consider the R^2 values of the different models considered for comparison with our final foreact model which incorporates the sentiment scores.
There is little increase in the predictive accuracy of the model with the incorporation of sentiment scores, showing that a simpler univariate model is in fact equally as useful as a more complex model with a larger predictor space
Forecast Methodology
Exchange rate forecasts were generated in an incremental process:
1. The lags of time step t were used to generate a prediction for time step t+1
2. This was used as the best estimate of the exchange rate at time t+1 and used as one of the predictors to predict the value of the exchange rate at time t+2
This incremental forecasting process was continued up to the desired forecasting horizon. It is important to note that at each forecasting step, the error increases significantly and the confidence bounds after even 2 time steps forecasts are large. The plot below shows 20 week forecasts generated from each time point. The forecasts are shown as the coloured lines.
The forecasts are seen to have very little predictive accuracy beyond predicting 1-2 weeks in the future. This result is unsurprising. Given the inherent unpredictable nature of exchange rates and their sensitivity to changes in the world, it would be unrealistic to expect that the exchange rate could be forecasted several weeks or months into the future. Looking more closely at the direction of prediction, during the sharp fall in exchange rates during the financial crisis (late 2008), the predictions are in the positive direction, and it takes several months before the direction of prediction aligns with the local direction of movement. Following the financial crisis, the predictions all show the value decreasing from the value at the local time point. This analysis shows that long horizon forecasts of the exchange rate are difficult and most probably inaccurate.
Model Comparison
Considering the R^2 values of the different models for one week forecasts, the ARMA model is seen to have the lowest accuracy. A univariate lasso model is almost equally as good as a model with sentiment scores included suggesting that the sentiment scores provide little additional predictive power.
In this project our goal was to predict future exchange rates at a weekly level. Using techniques from time series analysis and SentiWordNet, we developed models for exchange rate prediction which incorporated weekly sentiment scores from relevant news articles. We found that the addition of sentiment scores into the model does not necessarily result in an improvement in prediction accuracy, contrary to our initial hypothesis.
This is actually consistent with existing research literature (see [3]), where it is found that news sentiments are useful in indicating the direction of exchange rate movements but ineffective in predicting the magnitude of the exchange rate movements. Therefore, incorporating the sentiment scores through a large predictor space does not necessarily lead to improved predictive accuracy.
This project has highlighted the intrinsic difficulties of predicting exchange rates using news sentiments. As the exchange rate market is so liquid and fast-paced, any impact from extraneous events is reflected immediately on the rates, often much sooner than when an article is written about these events. Perhaps for future work it may be more fruitful to consider sentiment of news sources such as Twitter where news is reported much faster.
There are various areas we can investigate to further improve our model and develop this project:
Engineering and Applied Sciences
Harvard Business School
Statistics


_Star_Wars:_Episode_I--The_Phantom_Menace_ (PG) *** 1/2 (out of ****)
There are a couple of angles at which to approach _Star_Wars:_Episode_I_--The_Phantom_Menace_: in comparison to the three episodes that have been released; or, ideally, as an individual film, in and of itself. In the latter regard, _The_Phantom_Menace_ is the type of exceptionally well-made, highly imaginative science fiction adventure that one would expect from the mind of series creator George Lucas, who makes an impressive return to the director's chair after a self-imposed 22-year hiatus. It is in the former respect, however, that the film cannot help but fall short.
The shadow of the first three films released in the series--1977's _Star_Wars:_A_New_Hope_ (Episode IV), 1980's _The_Empire_Strikes_Back_ (Episode V), and 1983's _Return_of_the_Jedi_ (Episode VI)--looms large over _The_Phantom_Menace_, and it's not just because those landmark films have such an enduring legacy. Rather, it's because Lucas's _Phantom_Menace_ script is a hodgepodge of different elements from those three films. To start, the Gungan, an amphibious race on the planet Naboo, are scrappy warriors along the lines of _Jedi_'s Ewoks; a pod racing scene is pretty much _Jedi_'s forest speeder bike chase transplanted onto the desert; dual light saber-wielding villain Darth Maul (Ray Park) is a badass scenestealer in the tradition of Boba Fett, who first appeared in _Empire_.
The installment that _The_Phantom_Menace_ most closely resembles, however, is _A_New_Hope_. There's a wise elder Jedi Master, Qui-Gon Jinn (Liam Neeson), not unlike _A_New_Hope_'s Obi-Wan Kenobi. Obi-Wan is also in this episode, in a younger, wilder incarnation (played by Ewan McGregor) that recalls Luke Skywalker. Other similarities include a lavish celebration scene, the destruction of a space vessel, and the intricate, Princess Leia-to-the-next-level hair design of her future mother, Queen Amidala (Natalie Portman) of Naboo.
Unfortunately, _The_Phantom_Menace_ also falls into the same narrative rut that _A_New_Hope_ did in its first act, but to a much larger degree. After an interesting opening section, from Qui-Gon and Obi-Wan's rousing slice and dice through squads of battle droids aboard a Trade Federation spaceship to their rescue of Amidala from evil Federation forces on Naboo, the story gets bogged down in exposition once our heroes land on the desert planet of Tatooine. There, Qui-Gon discovers young Anakin Skywalker (Jake Lloyd), the future Darth Vader and focal character of this trilogy of _Star_Wars_ films. As Qui-Gon and Amidala's handmaiden Pradmé get to know "Ani" and his mother (Pernilla August), the film slows to a crawl.  Far from lovable, I wanted to strangle the critter by his second scene.
Things pick up with the aforementioned pod race sequence (which, I must say, is every bit the thrill ride the _Jedi_ speeder bike chase is), only to fall into more talky exposition, which only serves to make _The_Phantom_Menace_'s main story needlessly convoluted and, as such, largely uninvolving. Basically the plot boils down to Amidala being violently strongarmed into a treaty with the evil Trade Federation, which has been working with the mysterious Darth Sidious (the "Phantom Menace" of the title), whose main enforcer is the deadly Darth Maul.
However, this is not to say that the first two-thirds of _The_Phantom_Menace_ is as dry as a Tatooine summer (or spring... or fall... or winter). Far from it--though the story may not keep one consistently engaged, there are other things that do. Always capturing one's attention--and imagination--are the state-of-the-art visual effects on display. One of the greatest delights of this and the other _Star_Wars_ films are the new worlds springing from Lucas's fervid imagination. Tatooine is the only familiar pit stop; also on the travel itinerary are the Coruscant (briefly seen at the end of the _Jedi_ Special Edition), the city-covered planet that serves as the home of the Galactic Senate; Naboo; and the Gungan's hidden undersea home on Naboo. Then, of course, there are the various effects used to populate the streets of these worlds with exotic alien creatures, as well as those used to depict the spaceways and the crafts that travel them. Some CGI shots are more convincing than others, but they never fail to be the slightest bit believable or intriguing.
The new troupe of actors holds their own against the largely digital landscape. Neeson exudes the right air of authority and solemnity as Qui-Gon, as does Samuel L. Jackson in a much-publicized cameo as Jedi Council member Mace Windu. While Mark Hamill and Carrie Fisher did not settle into their acting stride until _Empire_, Portman and McGregor have comfortably nailed down their roles in their first outing, though McGregor's fairly limited screen time is somewhat surprising. On the other hand, not so surprising is the fact that Lloyd is the weak link in the core four. In all fairness, he does an adequate job as a whole, but that does not mean that he's immune to the stiff and cloying moments that often befall child actors; prepare to cringe when Lloyd lets out a forced "Whoopee!"
The numerous slow patches ultimately just makes one more appreciative of the pure visceral excitement of the slam-bang third act. Lucas cuts loose, following no less than four concurrent battles in which many shots are fired from pistols and space craft, light sabers are crossed, energy balls are flung, and more than a little property is destroyed. The highlight by far is an exhilarating, series-best light saber duel pitting both Qui-Gon and Obi-Wan against that dastardly Darth Maul, who more than lives up to his pre-release hype.
That mostly all of the action comes at the end will undoubtedly disappoint die-hard _Star_Wars_ fans and casual moviegoers alike. But with so much hype surrounding it (largely generated, in a nice change of pace, by the fans, _not_ the studio), there was no way _The_Phantom_Menace_ could live up to the overinflated expectations. What it could have possibly lived up to is the _Star_Wars_ legacy, and in time, it very well may--with _Episode_II_ and _Episode_III_ still yet to come (in 2002 and 2005, respectively), it's impossible to judge how well _The_Phantom_Menace_ plays within the context of the entire saga. At this point in time, however, _The_Phantom_Menace_, as polished and entertaining as it is, has nothing in it that quite compares to _A_New_Hope_'s euphoric sense of wonder and discovery; the exciting action highs and the despairing emotional lows of _Empire_; or the emotional catharsis of _Jedi_'s highly resonant climax. (opens May 19)
Michael Dequina
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Types of viruses
How to treat a virus
Most dangerous viruses
CDC Organization
Example of a Spherical Model virus
Example of a Polhedral Model Virus
Example of a Helica Model virus
Example of a Bacteriophage Model Virus
Concept of a Virus


Hi! I'm Daniel. I was born and raised in Venezuela where
I attended Universidad Del Zulia and graduated with a
bachelor's degree in Chemical Engineering in 2015. For
my senior project, I designed a recovery pilot plant for
automotive used oil, for which my team won the support
of a Ford dealership to carry on the project. Also, we
were selected to be published in Univeridad Del Zulia
scientist magazine, "REDILUZ".
During my time in college, I was part of multiple
student groups, and attended 2 international student
conferences in Costa Rica and
Argentina. Upon graduation, I joined a Christian campus
ministry and worked there until 2016. Afterward, I moved
to the United States, and the past year worked as
Quality Technician at the ZF plant in Oakwood, GA.
I've always had a natural curiosity for the "why of
things" and it excites me to see the process and outcome
of solving problems. Now that I've moved to the States,
this curiosity has brought me to Software Engineering.
So after a long process of validating my credentials, I
was accepted in the Fundamentals of Computer Science
Graduate Certificate Program at Kennesaw State
University. Once I've finished the certificate program,
I will be qualified to apply to the Computer Science
master degree at Kennesaw or any other university. In
the meantime, I'm glad to be accepted and have joined
the Full Stack Web Development Program at Georgia Tech
University for the Summer of 2020 with which I aim to
get a step ahead in the workplace while pursuing further
education.


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Flooding in Yorkshire – in pictures
Severe storms have brought a month’s rainfall in less than 24 hours to parts of central and northern England. Police have said a woman’s body has been found near a stretch of the River Derwent close to the town of Darley Dale in Derbyshire, while in Sheffield dozens of people were trapped in a shopping centre overnight after becoming stranded by rising waters. The torrential rain also brought widespread travel disruption
- Woman dies as floods hit Midlands and north of England
- Floods force shoppers to spend night in Meadowhall
A young child is rescued by a crew from South Yorkshire fire and rescue service in Doncaster
Photograph: Christopher Furlong/Getty Images
Flooding in the Bentley area of Doncaster. South Yorkshire police said there had been ‘significant issues’ in Doncaster, with Bentley, Toll Bar and Scawthorpe the worst affected
Photograph: Gary Calton/The Observer
Flooding in the Bentley area of Doncaster. As of Friday morning, the Environment Agency had three severe ‘danger to life’ warnings in place relating to the river at Kirk Bramwith, South Bramwith and the Willow Bridge caravan site, all in Doncaster
Photograph: Gary Calton/The Observer
Photograph: Gary Calton/The Observer
Photograph: Gary Calton/The Observer
Photograph: Christopher Furlong/Getty Images
Photograph: Gary Calton/The Observer
The fire and rescue services evacuate a disabled man from his home in the Bentley area of Doncaster
Photograph: Gary Calton/The Observer
Photograph: Oli Scarff/AFP/Getty Images
Photograph: Christopher Furlong/Getty Images
Members of the fire and rescue service in Doncaster
Photograph: Oli Scarff/AFP/Getty Images
The roof of a car pokes out of the waters of Doncaster’s River Don, which burst its banks
Photograph: Oli Scarff/AFP/Getty Images
A car abandoned in flood waters in Rotherham
Photograph: Darren Staples/Getty Images
A Range Rover is driven out of flood waters after the River Don burst its banks in Rotherham
Photograph: Darren Staples/Getty Images
Photograph: Darren Staples/Getty Images
Photograph: Oli Scarff/AFP/Getty Images
Water covers the rail tracks at Rotherham Central train station. Northern Rail said it expected ‘severe disruption’ to its network, with services unable to run between Sheffield and Gainsborough Central or Lincoln Central. Services have also been suspended between Sheffield and Leeds via Moorthorpe following the deluge
Photograph: Oli Scarff/AFP/Getty Images


Introduction Frontend: HTML5 / CSS / JS
Sam Vloeberghs — @samvloeberghs
CC Front-end & UX
Introduction Frontend: HTML5 / CSS / JS (1)
- HTML5 history & elements
- CSS and why to seperate content / design
- The purpose of JavaScript & how applied today
Introduction Frontend: HTML5 / CSS / JS (2)
- Browser vs desktop
- Write once run everywhere
- Highly maintainable
Differences HTML4 & HTML5
- More semantics
- Forgiving browsers
- New JavaScript API's
More semantics & Forgiving browsers
- nav, header, footer, section, article, ..
- HTML soft syntax <> XML hard syntax
- fix omitted endtags
CSS and its purpose
- Cascading Style Sheets
- Style content
- Not so much properties in level 1-3
- But many usecases!
CSS Level 1
- fonts, backgrounds & colors
- text formatting
- alignment of elements
- positioning, margin/padding & bordering
CSS Level 2 & 2.1
- New positioning concept ( abs / rel + z-index )
- Media types
- Error fixing
CSS Level 3 ( 4 modules )
- Media queries (!)
- Namespaces
- Selectors
- Color
@media (max-width: 600px) {
.facet_sidebar {
display: none;
}
}
CSS Level 4
- Extensions of the 4 modules
- New modules
JavaScript
- in the browser & server
- manipulate the DOM
- frameworks & libraries ( AngularJS, jQuery, .. )
Browser JavaScript API's
- WebStorage, File API
- Audio, Video, Canvas
- Battery, Bluetooth
- Dom manipulation + Shadow DOM
- ...
Documentation / links etc.
Usecase : Blog + detailpage + contactform
- Apply semantic HTML5
- Style the page with CSS
- Validate forms with JavaScript / jQuery
- Keep in mind responsiveness
Part #1: Building + styling a page
- Create a wrapper, center it, width 960px
- Use the logo in the heading, align left
- Build a navigation of 3 items, align right
- Center the text of the footer
Part #2: Using correct elements
- Create overview of articles on homepage - [] [] []
( align them horizontal )
- Create a detail page of blogpost
Part #3: Building a form
- Build a form with 4 elements
- Write CSS classes for input error cases
Part #4: Validating a form
- Validate the form elements
- Emphasize the incorect fields
- Show appropriate messages
- Use HTML5 validation
Part #5: Making it responsive
- Make the container auto width if viewport < 960
- 'Responsify the articles on the homepage'
[] ( align them vertical )
[]
[]
- Reformat the logo to be on top the navigation
- Replace the normal navigation with a select navigation
Part #6: Help out or improvise
- When done help out others
- Improvise functionality & build it:
- Show the 5 latest tweets from OrdinaBE
- Build a comment form & validate it
- ..


{% extends "base.html" %} {% load static %} {% block content %} {% csrf_token %} Are you sure you want to delete {{ object }} Cancel {% endblock content %}


{% extends "base.html" %} {% block content %}
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{{ notebook.description }}


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TensorRT
<IP_ADDRESS>
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RPROIParams is used to create the RPROIPlugin instance. It contains: More...
#include <NvInferPluginUtils.h>
Public Attributes | |
| int | poolingH |
| int | poolingW |
| int | featureStride |
| int | preNmsTop |
| int | nmsMaxOut |
| int | anchorsRatioCount |
| int | anchorsScaleCount |
| float | iouThreshold |
| float | minBoxSize |
| float | spatialScale |
RPROIParams is used to create the RPROIPlugin instance. It contains:
| poolingH | Height of the output in pixels after ROI pooling on feature map. |
| poolingW | Width of the output in pixels after ROI pooling on feature map. |
| featureStride | Feature stride; ratio of input image size to feature map size. Assuming that max pooling layers in neural network use square filters. |
| preNmsTop | Number of proposals to keep before applying NMS. |
| nmsMaxOut | Number of remaining proposals after applying NMS. |
| anchorsRatioCount | Number of anchor box ratios. |
| anchorsScaleCount | Number of anchor box scales. |
| iouThreshold | IoU (Intersection over Union) threshold used for the NMS step. |
| minBoxSize | Minimum allowed bounding box size before scaling, used for anchor box calculation. |
| spatialScale | Spatial scale between the input image and the last feature map. |