In any art-form, it's important to identify a focal point and spend extra time adding an appropriate amount of detail and work to it in order to bring life and personality to your project. In game development we call this "polish". You can polish every little part of your project until your eyes bleed but it's not going serve much purpose if you don't spend most of your time working on the foundations of your game. If you finish a piece of code and you're sure that it's not going to change much throughout the remainder of the game, you can polish early. Otherwise, try to put it off as long as you can. There are going to be times where you're going to toss entire game mechanics. It's obviously better not to polish those pieces until you've decided. It's all about timing, and no one is perfect at that, but everyone gets better with experience.
In VR, the focal points are usually objects you're able to pick up, or characters that interact with you. To make the player feel like there is something deeper than a flat lifeless 3D model, you should add animations, interaction scripts, and haptic feedback(controller vibration)*. For example, seeing swords collide with "clanking" sounds and physics is one thing, but to feel your sword grinding against a metal surface (or the increasing tension in your bowstring) is on another level. It's the layer of feedback that pushes you over the edge, where you allow yourself in a certain respect, to believe the ruse.
When you shake a balloon it should jiggle, it should deform from the water sinking on one side or the other based on gravity and how the player is tilting it, and it should pop in a satisfying way. Water balloons are satisfying to pop. Keep that in mind when you're designing things based on 'satisfying feelings', what is it that is satisfying, what is supposed to be enjoyable about this thing?
In this section I'm going to walk you through the parts of the water balloons in Plunker in 5 sections.
Notice that everything we're doing is basically polish, but when I was first creating this balloon mechanic I grey-boxed everything first. "Grey-boxing is the traditional technique of blocking out the level with simple shapes to see if the level design supports the gameplay before creating the final art assets for the level". If you end up not liking how the scripts and interactions turn out, this is where you may decide to toss something. Try to always grey-box things first (or make very quick minimal models if you feel like you need to).
When beginning this balloon mechanic, I first thought of it as an interactive object which means that it has multiple states and transitions between them. The model we're creating below is what you hold and throw, most of the time it will be "popped" or basically non existent. We can talk more about the transitions and states in the 2nd section since they are mostly code. But just keep in mind that I'm only walking through this section first because it's more visually appealing for the walkthrough.
If you're not familiar with 3D modeling, it's not the end of the world, although it is a major limiting factor on your indie-dev life. I've toyed around with Blender (a free 3D modeling program) for about 3 years. It's fairly easy to learn how to make geometric or simple objects and I highly suggest making it a priority to understand the basics of 3D modeling. There isn't otherwise much to be said about this model. It's simple. One thing I might make note of is how many vertices this mesh has (around 750). If the balloon were a side object on a shelf or otherwise, I may have used less verts, but since it's going to deform, and it is a focal point of this section in Plunker, it's nice to have more.
Rigging is a term in game development that refers to adding bones to a 3D model. If you're not familiar with rigging, this is another important point I would highly suggest committing some time and energy into. You won't need to learn to the extents that I have, but if you have a basic understanding then you'll be able to work well on a team and communicate with artists on a better level.
One notable thing you can see in this picture is how small the bones are. The ends don't even touch. This is because I'm mostly using automatic weighting and if the bones are smaller, the edges are more evenly weighted around the bone. This makes for easy animation when we need a body of water to jiggle around a central point. You can make animations in blender, but you'll want to use Mechanim© in Unity to fine tune things. Either way its just a matter of making the bones scale and rotate in a way that looks satisfying. Once you've got that done, you'll have a pretty juicy looking balloon.
We'll want to create a popping animation to transition between a "thrown state" and a "popped state". The popped state won't have anything rendered at all, we'll just pretend that the water and the remaining rubber flew everywhere. The unity particle system is great for making animations like this, and it's usually necessary if you want to polish your game. There are several layers to our "sploosh" animation. There's the spray of water as it mists into the air, as well as the thicker explosion of water near the center. There's also a very quick flash of red balloon rubber. If you'd like to learn more about making particle systems here is where I learned a lot.
start with the central spray like this.
Then you'll add a different type of splash particle. It's all about layering. Everything looks better with a few layers of different content.
Once the splashes are in place, you'll want a flash of red rubber to bridge the gap of the balloon disappearing.
Once all of the pieces are looking ok, you can put them together. (make sure to place them under each other in a hierarchy and simulate in world space so that they scale properly and they all play together at the same time - on wake).
Layering and taking the extra time to tweak your particle systems can make parts of your game stand out in a memorable, striking way. This makes a massive difference to the look of the game, for little cost.
SteamVR has a plugin for Unity where they provide some C# example scripts that save a lot of effort and time. If you're writing your code in C# from scratch then you're wasting a lot of time. Instead I suggest altering the provided scripts to your needs. In our case we're simply taking advantage of the basic Throwable.cs and it's easy to access OnPickup() and OnDrop() events to call our own functions when the player grabs or lets go of the balloons. If you're making a VR game, you should study these scripts to understand how they're working. I go into more detail on VR interaction in the VR-Util guide.
OnPickUp() calls a behavior function called SpawnBalloon(). This is because the balloon starts and ends in a sleep/popped state where it is an invisible object in a "balloon basket" waiting to be picked up.
OnDrop() calls a behavior function called Throw(). This sets the balloon into a state where it knows that it has been thrown and it's only waiting for a collision to pop it.

Now I'll show you some of the code as examples
Below you can hear the audio sound that is played. Satisfying.
When you create a jiggle animation, you'll need to have an animation that can show the balloon jiggling without overriding any other animation states that are currently being used. For example we can used Unity's Mechanim to create animation layers to have the states blend. Say that you have an animation that stretches the balloon out one way or another and you also want to control when it jiggles separately. Well you can do that with layers. Since we're going to control jiggling independently of other animation states, we put it on it's own layer, and set it as an additive layer. We can then choose how much of that layer is blended.
If we only ever have one continuous jiggling animation, we can make the balloon infinitely jiggle on this layer and then change the layer weight to control how much jiggling gets blended in to the end result. This can be accessed and changed at runtime through code but above we can see the effect it will have by using the UI.
I'll show you our update loop for the balloon and you can see how we determine how much and how long we want to jiggle it.
Since we move the balloon kinematically (by parenting it to the hand), we cannot get the velocity from the rigidbody, so we need to calculate it on our own with simple math and a few variables to keep track of displacement every frame. With this, we also calculate acceleration. If you recall, forces act on bodies in motion as a result of acceleration, so we'll only want to jiggle when there is a force jiggling the ballon (which is going to happen when the balloon moves or stops moving suddenly). When the acceleration surpasses a certain threshold we define, we can set the blend weight of our jiggling layer. We don't want this jiggling to turn off instantaneously, so we slowly decrease it until it is back to zero.
We already have a float variable jiggle_layer_weight that keeps track of how much our ballon is jiggling. If we use SteamVR's built in controller-vibration functions, it will be relatively easy to make it feel like the balloon is vibrating in your hands. Because we're using SteamVR's Throwable.cs script, we only have to access the correct hand and call a public function on it's Hand.cs Script. In the code below we've created a global variable, hand, that keeps track of which hand is holding the balloon. You'll want to set it when you grab the balloon and set it back to null when you let go as seen below. When you would update the animation weight, you'll also vibrate the controller by a propoertional amount. That's all there is to it. Such an easy step for what it accomplishes.
The balloon now jiggles when you shake it and gives you some physical feedback. Great, we like this. The last bit will seal the deal, and make the balloon a satisfying bit of VR experience. In this part we'll make sure the balloon deforms somewhat realistically based on how you're holding it and the effects of gravity. Lets take another look at the Update() function that we used earlier. We'll add the following lines that reference the Animator's other layer. This is why we have layers, so that we can deform the balloon and ensure that the deformations are a base animation and that jiggling is an additive animation.
Alone, this code senses what the upward component of our normalized forward vector points either up or down by a certain amount (0.15). It then sets state variables in the animator so that we can control the animations based on it's state. Is the knot side facing downward or is the smooth side facing downward or are they both pretty balanced? In these images you can see the parameters section of the animator is where we define the variables that we're referencing in our scripts.
To show the primary logic drivers in the animators state machine, we'll look at the transition definitions in the third picture. You can find this view by clicking on one of the arrows between two states and then setting it up from there. If you have no transitions defined you have to create transitions.
You can also see a state diagram that is extrordinarily simple since every since state can transition to every other state and all states are mutually exclusive at any one point in time. If you've been a software developer you can probably do a lot of this on your own without the UI based tools I present here. I'm showing you this because I did that for a year or two when I started out but I've learned that the more complex animations become more manageable with this sort of UI so long as you're not doing anything completely different than what this was made for.
I may have already mentioned this above, but when you've got a useable item like these water balloons, you don't want to instantiate a new prefab every single time the player grabs one, and you don't want to have a million instances of balloons laying around. The proper setup is keeping a few balloons in a deactivated state somewhere and activating them when they player needs one. This is why we have the popped state.
When the balloon is sitting in this basket, it is invisible and it has a collider that allows us to spawn the balloon. If you return to the code snippet above, spawning the balloon does a few things including enabling the renderer. This basket is also a reference to where the balloon should return once it finishes popping so that we can grab it again.
Since we only have two hands and we may want more buckets, we could rework this so that there are colliders on each basket that move the balloon into position and allow you to grab them so that we could have only two balloons present in the scene at any time rather than 2 per bucket. This is an extra exercise for you, and I may update it if I find it neccesary during development. Right now, I can't imagine having a few extra deactivated balloons causing any problems, but optimization is always fun if you have time.
In Plunker these water balloons were first thought up to be weapons against sand creatures.  I didn't feel like shooting sand with a gun was fun or satisfying, and a friend of mine threw out the idea that I should use water instead of bullets. So here I am with awesome balloons. I've reworked the minigun turret to have larger barrels and shoot balloons as well. Also Satisfying. The last bit of development I'd like to walk you through is how to make the balloons interact with specific game objects that are meant to do something when they are hit.
In order to execute a function upon colliding with an object you'll need to create a universal script that has a few universal functions to recieve damage. This will be a script that defines the skeleton of our base class. This portion of game dev is really fun because we get to take advantage of polymorphism. We can then specify another class specifically for our sand creature that will override our original functions so that no matter what is taking damage, the balloon can reference any class that derives from RecieveDamage.
Just to clear up what I mean by example: when the balloon collides with anything, we'll check if there is a script of type<RecieveDamage>. If there is a script of type<SandBoy_RecieveDamage> or type<BreakableCrate_RecieveDamage>, we will get a reference to them as long as they derive from type<RecieveDamage>. We'll be able to call public functions that appear in the base class. So if we call SendDamage(500) on the script, it may reduce the Sand Creatures health, or it could break a breakable box, depending on the overridden functions we write in the derived classes.I won't go into anymore detail here.
Here we see the base class defined with only one public virtual void function. This makes our jobs easier if we make this function warn us that we're using it since our scripts will likely not want to reference this base class (although if we had a common piece of code we could put it in here and refernce it in the override.
Next we see the subclass in our other script which has a little extra to show off why you may want to do this. I've defined an enum to contain specific places where the sandboy can be hit depending on which collider gets hit. I've chosen to serialize the field so that on the sand boy prefab I can put on of these scripts on each body part and set whether it's the head, leg, arms, etc. This way I can set it in the UI without leaving it accessible for code to access it as public. I've uysed a switch-case statement for my own readability to determine what should be done based on which part was hit. I have animations for the SandBoy to react when he is hit on different body parts, and its primary script keeps track of its health.
I honestly could go deeper and continue writing about every facet of this entire game forever, but then I may never finish the game itself. This is the stopping point for now; I hope you learned a lot from following along and feel free to use whatever you can grab from this in any way you'd like, commercial or otherwise.  In which case I can't imagine it would sell well and I'm not worried about it hurting my project. Cheers.
BalloonAssets.zip
If you're going to use my source code for your project or even poke around in it, please read through the rest of this walkthrough.  Ideally, you'll take pieces that you want and make a very modified version once you understand exactly what all of the parts do. I want to educate rather than give away free code, but again, you're free to do whatever you want with this, commercial or otherwise! If you'd like to make it rain, here is my money catching bucket. https://www.paypal.me/AuggoDoggo/5
If you felt any part of this guide was awkwardly paced, or hard to follow, please send me an email at<EMAIL_ADDRESS>and give me a chance to clear it up and improve at sharing my code creations. In the future, this is where I'll post answers to specific questions that aren't simply answered by updating a portion of the guide.


- clear() - Method in class de.mrapp.apriori.ItemSet
-
- clone() - Method in class de.mrapp.apriori.Apriori.Configuration
-
- clone() - Method in class de.mrapp.apriori.AssociationRule
-
- clone() - Method in class de.mrapp.apriori.FrequentItemSets
-
- clone() - Method in class de.mrapp.apriori.ItemSet
-
- clone() - Method in class de.mrapp.apriori.Output
-
- clone() - Method in class de.mrapp.apriori.RuleSet
-
- comparator() - Method in class de.mrapp.apriori.ItemSet
-
- compare(AssociationRule, AssociationRule) - Method in class de.mrapp.apriori.Sorting.AssociationRuleSorting
-
- compare(ItemSet, ItemSet) - Method in class de.mrapp.apriori.Sorting.ItemSetSorting
-
- compareTo(AssociationRule) - Method in class de.mrapp.apriori.AssociationRule
-
- compareTo(ItemSet<ItemType>) - Method in class de.mrapp.apriori.ItemSet
-
- Confidence - Class in de.mrapp.apriori.metrics
-
A metric, which measures the confidence of an association rule.
- Confidence() - Constructor for class de.mrapp.apriori.metrics.Confidence
-
- confidenceDelta(double) - Method in class de.mrapp.apriori.Apriori.RuleGeneratorBuilder
-
Sets the value, the minimum confidence should be decreased by after each iteration, when
trying to generate a specific number of association rules.
- Configuration() - Constructor for class de.mrapp.apriori.Apriori.Configuration
-
Creates a new configuration of the Apriori algorithm with default values.
- contains(Object) - Method in class de.mrapp.apriori.ItemSet
-
- containsAll(Collection<?>) - Method in class de.mrapp.apriori.ItemSet
-
- Conviction - Class in de.mrapp.apriori.metrics
-
A metric, which measures the conviction of an association rule.
- Conviction() - Constructor for class de.mrapp.apriori.metrics.Conviction
-
- covers(T...) - Method in class de.mrapp.apriori.AssociationRule
-
Returns, whether the association rule covers several items, i.e.
- covers(Iterable<? extends ItemType>) - Method in class de.mrapp.apriori.AssociationRule
-
Returns, whether the association rule covers several items, i.e.
- covers(Iterator<? extends ItemType>) - Method in class de.mrapp.apriori.AssociationRule
-
Returns, whether the association rule covers several items, i.e.
- custom(Comparator<AssociationRule>) - Method in class de.mrapp.apriori.TieBreaker.AssociationRuleTieBreaker
-
Adds an additional custom criteria to the current tie-breaking strategy.
- custom(Comparator<ItemSet>) - Method in class de.mrapp.apriori.TieBreaker.ItemSetTieBreaker
-
Adds an additional custom criteria to the current tie-breaking strategy.


Michał Bojanowski (Kozminski Univeristy), Dominika Czerniawska (University of Manchester and University of Warsaw), Wojciech Fenrich (University of Warsaw)
Authors thank (Polish) National Science Centre for support through SONATA grant 2012/07/D/HS6/01971 for the project Dynamics of Competition and Collaboration in Science: Individual Strategies, Collaboration Networks, and Organizational Hierarchies (http://recon.icm.edu.pl).
This is a dataset built from a qualitative study of 40 Individual in-Depth Interviews (IDI) conducted in the period April-August of 2016 as a part of the RECON project on collaboration in Polish science. This repository is an R package, but the data is also stored in portable CSV format so that it can be used with any other analytical software.
Data consists of 40 individual in-depth interviews conducted between April and August 2016 by two interviewers. The interviewees mentioned 333 collaborators in total. The sample consists of 20 female and 20 male scientists from six Polish cities. Respondents represented a broad range of disciplines: natural sciences, social sciences, life sciences, the humanities, engineering, and technology on different levels of career from PhD candidates to professors.
Each interview consisted of several parts two of which are of relevance here:
nodes
table described below.collaboration
table described below.resources
and described in detail below.While collaboration networks assembled from part (2) include alter-alter ties, the data on resources is available only for ego-alter dyads.
The data is contained in three tables as shown in the diagram below:
In all tables the NA
symbol (Not Available) is used to encode missing information.
The nodes
table has 374 rows and the following 7 columns:
id_interview
– Unique interview identification numberid_node
– Node number unique within each interview. Value 0
corresponds to the respondent (the ego)is_ego
– A binary variable which is equal to 1
for the respondents (the egos) and 0
otherwise.is_polish
– A binary variable which is equal to 1
if the researcher is affiliated with a Polish academic institution and 0
otherwise.department
– A numeric variable providing information whether two persons are affiliated with the same department at the same academic institution. Two researchers \(i\) and \(j\) mentioned in the same interview are affiliated with the same department if the have valid values on variable department
and these values are equal.scidegree
– Character variable encoding scientific degree. Values are "mgr"
=MA, "dr"
=PhD, "drhab"
=habilitated PhD, and "prof"
=full professor.female
– Binary variable which is equal to 1
if the researcher is female and 0
for males.The collaboration
table has 1732 rows and the following 3 columns:
id_interview
– Unique interview identification numberfrom
and to
– Node numbers referencing id_node
column from the nodes
table. As id_node
in table nodes
the values are unique within each interview. Pair of researchers declared as collaborators. For example a row with id_interview=2
, from=1
, and to=2
indicates that in the interview 2 nodes 1 and 2 where mentioned by the respondent as collaborators.The resources
table has 1761 rows and the following 4 columns:
id_interview
– Unique interview identification number.from
and two
– Node numbers referencing id_node
column from the nodes
table. As id_node
in table nodes
the values are unique within each interview.code
– Character variable indicating what type of resource was declared to flow from researcher from
to researcher to
from interview id_interview
.Possible values for variable code
are:
| code |
|---|
| career_development |
| conceptualization |
| contacts_in_academia |
| data_analysis |
| data_curation |
| data_or_other_sources |
| drafting |
| equipment |
| formal_administration |
| funding_acqusition |
| investigation |
| knowledge_other |
| methodology |
| motivation |
| non_academic_contacts |
| other_charactersitics |
| other_input |
| prestige |
| professional_achievements_formal |
| project_administration |
| proofreading |
| prototype_construction |
| software_creation |
| supervision_in |
| traits_of_character |
Below are example data and plots from interview 2.
Node data:
| id_interview | id_node | is_ego | is_polish | department | scidegree | female |
|---|---|---|---|---|---|---|
| 2 | 0 | 1 | 1 | 1 | dr | 0 |
| 2 | 1 | 0 | 1 | 2 | dr | 0 |
| 2 | 2 | 0 | 1 | 3 | dr | 1 |
| 2 | 3 | 0 | 1 | 3 | dr | 1 |
| 2 | 4 | 0 | 1 | 2 | dr | 1 |
| 2 | 5 | 0 | 1 | NA | dr | 1 |
| 2 | 6 | 0 | 0 | NA | prof | 0 |
| 2 | 7 | 0 | 1 | NA | NA | NA |
Collaboration network:
g <- collaboration %>%
filter(id_interview == 3) %>%
select(-id_interview) %>%
igraph::graph_from_data_frame(directed=FALSE) %>%
simplify()
xy <- graphlayouts::layout_with_stress(g)
plot(
g,
layout=xy,
vertex.color = "white",
edge.color = "black",
vertex.label.color = "black"
)
Resource flows:
edb <- resources %>%
filter(id_interview==3) %>%
select(-id_interview) %>%
arrange(from, to)
rg <- graph_from_data_frame(edb)
rnames <- sort(unique(E(rg)$code))
layout(matrix(1:16, 4, 4))
for(r in rnames) {
rgs <- delete.edges(rg, E(rg)[code != r])
opar <- par(mar=c(0,0,1,0))
plot(
simplify(rgs),
layout=xy,
vertex.color = "white",
edge.color = "black",
vertex.label.color = "black",
main = r
)
par(opar)
}
layout(1)
This is an R package, but you can download the files in CSV format using links below:
MIT license, see file LICENSE.md
.
A product of Provalis Research, see https://provalisresearch.com/products/qualitative-data-analysis-software/ .↩


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26-Oct-19 01:31 AM
[dscrd-hush] <Crypto99> 25%
[dscrd-hush] <Woody The Flipping Woodpecker> Heck ya up another $. I just hodl it in hope that one day will be with so much more but 1$ up is still a (+) in my books
[dscrd-hush] <Crypto99> This might come as a surprise, but most people in crypto are in the
[dscrd-hush] <Crypto99> 25% gain on a $8k coin, is crazy
(edited)
[dscrd-hush] <Crypto99> 30%
[dscrd-hush] <Woody The Flipping Woodpecker> I went up 2$ total today 1$ this morning and this evening again
[dscrd-hush] <Crypto99> Its $9.7k
[dscrd-hush] <Crypto99> $400+ in less than an hour
[dscrd-hush] <Woody The Flipping Woodpecker> Yeah holidays are comming people go nuts you know
[dscrd-hush] <Crypto99> !ticker hush
[dscrd-hush] <Woody The Flipping Woodpecker> My husband with drew before the pump
[dscrd-hush] <Crypto99>
[dscrd-hush] <Crypto99> Nobody can always be right
[dscrd-hush] <Woody The Flipping Woodpecker> He would have had and extra 5$ if he stayed in
[dscrd-hush] <Crypto99> Just let it ride
(edited)
[dscrd-hush] <Crypto99> I think Hush will make significant gains
[dscrd-hush] <Woody The Flipping Woodpecker> He took it out to go get his lab top. 3D dragon
[dscrd-hush] <Crypto99> Noice
[dscrd-hush] <Crypto99> Hush is 422 Satoshis right now
[dscrd-hush] <Crypto99> Up 25% over 7D
[dscrd-hush] <RunningMantheCoin> Hahahahaha 10k
[dscrd-hush] <Woody The Flipping Woodpecker> Yeah. I need to let him do his work he took on some work but he will get some nice pays for it. So I will let him do what he has to do then. I will borrow for the dragon hope it works
[dscrd-hush] <RunningMantheCoin> What is life
[dscrd-hush] <RunningMantheCoin> IT JUST WENT UP $300 in 2 minutes
[dscrd-hush] <Woody The Flipping Woodpecker> Yeah damn straight it's still pumping
[dscrd-hush] <1112> Promoter? When i get a million hush, i will rent a helicopter to drop millions business cards on Manhattan that say, "Buy HUSH"
[dscrd-hush] <Crypto99> Holy shiiite
[dscrd-hush] <Crypto99> $10k
[dscrd-hush] <Crypto99> $700+ in less than an hour
[dscrd-hush] <RunningMantheCoin> That was wild. Sideways for a while now. Minor break from the madness
[dscrd-hush] <Crypto99> Did someone say, Tether..
[dscrd-hush] <RunningMantheCoin> Bakkt said they hit record volumes today
[dscrd-hush] <Crypto99> What does it take to move a 167B Market Cap coin, 37% in one day?
[dscrd-hush] <RunningMantheCoin> It’s possible some larger players bought in
[dscrd-hush] <Crypto99> Percentage gain wise, it might be a daily record
[dscrd-hush] <RunningMantheCoin> @Crypto99 I wonder as well. I think the liquidity pool is probably 1/100 the size of the market. So a couple billion could make this move possible
[dscrd-hush] <1112> If people who own BTC just refuse to sell, forced buying from liquidations escalates the price. However, there are over 10k bitcoin wallets with more than 100k USD worth of BTC in them, so there's always someone looking to sell to buy a depreciating asset like a lambo.
[dscrd-hush] <Crypto99> 100k USD of BTC, is not much at all
[dscrd-hush] <Crypto99> 10 BTC
[dscrd-hush] <1112> In the USA, half of all adults can't come up with $500 cash for an emergency. So, it's a decent amount for spending money.
[dscrd-hush] <Crypto99> I saw some good research about how the top whales wallets, have increased so many percentage points at the same time, over several years
[dscrd-hush] <Crypto99> I didnt mean $100k is a small amount of money.
[dscrd-hush] <Crypto99> I just meant that 10 BTC is not even moving most ALTS very much
[dscrd-hush] <1112> Just bringing it up because someone with 10 million USD in BTC probably doesn't need to sell any to get cash. Someone with 100k in BTC might feel rich enough to sell and spend and provide liquidity to the market
[dscrd-hush] <Crypto99> That's true
[dscrd-hush] <Crypto99> Would if a few whales decided to dump and then pump, every quarter or twice a year, to cover all of their living costs and more? Lol
[dscrd-hush] <AkiraX> Bitcoin 10k
[dscrd-hush] <Crypto99> I'll put this a certain way.
Tether prints tethers.
How do those get in the market?
[dscrd-hush] <AkiraX> Jeez
[dscrd-hush] <Crypto99> I dont go and mint my own tether
[dscrd-hush] <AkiraX> Bitcoin doninance
[dscrd-hush] <1112> In my opinion, the people at large funds are behind on technology. They will be left holding the BTC bag when the world moves on to better technology. Not to wish any of them ill; a strong bitcoin ensures lots of hashing power to secure dPoW. But it's just a first draft of crypto.
[dscrd-hush] <Crypto99> If it were me, I'd have sold at 10k
[dscrd-hush] <1112> I would have sold at $30 when it tripled from $10! lol
[dscrd-hush] <Crypto99> $10,140 lol
[dscrd-hush] <Crypto99> 10,170
[dscrd-hush] <Crypto99> This has to be tether pumping this
[dscrd-hush] <Crypto99> @1112 Who gets to run the second draft and why?
[dscrd-hush] <Crypto99> Up 38% lol...nuts
[dscrd-hush] <1112> Everyone votes for the second draft with their own money by buying what they thing will succeed. So opinions vary.
[dscrd-hush] <Crypto99> When you have a bunch of buyers that dont understand what is good technology, and what isnt, then you might get XRP Ripple
[dscrd-hush] <1112> Thanks to XRP, a pairs trade of Short XRP, long BTC helps redistribute the wealth away from the uninformed haha
[dscrd-hush] <Crypto99> You short XRP?
[dscrd-hush] <Crypto99> And long BTC?
[dscrd-hush] <1112> No way. I gotta bet big on ARRR or something small. I can't compete.
[dscrd-hush] <Crypto99> If I'm trading, I just find something that moves within a good range consistently, and make gainz
[dscrd-hush] <1112> Trading stock futures is a lot better because the fed just prints more money or repo when they go down.
[dscrd-hush] <Crypto99> 6-8 sat gains on TRX, or something
[dscrd-hush] <Crypto99> Hold Hush at 400 sats, sell some at 700
[dscrd-hush] <Crypto99> Bought SNX Synthetix at 4 cents, sold at 24 cents
[dscrd-hush] <1112> Haha with small cap I'd say you can only buy, never sell. not enough volume. But let's put it this way. If i put 10k into HUSH and it went down 90%, i'd still have more left over than if i invested it in stocks.
[dscrd-hush] <Crypto99> It's now 88 cents
[dscrd-hush] <Crypto99> I bought at 4 in Feb or Mar, and could've sold at 88 cents
[dscrd-hush] <Crypto99> A 20x
[dscrd-hush] <Crypto99> Synthetix volume has been low like a small cap
[dscrd-hush] <Crypto99> If you invest without a plan, it usually doesnt end up as well as you'd like
[dscrd-hush] <Crypto99> One question I used when trading was, is this less likely or more likely to happen and, does the project have more upside? Not potential, but active plans and active work to achieve those
[dscrd-hush] <1112> Yes. Looking at how bitcoin turned out, now that i can see that a good idea can go 1000x, all the money i don't need i put into something that can realistically go 100x
BTC was pretty shitty. back then, you had to specify the correct change address for your utxo. nothing about it was easy.
[dscrd-hush] <1112> But anyone who saw past that and held got rewarded.
[dscrd-hush] <1112> if somethign shitty like XVG or DASH can get to a billion, a good coin like ARRR or KMD should easily do 10x if not 100x. Even if nobody sees it yet.
[dscrd-hush] <Crypto99> I got my first BTC in Nov/Dec 2010
[dscrd-hush] <Crypto99> 2011 sorry
[dscrd-hush] <Crypto99> No it was 2010
[dscrd-hush] <Crypto99> But that's history
[dscrd-hush] <1112> Haha. I read about BTC in 2009 and i just assumed everyone was smart and knew about it and it was just shitty. It was frontpage of slashdot, which was like reddit for tech stuff. Now i assume almost everyone knows nothing and i gotta do my own thing.
[dscrd-hush] <Crypto99> Yeah I heard people in privacy circles in 2009 begin to talk about it
[dscrd-hush] <Crypto99> You know, a digital financial revolution is coming


Some quick example text to build on the card title and make up the bulk of the card's content.
Add To CartSome quick example text to build on the card title and make up the bulk of the card's content.
Add To CartSome quick example text to build on the card title and make up the bulk of the card's content.
Add To Cart


Though the port is non-standard, you manage to connect it to your computer through the clever use of several paperclips. Upon connection, the port outputs a series of numbers (your puzzle input).
The data appears to be encrypted with the eXchange-Masking Addition System (XMAS) which, conveniently for you, is an old cypher with an important weakness.
XMAS starts by transmitting a preamble of 25 numbers. After that, each number you receive should be the sum of any two of the 25 immediately previous numbers. The two numbers will have different values, and there might be more than one such pair.
For example, suppose your preamble consists of the numbers 1
through 25
in a random order. To be valid, the next number must be the sum of two of those numbers:
26
would be a valid next number, as it could be1
plus25
(or many other pairs, like2
and24
).49
would be a valid next number, as it is the sum of24
and25
.100
would not be valid; no two of the previous 25 numbers sum to100
.50
would also not be valid; although25
appears in the previous 25 numbers, the two numbers in the pair must be different.
Suppose the 26th number is 45
, and the first number (no longer an option, as it is more than 25 numbers ago) was 20
. Now, for the next number to be valid, there needs to be some pair of numbers among 1
-19
, 21
-25
, or 45
that add up to it:
26
would still be a valid next number, as1
and25
are still within the previous 25 numbers.65
would not be valid, as no two of the available numbers sum to it.64
and66
would both be valid, as they are the result of19+45
and21+45
respectively.
Here is a larger example which only considers the previous 5 numbers (and has a preamble of length 5):
35
20
15
25
47
40
62
55
65
95
102
117
150
182
127
219
299
277
309
576
In this example, after the 5-number preamble, almost every number is the sum of two of the previous 5 numbers; the only number that does not follow this rule is 127
.
The first step of attacking the weakness in the XMAS data is to find the first number in the list (after the preamble) which is not the sum of two of the 25 numbers before it. What is the first number that does not have this property?


This tool displays the complete dependencies of these initial packages:
...
Click on a package in the tree view to display information about it, including a path by which it is reached from one of the initial packages. Use the break button to remove an edge from the graph, so that you can assess what edges need to be broken to eliminate an unwanted dependency.
ⓘThis list shows the packages directly imported by the selected package
ⓘThis list shows the packages that directly import the selected package
ⓘ
This section displays an arbitrary path from one of the
initial packages to the selected package. Click
the break button so see how your dependencies would
change if you were to remove a single edge.
Click break all to remove all inbound edges to a
package, removing it from the graph. This may be useful for
removing distracting packages that you don't plan to
eliminate.
The bold nodes are dominators: nodes that are found on
every path to the selected node. One way to break a dependency
on a package is to break all dependencies on any of its dominators.


Matplotlib is a simple Python library to create plots like this one:
As I always have to look up different styles of markers / lines, here is a little summary.
Simple plots
Here is some sample code:
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Visualize matplotlib marker styles."""
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.lines import Line2D
# Get colors / markers / functions
colors = ("b", "g", "r", "c", "m", "y", "k")
markers = []
for m in Line2D.markers:
try:
if len(m) == 1 and m != " ":
markers.append(m)
except TypeError:
pass
f1 = lambda xs: [x ** 2 for x in xs]
f2 = lambda xs: [x ** 3 for x in xs]
f3 = lambda xs: np.sin(xs)
f4 = lambda xs: np.log(xs)
f5 = lambda xs: [x ** 0.5 for x in xs]
f6 = lambda xs: [x for x in xs]
f7 = lambda xs: [x + 1 for x in xs]
functions = [f1, f2, f3, f4, f5, f6, f7]
# Define the plot
plt.ylim(-1.0, 1.0)
plt.title("Example for matplotlib markers", fontweight="bold", fontsize=20)
plt.xlabel(r"""x-axis label""", fontsize=20)
plt.ylabel(r"""y-axis label""", fontsize=20)
# Define at which x positions to evaluate the functions f_i(x)
xmin = -1
xmax = 1
samples = 50
xs = np.linspace(xmin, xmax, samples)
# Plot the functions
for color, marker, f in zip(colors, markers, functions):
format_str = "{color}{marker}-".format(color=color, marker=marker)
plt.plot(xs, f(xs), format_str, label=format_str)
plt.axhline(y=0.20)
plt.legend(fontsize=20)
plt.savefig("matplotlib-marker-styles.png") # or plt.show()
Markers
Basically, the matplotlib tries to have identifiers for the markers which look similar to the marker:
- Triangle-shaped:
v
,<
,>
,^
- Cross-like:
*
,+
,1
,2
,3
,4
- Circle-like:
o
,.
,h
,p
,H
,8


About 35 seconds after I posted this article, Lawrence Lomax pointed out that Mark Dalrymple had already published exhaustive details about NSString comparisons on the Big Nerd Ranch blog. Go read that and stop wasting your time over here!
Earlier tonight, I came across a snippet of Objective-C code where the author had used -compare:
instead of -isEqualToString:
in every instance. My general rule is to always use the highest-level, most readable API unless there is a critical performance
issue when doing so, so I was intrigued (although I was pretty sure why this was being done):
Here’s the simple test I ran:
NSString *someString = @"LANA!";
NSString *otherString = @"LAAANAA!";
NSString *sameString = @"LANA!";
NSTimeInterval start = [NSDate timeIntervalSinceReferenceDate];
BOOL boop = [someString isEqualToString:otherString];
NSLog(@"%f", [NSDate timeIntervalSinceReferenceDate] - start);
start = [NSDate timeIntervalSinceReferenceDate];
BOOL bleep = [someString compare:otherString] == NSOrderedSame;
NSLog(@"%f", [NSDate timeIntervalSinceReferenceDate] - start);
start = [NSDate timeIntervalSinceReferenceDate];
BOOL boopSame = [someString isEqualToString:sameString];
NSLog(@"%f", [NSDate timeIntervalSinceReferenceDate] - start);
start = [NSDate timeIntervalSinceReferenceDate];
BOOL bleepSame = [someString compare:sameString] == NSOrderedSame;
NSLog(@"%f", [NSDate timeIntervalSinceReferenceDate] - start);
| Method | Result | Time Logged (seconds) |
|---|---|---|
-isEqualToString: |
Strings did not match | 0.000014 |
-compare: |
Strings did not match | 0.000007 |
-isEqualToString: |
Strings matched | 0.000001 |
-compare: |
Strings matched | 0.000002 |
Turns out that -compare:
is roughly twice as fast as -isEqualToString:
when the strings don’t match and the inverse is true when the strings match — although with the matching strings the difference is very nearly negligible.
Personally, I’ll probably stick to -isEqualToString:
unless performance is absolutely critical — I find it much more readable, and that’s almost always more important than saving 0.000007 seconds.


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“Cutā patanti patitā,
giddhā ca punarāgatā;
Kataṁ kiccaṁ rataṁ rammaṁ,
sukhenanvāgataṁ sukhan”ti.
… Pakkho thero ….
They fall, collapsed and fallen;
greedy, they return.
The work is done, the joyful is enjoyed,
happiness is found through happiness.


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header
'Deus Ex Pandemus' is a Latin phrase which literally translates to 'God in a Pandemic'.
This is a play on the famous phrase Deus Ex Machina and is meant to express
the possibility that this pandemic might actually be a blessing in disguise.
Through the media of theater, presentation, and panel discussions, this program
seeks to explore just what these blessings might be.


by Lars Lindahl, high school student © 1999 Lars Lindahl email<EMAIL_ADDRESS>"The Truman Show" Starring Jim Carrey, Ed Harris, Laura Linney, Natasha McElhone Directed by Peter Weir Written by Andrew Niccol Grade: A
Jim Carrey plays Truman Burbank, a man who lives in a perfect world with a perfect job and a perfect wife. Little does he know that his life is the center of a 24 hour widely popular television show known as The Truman Show. He is surrounded by actors in one huge dome studio and is constantly watched by a variety of cameras, which are hidden throughout the city.
"The Truman Show" is a creative and enjoyable movie which is wonderfully acted and directed. Jim Carrey, known mostly for his rubber faced gags and his talking rear end, takes a surprising and effective turn to drama as Truman, similar to how Robin Williams has succeeded in the last decade. Carrey is so believable and likeable that you feel shame for his character and cheer him on during the film. He also uses humor to his advantage, making The Truman Show even more entertaining.
Ed Harris gives one of his best performances, as well, as the creator of The Truman Show, who is extremely picky about using the right cameras, the right actors, and the right scenery. Harris adds a powerful god like appearance to the movie as his obsession towards the show and Truman's life for over thirty years makes him a creepy villain unlike the average "bad guy" of current movies.
Peter Weir masterfully directs The Truman Show using unordinary camera angles to emphasize that the cameras are watching Truman no matter where he goes. Weir also creates a magnificent set from an elaborate city to a sea that never seems to end. He cleverly displays advertisements in random places and errors of the show's production ( a studio light falls from the sky) reminding the viewer that it is live television and some faults will occur. Throughout the movie he asks the viewer whether the city should continue to entertain the world or if it should halt for the sake of one innocent, unknowing man. The Truman Show is not your ordinary movie. Its detailed 1984-ish plot, its powerful acting, and its phenomenal directing make it one of the best movies of the 1990's.
© 1999 Lars Lindahl
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


How the battle between environmentalists and farmers could decide the fate of water flowing through the Sacramento-San Joaquin river delta.
by Jeff Pawlak
One of the biggest things I learned when I co-founded Aqueduct is that water is a massive rabbit hole. Water is so complex and all-encompassing that you can spend all of your time researching this topic (which is basically what I do), and still feel like you’ve barely scratched the surface. Environment, economy, social justice, infrastructure, public health, geopolitics - water affects literally everything.
Even water issues in California alone are ridiculously challenging to untangle. With a web of regulation, groundwater rights, mega-farmers, and drought conditions, it is difficult to make sense of it all. And yet California’s ability to solve its water challenges will likely define how governments around the world similarly respond. California is ground zero for water.
Because I’m endlessly focused on water issues and water technology, I wanted to take the time to write a blog post about a major water controversy in California. I’m writing this for the purposes of my own research, but also to educate my fellow Californians about an issue that affects them.
Specifically, this post centers around a battle between environmentalists and farmers regarding the fate of water flowing through the Sacramento-San Joaquin river delta and lower San Joaquin river. For context, this is approximately fifty miles northeast of San Francisco.
Farmers in the San Joaquin valley, which includes Stockton, Modesto, and Merced, depend on massive diversions of water flows from this river through canals and dams to support farming industries. As much as 80% of river water is pulled out for agriculture.
While these diversions have supported Central Valley communities during significant droughts, the resulting lower river levels have threatened sensitive fish species. The current water diversions present a major threat to the Chinook salmon, declared a protected fish by NOAA. The California Department of Fish and Wildlife determined that 50-60% of water flows need to be retained in the river in order to protect the Chinook.
In response to the salmon crisis, the Water Resources Control Board recently approved a plan to reduce the amount of water removed from the river, increasing retention to 40% and river water by 747,000 acre-feet per year. In addition, the proposal raises $1.7 billion from water users and the state to support infrastructure and research to support fish habitats.
Farmers counter that reducing their supply of water will reduce their crop yields and damage their local economy. It is the classic debate of individual and collective economic interests versus the integrity of the commons. Furthermore, the river diversion debate symbolizes the coastal-rural tension of California politics; highly represented urban liberals versus disenfranchised inland conservatives (I’m generalizing, but it mostly accurate).
This is largely visible when you drive between San Francisco and Los Angeles down Route 5. Once you leave the progressive bubble of San Francisco - dotted by rainbow LBGT flags and Bernie or Hillary bumper stickers - the entire highway is filled with billboards protesting the state government’s “water grabs” or warning of an artificially created dust bowl (or during the 2016 election - Trump-Pence campaign signs). Reduced water diversions may in fact damage their livelihoods, and they are angry about it.
Assemblyman Adam Grey, representing Merced, chastised the proposal as “fish first philosophy” which will decimate the local region’s economy.
But many people strongly feel that fish species collapse is unacceptable, especially in a state like California. They argue that an entire ecosystem can be thrown out of balance without proper safeguards in place. They believe that regulators have responded accordingly to reduce diversions, even though the proposed 40% retention targets are below the 50% target that scientists recommended. They hope that these measures are enacted quickly to preserve the environment, and that ecosystem risks are mitigated.
I very much empathize with outcries of the farmers. They see themselves as American heroes - growing food for the entire nation. And statistically, this is entirely correct. California produces somewhere around 11.3% of agricultural revenue in the United States, or $44.7 billion. This is exported throughout the country and around the world (California essentially exports water when it sends crops like almonds or alfalfa to countries like China).
But we are reaching an environmental breaking point. Currently, there simply isn’t enough water to sustain both the local environment and California agricultural exports. California farmers consume around 80% of human water use. Something needs to give.
Farmers have a long history with droughts and water scarcity. They have always responded to water shortages with petitions for supply side engineering - building infrastructure projects to extract more water out of the environment. For example, California transports massive amounts of water from the Sierra Nevada mountains to Southern California (this channel is also visible along Route 5).
This has worked so far, up to a point. But realistically, there is a finite amount of water available in the environment, and continued extraction of surface and groundwater will create major environmental risks that all of society will have to deal with. In other words, by addressing only the farmer’s needs, at the expense of other legitimate uses, we might engineer our way into creating a desert for everyone. Supply side is ultimately an expensive and short-sighted approach.
The question is, how do we make this whole water system work for everyone? The answer is - of course - demand side engineering. Instead of trying to extract more water, we need to adopt technology that promotes conservation and reduces water waste. Even more, we need to promote widespread re-use that redirects highly treated wastewater to our agriculture.
Unacceptable levels of treated water leak out of California pipes every year ( known as non-revenue water) - as much as 10-25% annually. While the farmers and the environmentalists fight about the river water use, this is a problem that is rarely discussed. If we addressed our leak issues, there would be more water for all uses.
Farmers can implement better technology to increase crop yields while reducing water consumption. Application of IoT sensors could capture massive amounts of data regarding crop performance, and subsequent machine learning algorithms could help optimize the irrigation to yield ratio, and help farmers develop novel solutions to an age-old problem.
Facing prolonged droughts, which will only be exacerbated by climate change, farmers need better intelligence about the dynamics of their industry. Predictive analytics can help farmers determine the crops that will be most profitable, and the most likely to survive drought periods. Perhaps I’m being optimistic, but I’d love to see a day when farmers opt out of growing water intensive crops and switch to more sustainable ones.
There are a million other potential solutions to the California water scarcity issues, but they all come down to the same thing - decreasing water consumption and waste while increasing economic productivity. Without increases in efficiency, California farmers and environmentalists will continue to face off in a cage match, with zero sum stakes. But both sides need to commit to creating an abundance water economy - investing in long-term demand-side solutions that ensure that there is more than enough water for all citizens and biota.
The proposal from the Water Resources Control Board may have addressed concerns of environmentalists, but it left farmers unhappy. We cannot simply regulate our way out of a water crisis. California’s water situation demands technological innovation that makes life possible for both the farmers and the fish.


--- layout: post title: Test Slideshow categories: [] tags: [] status: publish type: post published: true meta: _original_post_id: '' _original_import_author: John Duprey author: ---
http://www.slide.com/r/VqBc99u74z8LjJCIFJbBqOPHHDQQ93oy?previous_view=mscd_embedded_url&view=original


Little Goblin Plugin - Reference Documentation
Authors:
Version: 0.3.2
Little Goblin is a browser game framework written in Grails.
|
(Quick Reference)
Little Goblin Plugin - Reference DocumentationAuthors: Version: 0.3.2 Table of Contents |


This post walks through the parallel computing functionality of Julia to implement an asynchronous parallel version of the classical cutting-plane algorithm for convex (nonsmooth) optimization, demonstrating the complete workflow including running on both Amazon EC2 and a large multicore server. I will quickly review the cutting-plane algorithm and will be focusing primarily on parallel computation patterns, so don't worry if you're not familiar with the optimization side of things.
The cutting-plane algorithm is a method for solving the optimization problem
\[\min_{x \in \mathbb R^d} \sum_{i=1}^n f_i(x)\]where the functions \(( f_i \)) are convex but not necessarily differentiable.  Important applications also arise from Lagrangian relaxation. The idea of the algorithm is to approximate the functions \((f_i \)) with piecewise linear models \(( m_i \)) which are built up from information obtained by evaluating \(( f_i \)) at different points. We iteratively minimize over the models to generate candidate solution points.
We can state the algorithm as
Choose starting point \(( x \)).
For \((i = 1,\ldots,n\)), evaluate \((f_i(x) \)) and update corresponding model \(( m_i \)).
Let the next candidate \(( x \)) be the minimizer of \(( \sum_{i=1}^n m_i(x) \)).
If not converged, goto step 2.
If it is costly to evaluate \(( f_i(x) \)), then the algorithm is naturally parallelizable at step 2. The minimization in step 3 can be computed by solving a linear optimization problem, which is usually very fast. (Let me point out here that Julia has interfaces to linear programming and other optimization solvers under JuliaOpt.
Abstracting the math, we can write the algorithm using the following Julia code.
# functions initialize, isconverged, solvesubproblem, and process implemented elsewhere
state, subproblems = initialize()
while !isconverged(state)
results = map(solvesubproblem,subproblems)
state, subproblems = process(state, results)
end
The function solvesubproblem
corresponds to evaluating \((f_i(x)\)) for a given \(( i \)) and \(( x \)) (the elements of subproblems
could be tuples (i,x)
). The function process
corresponds to minimizing the model in step 3, and it produces a new state and a new set of subproblems to solve.
Note that the algorithm looks much like a map-reduce that would be easy to parallelize using many existing frameworks. Indeed, in Julia we can simply replace map
with pmap
(parallel map). Let's consider a twist that makes the parallelism not so straightforward.
Variability in the time taken by the solvesubproblem
function can lead to load imbalance and limit parallel efficiency as workers sit idle waiting for new tasks. Such variability arises naturally if solvesubproblem
itself requires solving a optimization problem, or if the workers and network are shared, as is often the case with cloud computing.
We can consider a new variant of the cutting-plane algorithm to address this issue. The key point is
When proportion \((0 < \alpha \le 1 \)) of subproblems for a given candidate have been solved, generate a new candidate and corresponding set of subproblems by using whatever information is presently available.
In other words, we generate new tasks to feed to workers without needing to wait for all current tasks to complete, making the algorithm asynchronous. The algorithm remains convergent, although the total number of iterations may increase. For more details, see this paper by Jeff Linderoth and Stephen Wright.
By introducing asynchronicity we can no longer use a nice black-box pmap
function and have to dig deeper into the parallel implementation. Fortunately, this is easy to do in Julia.
Julia implements distributed-memory parallelism based on one-sided message passing, where process push work onto others (via remotecall
) and the results are retrieved (via fetch
) by the process which requires them. Macros such as @spawn
and @parallel
provide pretty syntax around this low-level functionality. This model of parallelism is very different from the typical SIMD style of MPI. Both approaches are useful in different contexts, and I expect an MPI wrapper for Julia will appear in the future (see also here).
Reading the manual on parallel computing is highly recommended, and I won't try to reproduce it in this post. Instead, we'll dig into and extend one of the examples it presents.
The implementation of pmap
in Julia is
function pmap(f, lst)
np = nprocs() # determine the number of processors available
n = length(lst)
results = cell(n)
i = 1
# function to produce the next work item from the queue.
# in this case it's just an index.
next_idx() = (idx=i; i+=1; idx)
@sync begin
for p=1:np
if p != myid() || np == 1
@spawnlocal begin
while true
idx = next_idx()
if idx > n
break
end
results[idx] = remotecall_fetch(p, f, lst[idx])
end
end
end
end
end
results
end
On first sight, this code is not particularly intuitive. The @spawnlocal
macro creates a task on the master process (e.g. process 1). Each task feeds work to a corresponding worker; the call remotecall_fetch(p, f, lst[idx])
function calls f
on process p
and returns the result when finished. Tasks are uninterruptable and only surrender control at specific points such as remotecall_fetch
. Tasks cannot directly modify variables from the enclosing scope, but the same effect can be achieved by using the next_idx
function to access and mutate i
. The task idiom functions in place of using a loop to poll for results from each worker process.
Implementing our asynchronous algorithm is not much more than a modification of the above code:
# given constants n and 0 < alpha <= 1
# functions initialize and solvesubproblem defined elsewhere
np = nprocs()
state, subproblems = initialize()
converged = false
isconverged() = converged
function updatemodel(mysubproblem, result)
# store result
...
# decide whether to generate new subproblems
state.numback[mysubproblem.parent] += 1
if state.numback[mysubproblem.parent] >= alpha*n && !state.didtrigger[mysubproblem.parent]
state.didtrigger[mysubproblem.parent] = true
# generate newsubproblems by solving linear optimization problem
...
if ... # convergence test
converged = true
else
append!(subproblems, newsubproblems)
push!(state.didtrigger, false)
push!(state.numback, 0)
# ensure that for s in newsubproblems, s.parent == length(state.numback)
end
end
end
@sync begin
for p=1:np
if p != myid() || np == 1
@spawnlocal begin
while !isconverged()
if length(subproblems) == 0
# no more subproblems but haven't converged yet
yield()
continue
end
mysubproblem = shift!(subproblems) # pop subproblem from queue
result = remotecall_fetch(p, solvesubproblem, mysubproblem)
updatemodel(mysubproblem, result)
end
end
end
end
end
where state
is an instance of a type defined as
type State
didtrigger::Vector{Bool}
numback::Vector{Int}
...
end
There is little difference in the structure of the code inside the @sync
blocks, and the asynchronous logic is encapsulated in the local updatemodel
function which conditionally generates new subproblems. A strength of Julia is that functions like pmap
are implemented in Julia itself, so that it is particularly straightforward to make modifications like this.
Now for the fun part. The complete cutting-plane algorithm (along with additional variants) is implemented in JuliaBenders. The code is specialized for stochastic programming where the cutting-plane algorithm is known as the L-shaped method or Benders decomposition and is used to decompose the solution of large linear optimization problems. Here, solvesubproblem
entails solving a relatively small linear optimization problem. Test instances are taken from the previously mentioned paper.
We'll first run on a large multicore server. The runals.jl
(asynchronous L-shaped) file contains the algorithm we'll use. Its usage is
julia runals.jl [data source] [num subproblems] [async param] [block size]
where [num subproblems]
is the \((n\)) as above and [async param]
is the proportion \((\alpha\)). By setting \((\alpha = 1\)) we obtain the synchronous algorithm. For the asynchronous version we will take \((\alpha =
0.6\)). The [block size]
parameter controls how many subproblems are sent to a worker at once (in the previous code, this value was always 1). We will use 4000 subproblems in our experiments.
To run multiple Julia processes on a shared-memory machine, we pass the -p N
option to the julia
executable, which will start up N
system processes. To execute the asynchronous version with 10 workers, we run
julia -p 12 runals.jl Data/storm 4000 0.6 30
Note that we start 12 processes. These are the 10 workers, the master (which distributes tasks), and another process to perform the master's computations (an additional refinement which was not described above). Results from various runs are presented in the table below.
| Synchronous | Asynchronous | |||
| No. Workers | Speed | Efficiency | Speed | Efficiency |
| 10 | 154 | Baseline | 166 | Baseline |
| 20 | 309 | 100.3% | 348 | 105% |
| 40 | 517 | 84% | 654 | 98% |
| 60 | 674 | 73% | 918 | 92% |
There are a few more hoops to jump through in order to run on EC2. First we must build a system image (AMI) with Julia installed. Julia connects to workers over ssh, so I found it useful to put my EC2 ssh key on the AMI and also set StrictHostKeyChecking no
in /etc/ssh/ssh_config
to disable the authenticity prompt when connecting to new workers. Someone will likely correct me on if this is the right approach.
Assuming we have an AMI in place, we can fire up the instances. I used an m3.xlarge instance for the master and m1.medium instances for the workers. (Note: you can save a lot of money by using the spot market.)
To add remote workers on startup, Julia accepts a file with a list of host names through the --machinefile
option. We can generate this easily enough by using the EC2 API Tools (Ubuntu package ec2-api-tools
) with the command
ec2-describe-instances | grep running | awk '{ print $5; }' > mfile
On the master instance we can then run
julia --machinefile mfile runatr.jl Data/storm 4000 0.6 30
Results from various runs are presented in the table below.
| Synchronous | Asynchronous | |||
| No. Workers | Speed | Efficiency | Speed | Efficiency |
| 10 | 149 | Baseline | 151 | Baseline |
| 20 | 289 | 97% | 301 | 99.7% |
| 40 | 532 | 89% | 602 | 99.5% |
On both architectures the asynchronous version solves subproblems at a higher rate and has significantly better parallel efficiency. Scaling is better on EC2 than on the shared-memory server likely because the subproblem calculation is memory bound, and so performance is better on the distributed-memory architecture. Anyway, with Julia we can easily experiment on both.
A more detailed tutorial was prepared for the Julia IAP session at MIT in January 2013.


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Missing activist found dead in Mexico’s troubled Guerrero
by The Associated PressMEXICO CITY — A human rights activist has been found dead in southern Mexico more than a month after he disappeared.
Guerrero state Attorney General Jorge Zuriel de los Santos Barrila said Wednesday that authorities confirmed around midday the discovery of Arnulfo Cerón Soriano’s corpse.
Cerón disappeared Oct. 11 from the mountain city of Tlapan de Comonfort. His body was found along a road between the city and Igualita, about 12 miles (20 kilometres) away.
Zuriel said two people detained in recent weeks have been charged. He added that four others under arrest are being investigated as suspects.
Cerón led the Mountain Popular Front. The group says via Facebook that he will be missed and may he “rest in peace.” It previously said he had been the victim of a “forced disappearance.”
The Associated Press


function GetStippleColor: TColor32;
Returns color corresponding to the current counter position in stipple pattern. If the counter step is fractional, returned color will be interpolated. Calling GetStippleColor automatically changes the StippleCounter value by StippleStep.


| Ruby Enumerables | ||
|---|---|---|
| Method | Example | Description |
| all? |
=> true |
Passes each element of the collection to the given block. The method returns true if the block never returns false or nil. If the block is not given, Ruby adds an implicit block of { |obj| obj } which will cause all? to return true when none of the collection members are false or nil. |
| any? |
=> true |
Passes each element of the collection to the given block. The method returns true if the block ever returns a value other than false or nil. If the block is not given, Ruby adds an implicit block of { |obj| obj } that will cause any? to return true if at least one of the collection members is not false or nil. |
| count |
=> 4 |
Returns the number of items in enum through enumeration. If an argument is given, the number of items in enum that are equal to item are counted. If a block is given, it counts the number of elements yielding a true value. |
| cycle |
print, a, b, c, a, b, c. |
Calls block for each element of enum repeatedly n times or forever if none or nil is given. If a non-positive number is given or the collection is empty, does nothing. Returns nil if the loop has finished without getting interrupted. |
| find |
=> 35 |
Passes each entry in enum to block. Returns the first for which block is not false. If no object matches, calls ifnone and returns its result when it is specified, or returns nil otherwise. |
| drop |
=> [4, 5, 0] |
Drops first n elements from enum, and returns rest elements in an array. |
| each_slice |
=> |
Iterates the given block for each slice of |
| each_with_index |
=> {"cat"=>0, "dog"=>1, "wombat"=>2} |
Calls block with two arguments, the item and its index, for each item in enum. Given arguments are passed through to each(). |
| find_all |
=> [3, 6, 9] |
Returns an array containing all elements of enum for which the given block returns a true value. |
| find_index |
=> 34 |
Compares each entry in enum with value or passes to block. Returns the index for the first for which the evaluated value is non-false. If no object matches, returns nil. |
| first |
=> "foo"
=> ["foo", "bar"] |
Returns the first element, or the first n elements, of the enumerable. If the enumerable is empty, the first form returns nil, and the second form returns an empty array. |
| group_by |
=>{0=>[3, 6], 1=>[1, 4], 2=>[2, 5]} |
Groups the collection by result of the block. Returns a hash where the keys are the evaluated result from the block and the values are arrays of elements in the collection that correspond to the key. |
| inject/reduce |
=> 45
=> 45 |
Combines all elements of enum by applying a binary operation, specified by a block or a symbol that names a method or operator. |
| map |
=> [1, 4, 9, 16] |
Returns a new array with the results of running block once for every element in enum. |
| max |
=> "horse"
=> "albatross" |
Returns the object in enum with the maximum value. The first form assumes all objects implement Comparable; the second uses the block to return a <=> b. |
| max_by |
=> "albatross" |
Returns the object in enum that gives the maximum value from the given block. |
| min |
=> "albatross"
=> "dog" |
Returns the object in enum with the minimum value. The first form assumes all objects implement Comparable; the second uses the block to return a <=> b. |
| min_by |
=> "dog" |
Returns the object in enum that gives the minimum value from the given block. |
| minmax |
=> ["albatross", "horse"] |
Returns two elements array which contains the minimum and the maximum value in the enumerable. The first form assumes all objects implement Comparable; the second uses the block to return a <=> b. |
| minmax_by |
=> ["dog", "albatross"] |
Returns a two element array containing the objects in enum that correspond to the minimum and maximum values respectively from the given block. |
| none? |
=>true |
Passes each element of the collection to the given block. The method returns true if the block never returns true for all elements. If the block is not given, none? will return true only if none of the collection members is true. |
| one? |
=>true |
Passes each element of the collection to the given block. The method returns true if the block returns true exactly once. If the block is not given, one? will return true only if exactly one of the collection members is true. |
| partition |
=>[[2, 4, 6], [1, 3, 5]] |
Returns two arrays, the first containing the elements of enum for which the block evaluates to true, the second containing the rest. |
| reject |
=> [1, 3, 5] |
Returns an array for all elements of enum for which the given block returns false. |
| reverse_each |
=> |
Builds a temporary array and traverses that array in reverse order. |
| select |
=>[2, 4] |
Returns an array containing all elements of enum for which the given block returns a true value. |
| sort |
=> ["flea", "kea", "rhea"] |
Returns an array containing the items in enum sorted, either according to their own <=> method, or by using the results of the supplied block. The block should return -1, 0, or +1 depending on the comparison between a and b. As of Ruby 1.8, the method Enumerable#sort_by implements a built-in Schwartzian Transform, useful when key computation or comparison is expensive. |
| sort_by |
=> ["fig", "pear", "apple"] |
Sorts enum using a set of keys generated by mapping the values in enum through the given block. |
| take |
=> [1, 2, 3] |
Returns first n elements from enum. |
| take_while |
=> [1, 2] |
Passes elements to the block until the block returns nil or false, then stops iterating and returns an array of all prior elements. |
| to_a |
=> [1, 2, 3, 4, 5, 6, 7]
=> [["a", 1], ["b", 2], ["c", 3]] |
Returns an array containing the items in enum. |
| to_h |
=> {:hello => 0, :world => 1} |
Returns the result of interpreting enum as a list of [key, value] pairs. |
| zip |
=> [[4, 7], [5, 8], [6, 9]] |
Takes one element from enum and merges corresponding elements from each args. This generates a sequence of n-element arrays, where n is one more than the count of arguments. The length of the resulting sequence will be enum#size. If the size of any argument is less than enum#size, nil values are supplied. If a block is given, it is invoked for each output array, otherwise an array of arrays is returned. |


| UriReference | → | (AbsoluteUri | RelativeUri)? ('#' Fragment)? |
| AbsoluteUri | → | Scheme ':' (HierPart | OpaquePart) |
| RelativeUri | → | empty (NetPath | AbsPath | RelPath) ('?' Query)? |
| HierPart | → | (NetPath | AbsPath) ('?' Query)? |
| OpaquePart | → | UriCNoSlash UriC* |
| UriCNoSlash | → | Unreserved | Escaped | [;?:@&=+$,] |
| NetPath | → | "//" Authority (AbsPath)? |
| AbsPath | → | '/' PathSegments |
| RelPath | → | RelSegment AbsPath? |
| RelSegment | → | (Unreserved | Escaped | [;@&=+$,])+ |
| Scheme | → | Alpha (Alpha | Digit | [+.-])* |
| Authority | → | Server | RegName |
| RegName | → | (Unreserved | Escaped | [$,;:@&=+])+ |
| Server | → | (UserInfo '@')? HostPort? |
| UserInfo | → | (Unreserved | Escaped | [;:&=+$,])* |
| HostPort | → | Host (':' Port)? |
| Host | → | HostName | IPv4Address |
| HostName | → | (DomainLabel '.')* (TopLabel '.'? | empty) |
| DomainLabel | → | (AlphaNumOrDash+) |
| TopLabel | → | Alpha (AlphaNumOrDash+) | Alpha |
| IPv4Address | → | Digit+ '.' Digit+ '.' Digit+ '.' Digit+ |
| Port | → | (uint)? |
| Path | → | (AbsPath | OpaquePart)? |
| PathSegments | → | Segment ('/' Segment)* |
| Segment | → | PChar* (';' Param)* |
| Param | → | PChar* |
| PChar | → | Unreserved | Escaped | [:@&=+$,] |
| Query | → | UriC* |
| Fragment | → | UriC* |
| UriC | → | Reserved | Unreserved | Escaped |
| Reserved | → | [;/?:@&=+$,] |
| Unreserved | → | AlphaNum | Mark |
| Mark | → | [-_.!~*'()] |
| Escaped | → | '%' Hex Hex |
| Hex | → | Digit | [a-fA-F] |
| AlphaNum | → | Alpha | Digit |
| AlphaNumOrDash | → | AlphaNum | '-' |
| Alpha | → | LowerAlpha | UpperAlpha |
| UpperAlpha | → | [A-Z] |
| LowerAlpha | → | [a-z] |
| Digit | → | [0-9] |


**Expressive Vector Engine** Numeric Limits (insert ../../crumbs.html here) Those constants provides generator function for limiting values constants. Synopsis ==================================================================================================== All constant are implemented as callable object taking a [type specifier](../types/helpers.html#eve::as_) as argument. Each constant is available by including the header file `eve/constant/name.hpp`, where `name` is the constant name. **Example:** **Output:** List of constants ==================================================================================================== $$\newcommand{\choose}[4]{\left\{ \begin{array}{@{}ll@{}} #1 & \texttt{if E is } \texttt{#2}\ \\ #3 & \texttt{if E is } \texttt{#4} \end{array}\right.}$$ $$\newcommand{\choosefd}[2]{\left\{ \begin{array}{@{}ll@{}} #1 & \texttt{if E is float } \\ #2 & \texttt{if E is double} \end{array}\right.}$$ $$\newcommand{\tchoosefd}[2]{ \choosefd{\texttt{#1}}{\texttt{#2}}}$$ $$\newcommand{\choosefdi}[3]{\left\{ \begin{array}{@{}ll@{}} #1 & \texttt{if E is float } \\ #2 & \texttt{if E is double} \\ #3 & \texttt{if E is integral} \end{array}\right.}$$ In the following table: * `T` represents the requested type for the generated constant. * `E` represents the [element type](../../glossary.html#elementtype#elementtype) associated to `T` Description | Callable object | Value | Constraints on `T` | :-----------------------------------------------|:-----------------------|:-----------------|:-------------------| Infinity | `eve::inf` | $$\infty$$ | `floating_value` | Minus infinity | `eve::minf` | $$-\infty$$ | `floating_value` | Greatest value | `eve::valmax` | $$\tchoosefd{3.40282e+38f}{1.79769313486232e+308}$$ | `value` | Least value | `eve::valmin` | $$\tchoosefd{-3.40282e+38f}{-1.79769313486232e+308}$$ | `value` | Greatest contiguous integral floating value | `eve::maxflint` | $$\tchoosefd{16777216.0f}{9007199254740992.0}$$ | `floating_value` | Smallest strictly positive normal value | `eve::smallestposval` | $$\tchoosefd{1.17549e-38f}{2.2250738585072e-308}$$ | `floating_value` | Square root of greatest value | `eve::sqrtvalmax` | $$\tchoosefd{1.844674e+19f}{1.340780792994260e+154}$$ | `floating_value` | ----------------------------------------------------------------------------------------------------


Python is a general-purpose interpreted, interactive, object-oriented, and high-level programming language. It was created by Guido van Rossum during 1985- 1990. Like Perl, Python source code is also available under the GNU General Public License (GPL)
Python is a high-level, interpreted, interactive and object-oriented scripting language. Python is designed to be highly readable. It uses English keywords frequently where as other languages use punctuation, and it has fewer syntactical constructions than other languages.
Python is a MUST for students and working professionals to become a great Software Engineer specially when they are working in Web Development Domain. I will list down some of the key advantages of learning Python:
- Python is Interpreted − Python is processed at runtime by the interpreter. You do not need to compile your program before executing it. This is similar to PERL and PHP.
- Python is Interactive − You can actually sit at a Python prompt and interact with the interpreter directly to write your programs.
- Python is Object-Oriented − Python supports Object-Oriented style or technique of programming that encapsulates code within objects.
- Python is a Beginner's Language − Python is a great language for the beginner-level programmers and supports the development of a wide range of applications from simple text processing to WWW browsers to games.
Following are important characteristics of Python Programming −
- It supports functional and structured programming methods as well as OOP.
- It can be used as a scripting language or can be compiled to byte-code for building large applications.
- It provides very high-level dynamic data types and supports dynamic type checking.
- It supports automatic garbage collection.
- It can be easily integrated with C, C++, COM, ActiveX, CORBA, and Java.
Just to give you a little excitement about Python, I'm going to give you a small conventional Python Hello World program
print ("Hello, Python!")
Variables are nothing but reserved memory locations to store values. This means that when you create a variable you reserve some space in memory.
Based on the data type of a variable, the interpreter allocates memory and decides what can be stored in the reserved memory. Therefore, by assigning different data types to variables, you can store integers, decimals or characters in these variables.
Python variables do not need explicit declaration to reserve memory space. The declaration happens automatically when you assign a value to a variable. The equal sign (=) is used to assign values to variables.
The operand to the left of the = operator is the name of the variable and the operand to the right of the = operator is the value stored in the variable. For example −
counter = 100
miles = 1000.0
name = "John"
print counter
print miles
print name
Here, 100, 1000.0 and "John" are the values assigned to counter, miles, and name variables, respectively. This produces the following result −
100
1000.0
John
The data stored in memory can be of many types. For example, a person's age is stored as a numeric value and his or her address is stored as alphanumeric characters. Python has various standard data types that are used to define the operations possible on them and the storage method for each of them.
- Numbers
- String
- List
- Tuple
- Dictionary
Python has five standard data types −
Decision making is anticipation of conditions occurring while execution of the program and specifying actions taken according to the conditions.
Decision structures evaluate multiple expressions which produce TRUE or FALSE as outcome. You need to determine which action to take and which statements to execute if outcome is TRUE or FALSE otherwise.
Following is the general form of a typical decision making structure found in most of the programming languages −
The if statement contains a logical expression using which data is compared and a decision is made based on the result of the comparison.
var1 = 100
if var1:
print "1 - Got a true expression value"
print var1
In general, statements are executed sequentially: The first statement in a function is executed first, followed by the second, and so on. There may be a situation when you need to execute a block of code several number of times.
Programming languages provide various control structures that allow for more complicated execution paths.
A while loop statement in Python programming language repeatedly executes a target statement as long as a given condition is true.
while expression:
statement(s)..
Example
count = 0
while (count < 9):
print 'The count is:', count
count = count + 1
print "Good bye!"
A function is a block of organized, reusable code that is used to perform a single, related action. Functions provide better modularity for your application and a high degree of code reusing.
As you already know, Python gives you many built-in functions like print(), etc. but you can also create your own functions. These functions are called user-defined functions.
Defining a Function
You can define functions to provide the required functionality. Here are simple rules to define a function in Python.
- Function blocks begin with the keyword def followed by the function name and parentheses ( ( ) ).
- Any input parameters or arguments should be placed within these parentheses. You can also define parameters inside these parentheses.
- The first statement of a function can be an optional statement - the documentation string of the function or docstring.
- he code block within every function starts with a colon (:) and is indented.
- The statement return [expression] exits a function, optionally passing back an expression to the caller. A return statement with no arguments is the same as return None.
Syntax
def functionname( parameters ):
"function_docstring"
function_suite
return [expression]
Example
def printme( str ):
"This prints a passed string into this function"
print str
return
All the Documentations on this page is taken from TutorialsPoint


Instructor's name: Eric Pogue
Lewis office location: AS-132-A
Office hours: MWF 1-2pm CT by appointment
Appointments: Appointments can be requested via email
Lewis Phone Number:<PHONE_NUMBER> (mobile)
Lewis email address<EMAIL_ADDRESS>Course: Software Engineering (CPSC-44000-LT1)
Course Credit Hours: 3
Course description:
Methods, strategies, and tools for implementing software systems, particularly as part
of a development team. Topics include the software development life cycle, Unified
Modeling Language, software testing techniques, software security, open-source
development, requirements gathering and documentation, maintenance, and basic
software project management.
Prerequisites: CPSC-24500 Object-Oriented Programming
Course meeting times: MWF 12-12:50am CT from August 31st through December 12th
Meeting location: AS 104A
Course final: Monday, December 14th from 1:30 to 3:30pm CT
Student Learning Outcomes:
Program student learning outcomes:
Baccalaureate Characteristics:
Lewis University, guided by its Catholic and Lasallian heritage, provides to a diverse
student population programs for a liberal and professional education grounded in the
interaction of knowledge and fidelity in the search for truth.
Lewis promotes the development of the complete person through the pursuit of wisdom
and justice. Fundamental to its Mission is a spirit of association, which fosters
community in all teaching, learning and service.
How this course connects to the University Mission:
This course will allow us to extend our knowledge in software development, provide us
the foundation for lifelong learning in this domain, and provide us the opportunity to
assist each other on our learning journey.
Textbook:
Fox and Patterson. Engineering Software as a Service: An Agile Approach
Using Cloud Computing. ISBN:<PHONE_NUMBER>246.
E-Learning Headphone with Microphone:
Our blended classroom setting
requires a wired computer headset with a microphone for both in person and
online participation.
Supplemental readings, videos, online materials:
Additional readings, videos, and online materials will be provided.
Hardware and software requirements:
This course requires access to multiple
software tools. There are generally available for free but will need to be installed and
utilized from a Windows or Macintosh computer.
Other required materials or costs:
During this course we will be setting up and utilizing
GitHub and Microsoft Azure accounts. We will be able to complete course assignments
utilizing these resources for free or at a very low cost (estimated less than $20). A credit
card will be needed to sign up for these accounts.
Modality of Instruction: This course will be online.
The course is broken into 8 modules (sprints) with each module being one week.
| Module | Topics | Textbook Readings |
|---|---|---|
| 1 |
Introduction to SaaS and Agile Development The Architecture of SaaS Applications Getting Started with Git & GitHub Cloud Computing with Microsoft Azure |
Chapter 1 Chapter 2 A.5 and A.6 |
| 2 |
Ruby Overview Getting Started with JavaScript Getting Started with Bootstrap Getting Started with Node.js |
Chapter 3 Chapter 4 Chapter 6 |
| 3 |
Behavior Driven Development Plan and Document Requirements Getting Started with Microsoft Azure and Node.js |
Chapter 7 |
| 4 |
Testing and Test-Driven Development HTML, CSS, JavaScript, and Bootstrap Azure, Node.js, JavaScript, and Express |
Chapter 8 |
| 5 |
Maintenance, and Metrics Agile Requirements & Saas Environment Class Project - Sprint 1: |
Chapter 9 |
| 6 |
Project Management & Scaled Agile Serverless Computing Class Project - Sprint 2 |
Chapter 10 |
| 7 |
Design Patterns Software Architecture Class Project - Sprint 3 |
Chapter 11 |
| 8 |
Performance, Releases, Reliability, and Security More Software Architecture Class Project - Sprint 4 |
Chapter 12 |
Schedule Changes Material changes to the course schedule will be communicated through course lecture and/or Blackboard announcements.
Assignments and Course Requirements
Assignments for this course will take the form of Programming Projects, Quizzes,
Discussion Board topics, a class Demonstration, and a Final Project.
Course Grade:
The course grade will be based on the following:
| Assignment | Pts | # | Total Pts | % of Grade |
|---|---|---|---|---|
| Lab | 70 | 8 | 560 | 56% |
| Quiz | 40 | 8 | 320 | 32% |
| Discussion | 8 | 8 | 64 | 6% |
| Reflection | 3 | 8 | 24 | 2% |
| Demo | 12 | 1 | 12 | 1% |
| Final Project Presentation | 20 | 1 | 20 | 2% |
| Totals: | 34 | 1000 | 100% |
Grading Policies:
No late assignments will be accepted. It is vastly preferable to
submit a partially complete assignment on time than to turn in a late assignment.
Similarly, it is vastly more beneficially to turn in a programming or lab assignment that
has 70% of the features fully implemented than to turn in an assignment that has 100%
of the features partially implemented.
Final course letter grade will be determined using the following scale:
Course Policies:
Class attendance is required unless otherwise specified. During our
class time we will remain focused on the topics at hand, avoid utilizing phones or email,
and be inclusive of our fellow classmates.
Changes to Course Assignments or Grades:
Changes to the course assignments or
grades will be communicated through course lecture and/or Blackboard announcements.
Responsiveness to Change
Understanding that the COVID-19 pandemic could influence the course of this
semester, Lewis University will be guided by our Lasallian mission and the wellbeing of our community of students, faculty, and staff in respond and adapting to
any sudden changes or circumstances. Based on the guidance of the State of
Illinois and the Centers for Disease Control, it may be necessary to change the
planned modality this course.
Flexibility, Accommodations, and Student Absences
Because we are committed to student success, the University community is
committed to academic standards while maintaining flexibility and empathy.
Absences relating to the Coronavirus crisis will be recognized as excused.
Students experiencing disruptions in their lives related to the Coronavirus that
impact class attendance and participation should contact their instructor and/or
college Dean’s Office for assistance. Students directly impacted by Coronavirus
will have the ability to request alternative grading this semester. Requests will be
evaluated on a case by case basis and will require documentation.
Students who require academic accommodations due to disability caused by
COVID-19, or to limit risk of exposure to Coronavirus, can engage in an
interactive process with the Learning Access Coordinator to explore all avenues
for accommodations. Students can contact the Academic Services office at 815-
836-5593 or<EMAIL_ADDRESS>to request an appointment.
Face Coverings, Physical Distancing, and Surface Cleaning
Face coverings are required in classrooms. Students will be expected to maintain
physical distancing in the classroom (6 ft minimum) and to keep their nose and
mouth covered at all times. Faculty will require students without face covering to
obtain a disposable mask at the nearest University office providing them. While
the University will disinfect classrooms and common spaces throughout the day,
cleaning supplies will be provided in classrooms and offices so that students and
faculty can wipe down work surfaces before class begins
Requests for Reasonable Accommodations
Lewis University is committed to providing equal access and opportunity for participation
in all programs, services and activities. If you are a student with a disability who would
like to request a reasonable accommodation, please speak with the Learning Access
Coordinator at the Center for Academic Success and Enrichment (CASE). Please make
an appointment by calling<PHONE_NUMBER> or emailing<EMAIL_ADDRESS>Since
accommodations require early planning and are not provided retroactively, it is
recommended that you make your request prior to or during the first week of class. It is
not necessary to disclose the nature of your disability to your instructor. For more
information about academic support services, visit the website at:
www.lewisu.edu/CASE.
Lewis University has adopted Blackboard Ally providing alternative formats for files
uploaded by instructors. Students can click the down arrow next to any file and select
Alternative Formats.
Sanctified Zone
Guided by its Catholic and Lasallian heritage, Lewis University is firmly committed to
fostering a campus atmosphere that is permeated by its Mission values of Fidelity,
Wisdom, Knowledge, Justice, and Association. Accordingly, we have declared the
University campus to be a Sanctified Zone, a place and a people United in Diversity.
The active promotion of diversity and the opposition to all forms of prejudice and bias are
a powerful and healing expression of our desire to be Signs of Faith (Signum Fidei) to
each other. To learn more about the Sanctified Zone, please visit:
www.lewisu.edu/sanctifiedzone
Academic Integrity
Scholastic integrity lies at the heart of Lewis University. Plagiarism, collusion and other
forms of cheating or scholastic dishonesty are incompatible with the principles of the
University. Students engaging in such activities are subject to loss of credit and
expulsion from the University. Cases involving academic dishonesty are initially
considered and determined at the instructor level. If the student is not satisfied with the
instructor’s explanation, the student may appeal at the department/program level.
Appeal of the department /program decision must be made to the Dean of the
college/school. The Dean reviews the appeal and makes the final decision in all cases
except those in which suspension or expulsion is recommended, and in these cases the
Provost makes the final decision.
University Student Complaint Policy
The University Student Complaint Policy can be found at lewisu.edu/studentcomplaints
University Grade Appeal Policy
The University Grade Appeal Policy can be found at lewisu.edu/studentcomplaints
Center for Health & Counseling Services
To support student success, all Lewis students are eligible for free health and
mental health services on the Romeoville campus. This includes commuters and
those living on campus, part-time and full-time students, graduate and
undergraduate students, and those taking Lewis classes at other locations. For
more information, visit the Center for Health & Counseling website at
www.lewisu.edu/studentservices/health or call<PHONE_NUMBER>.


We're a locally owned Medical Company in Jackson, TN that buys, fix, and cleans medical equipment to be resold.
We are open every day of the week from 8:00am till 7:00pm!
The Master Medical Equipment Promise:
If there is a product that does not pass our MME 5-Point Inspection process, it does not receive the MME Seal of Approval. We offer that guarantee because we want only the best for our customers.
Don’t be persuaded by the one-man popshops out there with used medical equipment who promise more than they can deliver. Our company stands by our guarantee and if we don’t have what you need, we can find it from a reputable dealer.
Here at Master Medical Equipment, we value relationships and your business is important to us.
“MME is always helpful and on top of my orders! They really know the industry. Customer service is second to none and they make sure that what I need is what I get. Warranty has never been a problem.”
Doug Neel
Repair Manager House of Supply Co., Memphis TN.
56 N Conalco Drive
Jackson, TN 38301
731-554-1223
866-468-9558
Fax:<PHONE_NUMBER>
Feel free to contact us or drive by at the adress's above!
to page 2 Brands
to page 3 Equipment
to page 4 Specials of the company
to page 5 We are also partners with biomedical
to page 6 The team your working with


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The maximum character length allowed in this text box is 250.
I authorize an officer from the office of the Registrar of Companies to make the said search on my
behalf
and I do
hear by expressly declare that I absolve the Registrar General of Companies and every officer of his
Department
from
responsibility for any act or omission of the said officer in regard to such search or any information that
may
be
given or omitted to be given by him to me.


Have you ever eaten haggis before?
Have you ever eaten snails before?
Have you ever eaten locusts before?


Are you looking for IT services & a solution provider?
Collaboration: We’re all about forging relationships. Our team is close and tight-knit—and that’s how we’ll work with you, too.
Craftsmanship: We work with brands who are the best at what they do. They work with us because we’re the best at what we do.
Accountability: Honest, clear communication is what we’re all about.

Find Out About Our Projects
The Nigerian Spectator is an online independent news outlet.
View DetailsSamuel Adegboyega University, Ogwa, was founded by The Apostolic Church Nigeria, Lagos, Western and Northern Areas (LAWNA) Territory
View DetailsNaija Houses is the best real estate listing website for buying, renting or selling of the residential & commercial property anywhere in Nigeria.
View DetailsWe follow this simple 4 steps
We discuss your projects and come up with working ideas to solve your IT Problems.
We gather all the required information to complete the project
At this stage we design the plan and develop the idea to present a complete project.
stage of the project. After Development, Testing and Your satisfaction with our innovative IT expertise. You give consent and we launch your project.


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Welcome to the
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Hey I am a header1!
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Black Mirror
An anthology series exploring a twisted, high-tech world where humanity's greatest innovations and darkest instincts collide.