Plates up: Half pins across: Gear ratio: Ready.
ADVANCED (takes way longer to calculate!!!) Allow Two Gears On Same Axle Negative Movements Allowed
Gears to use: 8 12 16 20 24 36 40


Product Description
Game Ready Laptop
This metal-clad Acer Predator Helios gaming laptop features a 39.6 cm (15.6)
FHD IPS display is a powerful and stylish device for your gaming needs.
NVIDIA RTX Technology
Powered by the NVIDIA Turing GPU architecture, along with NVIDIA RTX 3060,
for a fast, realistic, and AI-enhanced gaming experience.
The NVIDIA Turing architecture brings together real-time ray tracing,
programmable shading, and artificial intelligence to improve your
gaming experience.
Desktop-caliber Gaming
This gaming laptop runs on a 10th Gen processor that can deliver a clock speed
of up to 5.0 GHz Turbo. The 6 cores and 12 threads are capable of overcoming
most challenges while ensuring that your gaming experience is not compromised.
Fast Speed
This laptop features a 240 Hz panel that comes with a 3 ms overdrive response
time. As a result, you get to view blur-free, crisp, and clear high-octane
gameplay.
Quick Loading
For fast loading speed, this laptop features a dual-storage facility of
256 GB SSD, PCIe Gen3, 8 Gb/s, NVMe and 1 TB HDD 6.3 cm (2.5) with 7200 RPM.
Furthermore, the expandable DDR4 storage ensures that you can customise your laptop
as per your gaming needs.
Effective Cooling
This laptop comes with a 4th Gen AeroBlade 3D Technology to ensure that the
system is cool while you play your favourite games.
Low Latency Gaming
For exciting and victorious online gaming, this Acer gaming laptop comes with
Killer’s E2600 Ethernet Controller, Killer Wi-Fi 6 AX1650i, and Control
Center 2.0.
4-zone RGB Customized Keyboard
This gaming device features a 4-zone RGB customized keyboard having a Predator typeface,
concave-shaped, see-through keycaps for WASD, and two integral keys for Turbo and PredatorSense.
By pressing on the Turbo key, you can activate instant overclocking, and to open the utility app,
you can tap on the PredatorSense key.
Multiple Ports
This laptop comes with an HDMI 2.0, MiniDP, and the USB 3.2 standard with Gen1 and 2 ports to
connect to various peripheral devices to improve your gaming experience.
Ray Tracing
This feature simulated the right behaviour of light and delivers real-time, cinematic-quality
rendering. So, you can fully enjoy visually intense games.
Bionic Design
The design is inspired by powerful, yet silent owl flight mechanism, and so fans have a serrated
edge to ensure that more air passes through easily.
Ultra-thin Blades
The all-metal, 0.1 mm thick 59 bionic blades have a stabilizing dual indent groove.
Reduced Noise
This gaming laptop has winglets placed along its top and bottom fan edge. Also, it has a curved
fin along its inner blade. As a result, you can experience reduced turbulences and noise.
DTS X Ultra
You can enjoy immersive 360° surround sound experience on your headphones or speakers, thanks to
the DTS X Ultra. So, you can enjoy spatial sound while playing games and watching videos.
Built-in Alexa
Alexa lets you control various functionalities of this gaming laptop with ease.
Specifications
General
Sales Package
: Laptop, Power Adaptor, User Guide, Warranty Documents
Model Number
: PH315-53
Part Number
: NH.QCYSI.008
Series
: Predator Helios 300
Type
: Gaming Laptop
Suitable For
: Gaming, Processing & Multitasking
Battery Backup
: Upto 7 hours
Power Supply
: 230 W AC Adapter
Battery Cell
: 4 cell
MS Office Provided : No
Processor And Memory Features
Dedicated Graphic Memory Type : GDDR6
Dedicated Graphic Memory Capacity : 6 GB
Processor Brand
: Intel
Processor Name
: Core i7
Processor Generation
: 10th Gen
SSD
: Yes
SSD Capacity
: 256 GB
RAM
: 16 GB
RAM Type
: DDR4
HDD Capacity
: 1 TB
Processor Variant
: 10870H
Chipset
: Mobile Intel HM470 PCH Chipset
Memory Slots
: 2 slots
Expandable Memory
: Upto 32 GB
RPM
: 7200
Graphic Processor
: NVIDIA GeForce RTX 3060
Operating System
OS Architecture : 64 bit
Operating System : Windows 10 Home
System Architecture : 64
Port And Slot Features
Mic In
: Yes
RJ45
: Yes
USB Port
: 1 x USB Type-C Port (USB 3.2 Gen 2),
1 x USB 3.2 Gen 2 Port with Power-Off Charging, 2 x USB 3.2 Gen 1 Ports
HDMI Port
: 1 x HDMI
Hardware Interface : PCIe NVMe
Display And Audio Features
Touchscreen
: No
Screen Size
: 39.62 cm (15.6 inches)
Screen Resolution
: 1920 x 1080 Pixels
Screen Type
: Full HD LED Backlit IPS ComfyView Display (300 nits Brightness, 72% NTSC Color Gamut)
Speakers
: Built-in Speakers
Internal Mic
: Built-in Dual Microphones
Refresh Rate
: 144 Hz
Sound Properties :
DTS X : Ultra Audio, Featuring Optimized Bass, Loudness, Speaker Protection
with up to 6 Custom Content Modes by Smart Amplifier, Supported in Windows
Spatial Sound for PC Gaming with DTS License Integrated, Immersive Audio
Rendering Over Headphones and Internal Speakers, Acer Purified Voice
Technology with Dual Built-in Microphones. Features Include Far-Field Pickup,
Keystroke Suppression, Adaptive Beam Forming, and Pre-Defined Personal and
Conference Call Modes, Compatible with Cortana with Voice, Acer TrueHarmony
Technology for Lower Distortion,
Wider Frequency Range, Headphone-Like Audio
and Powerful Sound
Connectivity Features
Wireless LAN : Killer Wi-Fi 6 AX1650i
Bluetooth : v5.1
Ethernet
: Killer Ethernet E2600, 10/100/1000 Mbps
Additional Features
Disk Drive
: Not Available
Web Camera
: 720p HD Webcam
Finger Print Sensor : No
Keyboard
: FineTip RGB-backlit Keyboard with Independent Standard Numeric Keypad
Backlit Keyboard : Yes
Pointer Device
: Multi-Gesture Touchpad
Included Software
: PredatorSense, Acer Care Center, Planet9, Acer Product Registration, Quick Access
Additional Features
: 59 WHr Li-ion Battery, BIOS User, Supervisor Passwords


A caching solution that includes a JSP tag library and set of classes to perform fine grained dynamic caching of JSP content, servlet responses or arbitrary objects. It provides both in memory and persistent on disk caches, and can allow your site to have graceful error tolerance (eg if an error occurs like your db goes down, you can serve the cached content so people can still surf the site almost without knowing).
The OSCache Homepage is at www.opensymphony.com/oscache


Heima spp. (Sinicuichi) & Salvia divinorium
This is my second try at an experience with sinicuichi. This time the tea had been prepaired with approximately 15-20grams of sinicuichi that had, unlike my previous attempt, been cut to very small pieces. It sat for a little over a day in the sun. This time the brew was an opaque brown color, with a very bitter (disgusting) taste. While I could get down the liquid without difficulties, it was not a pleasent experience.
Some effects were felt almost immediately (t+0:10). A feeling a sedation and a slight laxative effect which went away in 5-10 minutes. The sedation however, persisted. Music sounded phenominal - more resonant and bassy than usual. I put on an awesome song called 'Archie Shepp - Blues for Brother George Jackson (Mondo Grosso remix)' and waited for work. I'm really getting caught up in the music, more than usual. I feel cold. I belive that this is because of the sinicuichi rather than the ambient tempiture.
An achy feeling pervades my body, as if I had done a workout the day before, which I had not. I also had this side effect the last time I drank sinicuichi tea, but had put it down to a great deal of walking I had done the previous day. There is also a slight but ignorable nausea. I decide to hit a bowl of salvia at t+1:00, as much out of a desire to rid myself of the nausea as much as to trip (a oft missed fact about salvia is that smoking psychoactive amounts of it *temporarily* banishes nausea). Salvia wow... I think I smoked more than I intended. My sense of reality was distorted in ways that are impossible to explain.
The soreness is strange - it permeates every muscle in my body but I don't really mind. It's an almost relaxing feeling, more the ache of your body after a satisfying workout than anything else.
The auditory hallucinations are even stranger. It's nothing like the 'distant' sounds I was lead to believe I would hear. Instead, the effects resemble a low dose of DIPT, a substance I have had the pleasure of being entertained by only once. The EQing of sounds is off, bass and lower midrange sounds are accented, giving music a warm bassy feeling. The letter 'S' seems to stick out or be overly pronounced - to my ears it gives everyone a slight lisp. Harmonics are frequently changed and emphasised to the point at which one note sounds like 2 or more in tandem. This also gives voices a strange, robotic sound. It does not, however, interfear with my enjoyment of music.
One last note: this substance does not impair my ablity to think or physical coordination in the slightest. I feel quite clearheaded. About recalling memories from the distant past - well that has failed to happen.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


--- creation_epoch:<PHONE_NUMBER> lastmod_epoch:<PHONE_NUMBER> categories: Security title: AWS Security Architecture work I've done --- I've presented this material at security conferences regarding my professional work I've done with Amazon AWS.
I provide an architecture overview of the security my company used to provide for rapid business flexibility but still add multiple layers of security to our IaaS cloud usage.
InnoTech OKC - 2017
InnoTech OKC - 2016


---
layout: game-page
title: FarmFight
year: 2021
semester: Fall
description: "FarmFight is a 2d multiplayer game with the goal of taking all the tiles on a board. Use your intuition and reaction speed to plant and harvest your crops while using soldiers to take other player's crops. Take the most tiles on the board within 5 minutes to win!
Play the game here. Here is the source code."
tags: Windows Mac Multiplayer
# tag options: "Windows", "Mac", "Linux",
# "Singleplayer", "Multiplayer",
# "Controller-Compatible", "VR",
# "Game-Jam"
project_members: {
Ram Potham: "Team Lead, Game Design",
Matthew Garcia: "Programming, Bot Design",
Alana Gerald: Art,
Angela Xu: Art,
Courtney Davis: "Art, Visual Effects, Programming",
Eli Fox: "Programming, Networking",
Clarise Liu: "Sound"
}
stage_game_path: FarmFight/
builds: {
Windows: FarmFight.zip,
Mac: FarmFight.app.zip,
Android: FarmFight.apk
}
thumbnail: banner.png
top-img: banner.png
screenshots: [
banner.png,
ss2.png,
ss3.png,
ss4.png
]
# Optional
awards: [
]
top-img-fit: fill
# top-img-fit options:
# "cover" - Cover the entire top area with the image, without squashing/stretching
# "fill" - Fit the FULL image into the top area, squashing/stretching as necessary
---


Proposal
Home
Payments
Delegate Status
Status: {{node['forging']}}
Rank: {{node['rank']}}
Voters: {{node['votes']}}
Pool Ledger
Address
Pending ({{n['coin']}})
Paid ({{n['coin']}})
{% for r in row %}
{{ r[0] }}
{{ r[1]/100000000 }}
{{ r[2]/100000000 }}
{% endfor %}


Select your property
{% set properties = [ '300 Creek Road, Greenwich, SE10 9SW', '296 Creek Road, Greenwich, SE10 9SW', '298 Creek Road, Greenwich, SE10 9SW', '302A Creek Road, Greenwich, SE10 9SW', '302B Creek Road, Greenwich, SE10 9SW', '304 Creek Road, Greenwich, SE10 9SW', '306 Creek Road, Greenwich, SE10 9SW', 'Nautical House, Creek Road, Greenwich, SE10 9SW', 'Flat A 310, Creek Road, Greenwich, SE10 9SW', 'Flat B 310, Creek Road, Greenwich, SE10 9SW' ] %}We found {{ properties | length }} properties at this postcode.
-
{% for address in properties %}
- {{ address }} {% endfor %}


sonohi no kibun ni awase te fuku no iro o →
You can change your clothes' color depending on your mood. Just say "Change"! I already have it.


Tylr - /ˈtʌɪlə/
The planning tool for laying tiles
Welcome!
This tool is made so you can plan your tiles' layout in an easy fasion. It's not always so easy to know exactly how the tiles fit in a room, and how to cut them for optimal placement.
Just fill out some numbers below and we'll try to help you figure out exactly that, in an easy manner that's a lot easier on your back and knees.
Room Settings
centimeter(s).
centimeter(s).
Tile Settings
centimeter(s).
centimeter(s).
centimeter(s).


| DOSE: T+ 0:00 |
7.0 mg |
oral |
2C-T-2 |
(powder / crystals) |
| T+ 0:30 |
7.0 mg |
oral |
2C-T-2 |
(powder / crystals) |
| T+ 3:50 |
10 mg |
oral |
Pharms - Diazepam |
(pill / tablet) |
Experience – First time
Setting – my basement
I have been interested in the 2C compounds for awhile now. My first experience with 2C-I was almost perfect. I felt I needed to explore these chemicals a little more, rather than limit myself to one.
For the evening I was trying to decide on 2C-T-2 or 2C-C. I was debating whether I should go with something I’ve wanted to try for awhile, or go with something new and be adventurous. I opted to go with the 2C-T-2 as it had been on my “to do” list longer.
I had heard of the horrible nausea that had been associated with 2C-T-2 so I decided to mix my dose into a drink, and sip it over 30 minutes. I poured myself a tall glass of juice and sprinkled 14mg of 2C-T-2 into it. I lit some incense and prepared to drink.
11:10 PM – I sip my 2C-T-2 cocktail. There is no taste other than the juice. I am pleased as I did not want a chemical flavored beverage.
11:30 PM – I’m still sipping my drink. I am almost ¾ done with it. I feel a bit light headed. There is no nausea to speak off. I had a garbage bag nearby in case I needed to throw up, but I could tell it wasn’t going to be a problem.
11:38 PM – I finish my drink. I have a mild body high now. It feels a lot like how I remember 2C-I feeling. My mind seems to be drifting off.
12:00 AM – My depth perception is definitely altered. I am at my computer and it seems much farther away from me than it did a few minutes ago. I reach out for it and my arm seems to extend forever until it makes contact. Quite interesting.
12:05 AM – I notice a bit of a headache. This feels much like the 2C-I headaches I usually get. I have not read of 2C-T-2 giving headaches, but I just dismiss it as something that I will have to deal with. I also notice some objects are taking on a breathing effect. The room seems to be coming alive now.
12:08 AM – There is a light directly above me. It feels so warm. I makes me quite happy and I feel very comfortable.
12:20 AM – Body buzz is getting stronger. I also notice a splash of color on the walls. It looked like someone splashed red paint, and then it faded away. It was quite nice to see. I call my girlfriend R as she had been out for the night, and wanted to hear from me. Talking to her increased my mood even more.
12:35 AM – Strong patterning has appeared. Very colorful images are sliding across the carpet. I try to describe them to R but I can say is they are beautiful. She laughs. I have no problem talking to her while tripping, as she knows how incoherent I can become. And because I know she is aware of this, I feel no shame.
1:00 AM – I hang up with R. I am still completely in awe of the patterning I’m seeing. The body buzz is still there but actually seems weaker. Could it be I have passed the peak in less than 2 hours?
1:30 AM – The patterning stops. This surprises me as the hallucinations I was having were so vivid and now they are gone. The breathing of objects seems to have diminished as well.
2:00 AM – All visuals are gone. The body buzz is mild at best. My mind feels completely sober. I am extremely confused by this. I had read the duration of 2C-T-2 was roughly 6-8 hours. It was almost 3 hours since I started ingesting it and I felt sober. Perhaps this comes in waves I thought. I decided to just wait, and that it would all come back to me.
3:00 AM – Nothing. I still feel sober. But I feel stimulated. I am rather disappointed the results here. I try to go to sleep, but it does not work. I feel like I’ve had way too much caffeine. I take 10mg of Valium.
3:30 AM – The valium has knocked me out, and I am done for the night.
-------------------------------------------------
I don’t know exactly what to think here. I was really disappointed with how short it was. I don’t understand why this happened. Perhaps my body metabolizes 2C-T-2 very quickly. Also, it could have been my dose was weak; a mere 14mg. Also, drinking over the 30 minutes could have made a difference.
The results of this could have been a combination of things. I guess I’ll need to try this again. I think next time I will take a higher dose (16-18mg) and I’ll take it all at once, and just try to deal with the nausea that may come.
The effects of this drug were quite pleasant for awhile. It was just a shame to have them all end so fast…
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Pasta Palace Manager Portal
Home
(current)
Handbook
Employees
Submit Reimbursement Request
View Schedule
Manager Portal
Approve Reimbursement Requests
Modify Employee Schedule
Update Employee Information
Search
Update Employee Information
Pasta Palace Reimbursement Management Portal
For use by Pasta Palace's manager.
Email Address
Password
Please ensure CAPSLOCK is off.
Reimbursement Requests Will Appear Below


随机梯度下降:Stochastic gradient descent
shortened to SGD
also known as incremental gradient descent,
a stochastc approximation of the gradient descent
for minimizing an objective function
is the value of the loss function at -th example
is the empirical risk
Algorithm
- Choose an initial vector of parameters w and learing rate
- Repeat:
- Randomly shuffle examples in the training set.
- For i =1,2,…,n,do:
- until minimum is obtained
note
A compromise between computing the true gradient and the gradient at a single example is to compute the gradient more than one training examplemini-batcha at each-step
动量方法:momentum method
reference
Rumelhart, David E.; Hinton, Geoffrey E.; Williams, Ronald J. (8 October 1986). “Learning representations by back-propagating errors”. Nature. 323 (6088): 533–536. doi:10.1038/323533a0.
Determines the next update as a linear combination of the gradient and the previous update
is a step size (sometimes called the learning rate in machine learning
AdaGrad
Increases the learning rate for more sparse parameters and decreases.
perform well where data is sparse and sparse parameters are more informative
Algorithm
A base learning rate , multiplied with the elements of a vector which is the diagonal of the outer product matrix.
where , iteration
This vector is updated after every iteration
written as per-parameter updates,
Each gives rise to a scaling factor for the learning rate that applies to a single parameter .
is the norm of previous derivatives, extreme parameters updates get dampened.
parameters that get few or small updates receive higher learning rates.
RMSProp (for Root Mean Square Propagation )
Divide the learning rate for a weight by a running average of the magnitudes of recent gradients for that weight.
is the forgetting factor.
parameters are updated as,
Capable to work with mini-batches as well opposed to only full-batches.
Adam (short for adaptive Moment estimation)
update to RMSProp optimizer.
In this optimization, running averages of both the gradients and second moments of gradients are used.
and denote the power t of ,.
, are the forgetting factors.
is a small number used to prevent division by 0.
Good default settings for machine learing problems ar
and


File: ../src/away/materials/lightpickers/LightPickerBase.ts
///<reference path="../../_definitions.ts"/> module away.materials { /** * LightPickerBase provides an abstract base clase for light picker classes. These classes are responsible for * feeding materials with relevant lights. Usually, StaticLightPicker can be used, but LightPickerBase can be * extended to provide more application-specific dynamic selection of lights. * * @see StaticLightPicker */ export class LightPickerBase extends away.library.NamedAssetBase implements away.library.IAsset { public _pNumPointLights:number = 0; public _pNumDirectionalLights:number = 0; public _pNumCastingPointLights:number =0; public _pNumCastingDirectionalLights:number = 0; public _pNumLightProbes:number = 0; public _pAllPickedLights:away.lights.LightBase[];//Vector.<LightBase>; public _pPointLights:away.lights.PointLight[];//Vector.<PointLight>; public _pCastingPointLights:away.lights.PointLight[];//Vector.<PointLight>; public _pDirectionalLights:away.lights.DirectionalLight[];//Vector.<DirectionalLight>; public _pCastingDirectionalLights:away.lights.DirectionalLight[];//Vector.<DirectionalLight>; public _pLightProbes:away.lights.LightProbe[];//Vector.<LightProbe>; public _pLightProbeWeights:number[]; /** * Creates a new LightPickerBase object. */ constructor() { super(); } /** * Disposes resources used by the light picker. */ public dispose() { } /** * @inheritDoc */ public get assetType():string { return away.library.AssetType.LIGHT_PICKER; } /** * The maximum amount of directional lights that will be provided. */ public get numDirectionalLights():number { return this._pNumDirectionalLights; } /** * The maximum amount of point lights that will be provided. */ public get numPointLights():number { return this._pNumPointLights; } /** * The maximum amount of directional lights that cast shadows. */ public get numCastingDirectionalLights():number { return this._pNumCastingDirectionalLights; } /** * The amount of point lights that cast shadows. */ public get numCastingPointLights():number { return this._pNumCastingPointLights; } /** * The maximum amount of light probes that will be provided. */ public get numLightProbes():number { return this._pNumLightProbes; } /** * The collected point lights to be used for shading. */ public get pointLights():away.lights.PointLight[]//Vector.<PointLight> { return this._pPointLights; } /** * The collected directional lights to be used for shading. */ public get directionalLights():away.lights.DirectionalLight[]//Vector.<DirectionalLight> { return this._pDirectionalLights; } /** * The collected point lights that cast shadows to be used for shading. */ public get castingPointLights():away.lights.PointLight[]//Vector.<PointLight> { return this._pCastingPointLights; } /** * The collected directional lights that cast shadows to be used for shading. */ public get castingDirectionalLights():away.lights.DirectionalLight[]//:Vector.<DirectionalLight> { return this._pCastingDirectionalLights; } /** * The collected light probes to be used for shading. */ public get lightProbes():away.lights.LightProbe[]//:Vector.<LightProbe> { return this._pLightProbes; } /** * The weights for each light probe, defining their influence on the object. */ public get lightProbeWeights():number[] { return this._pLightProbeWeights; } /** * A collection of all the collected lights. */ public get allPickedLights():away.lights.LightBase[]//Vector.<LightBase> { return this._pAllPickedLights; } /** * Updates set of lights for a given renderable and EntityCollector. Always call super.collectLights() after custom overridden code. */ public collectLights(renderable:away.base.IRenderable, entityCollector:away.traverse.EntityCollector) { this.updateProbeWeights(renderable); } /** * Updates the weights for the light probes, based on the renderable's position relative to them. * @param renderable The renderble for which to calculate the light probes' influence. */ private updateProbeWeights(renderable:away.base.IRenderable) { // todo: this will cause the same calculations to occur per SubMesh. See if this can be improved. var objectPos:away.geom.Vector3D = renderable.sourceEntity.scenePosition; var lightPos:away.geom.Vector3D; var rx:number = objectPos.x, ry:number = objectPos.y, rz:number = objectPos.z; var dx:number, dy:number, dz:number; var w:number, total:number = 0; var i:number; // calculates weights for probes for (i = 0; i < this._pNumLightProbes; ++i) { lightPos = this._pLightProbes[i].scenePosition; dx = rx - lightPos.x; dy = ry - lightPos.y; dz = rz - lightPos.z; // weight is inversely proportional to square of distance w = dx*dx + dy*dy + dz*dz; // just... huge if at the same spot w = w > .00001? 1/w : 50000000; this._pLightProbeWeights[i] = w; total += w; } // normalize total = 1/total; for (i = 0; i < this._pNumLightProbes; ++i) { this._pLightProbeWeights[i] *= total; } } } }


GDC
2002: Polygon Soup for the Programmer's Soul: 3D Pathfinding
By
Patrick
Smith
Gamasutra
April
5, 2002
URL: http://www.gamasutra.com/features/20020405/smith_01.htm
One of the fundamental goals of an AI system is to avoid making the unit appear "dumb." At the root of this challenge lies one of the hardest problems to overcome efficiently and believably: pathfinding. Today, 3D graphics and sound technologies begin to reach a level of realism that is easily destroyed by seeing an AI unit walk face-first into a solid wall, slide along the perimeter and ultimately get stuck on an errant polygon. Traditional technologies that worked well for games a few years ago fall apart when faced with the complexity of today's 3D environments.
This paper addresses the pitfalls of attempting to pathfind the arbitrary world we call "polygon soup." It covers automatic data generation and compression, a run-time distributed algorithm, organic post-process modifiers, and solutions for tricky situations such as doors, ladders, and elevators.
Where to Get Good Data
There are numerous good algorithms for determining a path given a connectivity
graph. For simple 2D games the connectivity graph was a side affect of the tiling
system used to create the map. For 3D games, especially those constructed from
arbitrary geometry, no such simple solution exists. So the problem exists, where
do we get good data?
Imagine importing the outline of a stained glass window into your favorite paint program. The image consists of large, irregularly shaped regions of pure white space. Your task: color each of these regions to complete the stained glass window. Now, which tool is best for the job, the pencil (which would require you to color each and every pixel independently) or the paint bucket (which can recursively flood a whole region with color)? Should be an easy decision, right? So why is it that many level designers are forced to manually populate their levels with numerous pathfind nodes, sectors, grids, zones, planes, subdivisions, portals or whatever whoseewhatsit is popular that day? Now, visualize an automated system that could discover all possible traversable locations in your most complicated environment with a single click of the mouse. Can you picture it? Great! Let's build it.
To solve this Herculean task we'll borrow a simple idea from our paint program metaphor, namely, the recursive flood fill. The only tools we'll need are a good collision detection system and the knowledge of a single point in the polygon soup where a unit can stand. The algorithm works as follows:
Use the collision detection system to determine if the unit can exist at the start point. If the area is valid, add that point to a list. This will be our task list. Now, loop over all the entries in the task list (which is currently one). For each entry in the list, simulate the unit taking a step in each of the four cardinal directions (north, east, south, and west). If one of these points passes the collision check, add it to the task list. Retain the connection information between these two points (point A can walk to point B).
There, you're done, once this algorithm finishes you'll have a complete connectivity graph of the current game level. Of course there are always details…
Detail 1: Of
shapes and areas.
When doing the collision checks we aren't really dealing with points, but volumes.
This is because we are attempting to simulate an object, and objects have volume.
We need to decide what volume yields the best results. Different collision systems
use different representations for an object's collision volume; some use cylinders,
some spheres, while others use boxes. In order to cover space continuously,
without leaving gaps between areas, we'll use boxes.
|
Circular
shapes form gaps when arranged adjacently, while boxes cover space continuously.
|
At this point you should begin to see the familiar form the data is taking, a grid! However, this isn't the simple 2D grid you may be imagining. If you ignore the elevation of each box, the grid is uniform, however because we simulate walking from one grid cell to the next, it can take us up or down slopes, across bridges and over and under overpasses. This means that for any given coordinate on the "plane" there can exist more then one grid cell (consider elevation). A good example of this is a spiral staircase. The algorithm will fill up and around each turn of the staircase as if it were on a flat plane, however if you stood at the top and cast a ray down the course of the staircase you would encounter a grid cell at every turn of the stairwell.
Detail 2: Size
does matter.
It is very important that the cell size is appropriate for the object pathfinding
the level. Too large and the object will not be able to path through doors that
are not orthogonal, too small and you'll find your computer running out of RAM
before it finishes flood filling. As a suggestion, make an axis-aligned bounding
box from the object's collision volume. This box should completely contain the
object regardless of its orientation. Now, divide the X and Y dimensions by
2, thus giving you a "tall, skinny" box.
|
A
good sized cell is generally a quarter of the object's maximized collision
box.
|
The reason for this derives from the fact that the grid is axis-aligned (non-rotated). If a door or other passageway is rotated, and during game play the object fits through the door because it rotates as well, then a non-rotated pathfind cell will collide with the door.
Detail 3: It's
not what you know, but who you know.
At a minimum, each cell of the grid only needs to know which neighbors are traversable.
This can be accomplished via a simple data structure that has four pointers,
one to each of its neighbors. If a pointer is NULL, that direction cannot be
traversed.
class
CPathCell
{
.
.
.
CPathCell * NorthCell;
CPathCell * SouthCell;
CPathCell
* EastCell;
CPathCell * WestCell;
};
This simple data structure completes a connectivity graph - we now have enough information to pathfind between any two cells in the level.
Detail 4: No
bonus for extra work.
Be careful to avoid reprocessing cells that you already know are traversable.
For example, we start with cell A and successfully simulate stepping to cell
B. When it comes time to process cell B, we already know cell A has been processed
so it should not be added back into the list. However, we still need to simulate
stepping from cell B to cell A - just because we can walk from A to B does not
guarantee we can walk from B to A (i.e. stepping from A to B takes you off a
small cliff).
Detail 4: Isn't
this a lot of memory?
Simply put, yes it is. Therefore it is extremely important to keep your data
structures as tight as possible. For instance we've seen upwards of 10 million
cells generated for a large level.
Keep in mind that this is a preprocess step, we're simply generating the data. Before we can use this data it must be compressed into a format that is feasible for run-time application.
Doors, Elevators, and
Ladders - Oh my!
It is extremely satisfying to give an AI the command, "Goto (x,y,z)",
and watch as the unit performs a complex series of steps. For example, given
a single destination, an AI could walk to the front door of a building, open
the door, walk to the elevator, wait for the elevator to arrive, take the elevator
to the roof, exit the elevator, walk to the ladder at the base of the tower,
and climb to the top of the tower.
Even more satisfying is to have this information about the "teleport" mechanisms automatically detected during the data generation step. To arrive at this goal, we'll need to know the location of each door, elevator, ladder, or other teleport mechanism, and it's enter and exit zones. The algorithm now works as follows:
During flood fill, grab an unprocessed cell from the task list. Perform the simulation as before, if the cell passes, check to see if it is completely contained within the enter zone of one of the teleport mechanisms. If this cell is indeed inside an enter zone, create a new cell in the center of the exit zone and perform a collision check to ensure it is safe to stand there. If the new cell passes this test, add it back into the task list - this will ensure the simulation continues on the other side of the mechanism.
Avoid the temptation to store the connection between the "enter" cell and the "exit" cell. This extra data will unnecessarily bloat the size of each one of the cells in your 10 million cell flood fill. The connection information can easily be determined after the flood fill is complete. This issue is addressed later when discussing data compression.
What to do if you can't
devote 500MB of RAM to Pathfind
Now that we have a nice simple algorithm for generating the connectivity graph
of a level, it is time to start inspecting the data. Let's say each cell requires
approximately 50 bytes of data, and we've generated somewhere on the order of
10 million cells, that means we have 500MB of pathfind data. When problem solving,
the more data the better, but half a gigabyte is just plain ridiculous! It's
time to compress the data down to something more manageable.
Let's examine a simple scenario. If cell A connects to cell B, and cell B connects to cell A, then do we really need two cells? No we do not; we can combine the two cells into a sector and throw away the original cells. We define a sector to be a convex polyhedron of freely traversable space. Given our particular dataset and purpose, a box is the most effective convex polyhedron.
To expand on this compression scheme, consider the following diagram.
Cell A connects to B and C, cell C connects to A and D, cell D connects to C and B, cell B connects to D and A. Here, we can combine all four cells into a single sector and discard the original cells. To generalize, we can combine any rectangular set of contiguous, connected cells into a single sector. Ultimately, we'll want to compress all the cells into sectors. The sector then becomes the basic data type used during runtime to solve a path.
Once a sector is generated, it is necessary to retain the connection information its composite cells originally contained. To accomplish this, retain the edge cells and discard the interior cells of the sector. These edge cells share connection information with cells in other sectors, in effect, linking sectors together.
|
To
retain a sector's connection data, keep the edge cells.
|
After all the sectors have been built, you can then combine a sector's edge cells that share a common destination sector into a portal. We define a portal to be a connection between sectors; i.e. to get from sector A to sector B walk from your current position in sector A through the AB portal. The portal's physical shape is the bounding volume of its composite edge cells.
|
Portals
are connections between two sectors.
|
Portals can be one-way or two-way. If the edge cells in sector A are connected to the edge cells in sector B, and the same cells in sector B are connected to the sector A cells, then the portal is two-way. Otherwise the portal is one-way.
Once the portal is created, it is added to the list portal-list of the sector that contained the edge cells. If this is a two-way portal, it is added to the portal-list of both sectors.
Doors, Elevators, and
Ladders - Again!
Earlier we discussed how to flood fill across teleport mechanisms such as doors,
elevators and ladders. Now that we've converted all our cells into sectors and
portals, it's time to incorporate information about these mechanisms so that
our AI can use them.
To build a portal for a given mechanism, first intersect its entrance and exit zones with the existing pathfind sectors. If the intersection test yields at least one sector for the entrance and exit zones then we can create a portal between the two. The entrance portal's bounding box is the volume created by intersecting the entrance zone with the pathfind sector. The exit portal's bounding box should follow the same formula.
Store any specific
information about the mechanism the AI will need to know in order to operate
it in the portal. For example, if a door is locked, save the type of key that's
needed to open the lock. When the path solver is attempting to resolve the path,
it can ignore the portal if the AI is not carrying the required key.
Anatomy of a Sector
Once a sector is generated it contains two important pieces of information,
a bounding box and a list of portals. The bounding box is used at run-time to
determine where, in the pathfind world, an AI currently is. The portal-list
is the connection information that the path solver uses to calculate how an
object gets from its current position to its destination position.
class
CPathfindSector
{
.
.
.
CAABox BoundingBox;
CVector<CPathfindPortal *>
PortalList;
};
As you can see,
the sector is really just a spatial portal linkage; most of the interesting
data is contained in the portal.
Anatomy of a Portal
The portal contains as much data as is necessary to get the AI from one sector
to the next. At a minimum this data is a reference to the destination sector
(possibly two sectors for a two-way portal), and its bounding box. A portal
may also contain information about a teleport mechanism or an action to perform.
class
CPathfindPortal
{
.
.
.
CPathfindSector * DestSector1;
CPathfindSector * DestSector2;
int MechanismID;
MECHANISM_TYPE MechanismType;
CPathfindPortal * MechanismExitPortal;
ACTION_TYPE ActionType;
};
As you can see all the connection information is stored in the portal and is therefore the "key" to solving a path.
Solving the Path
Bob, the digital super spy, needs to avert international tragedy at the UN building.
 To make matters
worse, everywhere he looks he sees an ocean of polygons swimming before his
eyes. How can he possibly hope to reach his goal before time runs out?
Luckily for Bob, we can use his (X,Y,Z) location to lookup the pathfind sector he's washing socks in. This sector contains a list of portals he can use to get to other sectors, which contain their own portal lists, which can be used to get to yet more sectors, and so on and so forth.
At this point any number of best-path algorithms can be applied to get Bob to the UN building. We found a modified A-star algorithm solves the problem nicely. A simple implementation would use the accumulated distance between portals as the traversal cost, and the straight-line distance to the goal as the heuristic cost.
Now pretend that Bob and twenty of his closest buddies need to get to the UN building at the same time. Twenty different requests for complex paths can well exceed the amount of CPU your game devotes to AI each frame. Luckily for us, it is not very noticeable if we distribute the processing of these paths over a few frames. For example, if Bob sits and thinks for a half second before getting up and walking out the door, is anyone likely to notice? On the flip side, a half a second (500 milliseconds, ~1 billion clock cycles) is more then sufficient for a pathfinding system to solve a few simultaneous paths.
To distribute path solves over multiple frames you could use an algorithm like the one outlined in the following pseudo-code.
while
(Time_Has_Not_Elapsed () && CurrentPathSolver != NULL)
{
if (CurrentPathSolver->Process
(Time_Remaining_This_Frame))
{
RELEASE (CurrentPathSolver);
CurrentPathSolver
= Get_Next_Path_Solver ();
}
}
This is a simple time slice manager which allows the current path to process a little bit each frame. When the path is solved, a new path is processed until either it finishes or time has elapsed.
To distribute the path solve itself over multiple frames, consider the following pseudo-code.
CPathSolver::Process
(float time)
{
while (Time_Has_Not_Elapsed)
{
CPathfindSector
* sector = Get_Least_Cost_Sector ();
if
(sector == NULL)
{
Handle_No_Path
();
}
else if (sector
== DestinationSector)
{
Hande_Path_Found
();
}
else
{
CPathfindPortal
*portal = NULL;
while
(portal = sector->Get_Next_Portal (portal))
{
Process_Portal
(portal);
}
}
}
}
This algorithm will process as many sectors as necessary until its time-slice has elapsed.
Loosen Up Dude! Avoiding
Robotic Paths
Using the sector/portal system, a solved path is represented by an ordered series
of portals.  Unless your AI is a robot, it is desirable for the unit to behave as
organically as possible; bee-lining to each portal does not make for a very
organic path.
To make our path appear a little more natural we'd like to use splines. In layman's terms, splines are basically curved lines that follow a set of points. Different types of splines follow their points in different ways. Some splines pass through their control points, whereas other splines act like magnets and are either attracted or repulsed by their control points. To see the advantage of splining the path, consider the following diagram.
|
Splining
the final path yields a much more organic curve.
|
Looks great, right? Unfortunately, there is a problem. Since the spline doesn't know anything about the sectors and portals that define the "safe" area for a unit to walk, it is likely that a curve on the spline will leave these volumes causing the unit to walk into desk corners, doorways, trash cans, or even meander off the edge of a cliff!
Luckily we can take advantage of a mathematical property of Bezier curves (a type of spline): if the control points of the Bezier curve lie inside a volume, then the curve itself will lie inside the volume. A Bezier curve is a cubic spline defined by a start point, an end point and a series of control points. The control points act like magnets to "pull" the curve away from the line segment defined by the start and end points.
Given this, our organic post process algorithm would function as follows:
Build a Bezier curve from the points on the final path. Build a list of the sectors and portals that the AI will pass through on its path. For each control point on the Bezier curve, clip it to the sector volumes. To clip a control point, form a line from the control point to the corresponding point on the un-splined path. Test this line segment to see if it passes through the side of any of the sector boxes in the list. If it does, check to see if the area of intersection is a portal. If the point intersects a sector wall, and it does not pass through a portal, then clip the control point to the sector wall; otherwise leave the point alone - it is valid.
|
Control
points that pass through a sector wall need to be clipped. Control points
that pass through a portal are valid.
|
This algorithm will allow the AI to follow a nicely curved path without ever leaving the safe pathfind sectors.
Using Innate Waypaths
In a busy city, it would be odd to see cars driving down the sidewalk and pedestrians
meandering through the streets. However, using our current pathfinding system
this is exactly what you'd see. The flood fill algorithm does not encode (nor
is it aware of) the type of ground covered, be it sidewalk, highway, or burning
hot lava. A possible solution to this problem is to store surface information
in each cell of the flood fill and have your compression algorithm only generate
sectors containing cells of the same type. This would yield sidewalk sectors,
highway sectors and burning hot lava sectors. The run-time path solver could
then utilize this information when determining what sectors are valid for a
given object (i.e. cars only pathfind through street sectors).
Innate waypaths are another powerful solution that integrates well with our pathfind system. A waypath is a manually created series of waypoints which form a path. This waypath can be rigid or splined. Each waypoint on the waypath can encode specific information, such as crouch here, speed up, slow down, or even jump to the next waypoint. To integrate this information into our path solver, consider the following algorithm.
Run a normal pathfind flood fill to generate sectors and portals. For each innate waypath, create a "dummy" sector (a sector without size or position), and add it to the system. For every waypoint in the waypath, find which pathfind sector the waypoint intersects. Create a two-way portal from the pathfind sector to the dummy waypath sector at the location of the waypoint. Add this new portal to both the pathfind sector and the dummy waypath sector.
During the run-time path solve, the algorithm's heuristic should bias toward these waypath sectors (i.e. multiply the cost of using a waypath sector by 0.75). This will cause AIs to "tend" to follow innate waypaths.
Given this system, to enable vehicle AIs to drive along the right side of the street, a level designer would simply draw a one-way, vehicle only, innate waypath along each side of the road.
|
Innate
waypaths can be used to keep vehicles driving along the right side of
the street.
|
Follow the Leader
Currently, players are smarter then AIs: humans have the ability to combine
abstract concepts and make leaps of logic. This reasoning ability allows players
to perform actions that AIs cannot originate, but if we're lucky perhaps they
can replicate. For instance, during a firefight on the second floor of a building,
the player reasons he cannot win. In desperation he shoots out the window and,
with a mighty leap, escapes onto the street. What does the AI do? Unless we
give it an option, the AI will pathfind down the stairs, open the front door
and look dumbly down the street, for the player is long gone. Wouldn't it be
much more fun if the AI simply jumped out the window after the player?
There are many
different ways to approach this problem, so let's choose the simplest. We know
where the player jumped from and we know where the player landed. If both points
are inside the pathfind data, why not add a temporary one-way "jump"
portal between these two sectors? All we need to do is encode a little information
about the jump such as orientation, velocity, and time. Depending on the physics
system, this may be enough for the AI to replicate the player's mighty leap.
We can even keep a FIFO "bucket" of these temporary portals so other
AIs can follow.
Vehicles
Arbitrary vehicle pathfinding is an order of magnitude more difficult then traditional
bipedal pathfinding. Humans can turn on a dime, whereas vehicles have a turn
radius. Humans follow the same rules regardless of orientation, whereas vehicles
act differently in drive and reverse. Humans can stop on a dime, whereas vehicles
skid. Humans do not tip over when running too fast, whereas vehicles can roll.
A complete vehicle pathfinding solution is outside the scope of this paper, however we will present a brief overview that may work for some games.
Firstly, modify
the flood fill algorithm to use the bounding volume of the largest vehicle regardless
of its orientation. During the run-time path solve, take the turn radius of
the vehicle into consideration when evaluating portals. In other words, we know
which portal the vehicle is coming from, so discard any destination portals
that would cause the vehicle to turn sharper then it is able.
|
Take
turn radius into consideration when evaluating portals.
|
Once the path is generated, it may be problematic for a vehicle to follow. This is because orientation is important when dealing with vehicles; however our sector/portal system doesn't implicitly handle this very well. Consider the following diagram.
|
Orientation
is important when traversing the path. Once in Sector B, the vehicle will
never make the turn to Sector C.
|
In the sector/portal system, the vehicle will beeline from the Sector A's entrance to Sector B. This orients the vehicle in such a way that it will be impossible for the vehicle to make the following turn into the portal for Sector C. However, if the vehicle would "arc" out into Sector A, it would be possible to make both Sector B and Sector C. Unfortunately there is no way of knowing this is required unless we search ahead on the path.
A simple solution to this problem, which works well with the system we've described so far, is to spline the path. However, none of the classical "true" splines will work for this situation; which is fine -- we'll create our own custom curve.
Our goal: create a continuous curved line that will cause the vehicle to arc around corners while still obeying the vehicle's turn radius restrictions. Such a path can be created using only a straight line and the vehicle's turning circle. Unlike "true" splines, this path is not continuous in the mathematical sense, but composed of three distinct continuous parts, which, when placed end-to-end, form a continuous path. The three parts are: the exit curve from the previous point, a straight line from the exit curve to the enter curve of the next point, and the enter curve of the next point. Note: The starting and ending points only contain two parts (there is no previous part for the starting point, nor is there a next point for the ending point). Consider the following diagram:
|
The
vehicle curve is composed of three distinct parts: the exit curve, a straight
line, and the enter curve.
|
To build this curve, overlay the vehicle's turning circle onto each node of the path. We will assume the optimal center of this turn arc will lie at the point halfway between the angles formed by the (prev_node - curr_node) and (next_node - curr_node) vectors. This causes the actual node point to lie on the perimeter of the turning arc.
For each turning circle on the path, find the "in" and "out" tangent points. The "in" tangent point is the closest point of tangency from the previous point to the turn arc. The "out" tangent point is the closet point of tangency from the next point to the turn arc. Now, simply connect the dots. The path is as follows: straight-line from starting point to the "in" tangent point on the turn arc of the next path node; follow the turn arc to "out" tangent point; straight line from this point to the "in" tangent point of the turn arc of the next path node, etc, etc.
Once finished, this algorithm yields a continuous curve that follows the turning restrictions of the vehicle following the path.
|
The
vehicle curve ensures the vehicle will not attempt any impossible turns.
|
Keep in mind that this curve may cause the vehicle to drive outside the "safe" areas of the path, thus potentially colliding with objects along the way.
Conclusion
By taking advantage of a simple flood fill algorithm, we can overcome the Herculean
task of automatically generating connectivity data through complex polygon soup.
With a few tricks and extensions we can easily incorporate doors, ladders, and
elevators; spline the result of the path solve to yield a more organic looking
path; dynamically alter the pathfind data to allow the AI to replicate a player's
actions; incorporate innate waypaths to force a specific behavior from the AI;
and distribute the run-time path solve over multiple frames to balance the CPU
load.
Special thanks to Eric Cosky for the original concept of the flood fill algorithm, a brilliant idea that proves the worth of brainstorming with a friend before jumping into a complex problem. Also, thanks to Colin Mclaughlan for suggesting the concept of dynamic temporary "jump" portals.
Copyright © 2003 CMP Media Inc. All rights reserved.


tween.js
08 _ repeat
Demonstrating the repeat() feature.
repeat once
repeat five times
repeat forever


Develop a classification model that recommends the asset classes based on the freeform title, description, and other relevant procurement fields to provide data fidelity. The data comprises of procurement, distribution, maintenance, and retirement of the assets for a period of time. The aim is to be develop a classification model to provide an automatic mapping of order details to the Asset class.
The classification application can be used by a Supply chain organization to precisely assign classes to the orders based on the textual data provided by the users. These organizations prioritize efficient delivery of the products to the customers. It is a dynamic way to classify the orders as the misclassification rate can be decreased. Unlike the traditional way of manually assigning classes to the orders, this approach automates the process, lowering the human effort involved. Our product is more precise and can be used across similar datasets and is not restricted to only one organization. It needs to be noted that model performance is dependent on the quality and the quantity of the data.
The application or investigation process during the development can be used to identify the classes that are closely related to each other. This involves knowledge obtained using the features, patterns, and model. These insights can be used to combine or remove certain classes or suggest new ones. This will be very useful for various stakeholders in the supply chain cycle to improve or modify the asset classes.
The product can be used in providing recommendations to the user. Relevant fields are also provided to the user to input content based on the recommended class. This improves the quality of data being generated. Over time, this is expected to increase the fidelity of the data.
The Predict page lists the top five predicted asset classes with confidence values (Probability of the input belonging to that particular class) arranged in descending order.
The Continuous Learning page smoothly updates the prediction model of different tasks and data distributions and able to re-use and retain useful knowledge.
The Important Features page generates a list of 20 important words that are used in making asset class predictions, these are generated by the Random Forest Model. These top 20 words are represented in a bar graph with words on the y-axis and their associated weight on x-axis arranged in the decreasing order of the weight value.
Performance Metrics generates visualizations of the precision, recall, F1 score and Accuracy obtained by the KNN, Decision Trees, Naïve Bayes and Random Forest Models on the Dataset 1, Dataset 2 and Dataset 3. Stacked bar plots are generated for each of the metrics.
The Algorithms and Models describes all the shallow and deep learning models that are applied on the dataset to perform text classification. This section describes the overview of Naïve Bayes, K Nearest Neighbors, Decision Trees, Random Forest, Multi-Layer Perceptron, Convolutional Neural Network, LSTM and Bi-LSTM models.
Meet our team
Continual Learning (CL) is built on the idea of learning continuously and adaptively about the external world and enabling the autonomous incremental development of ever more complex skills and knowledge.
Would you like to train the model with new entries?
Naïve Bayes uses the probability calculated from the training set to calculate the posterior probability. As shown in Figure 17, y is the target class and [x1,x2 .. xn] are features provided as the input to the model. Naïve Bayes uses the feature selection score method by calculating the KL-Divergence between training documents and their labels. It tries to identify the difference in the probability distribution of one record from another to create a better performing model on the lesser number of classes. It assumes that the target label is assigned based on conditions between the attributes and that all thefeatures are independent. This assumption might not hold in real life.
KNN is an unsupervised classification algorithm, which classifies the new record based on k training sets and a similarity measure (computed based on the Euclidean distance). It is a simple, fast processing model that does not have a training phase. It is called a lazy learner because it does not train a model based on the training data but only memorizes the dataset. However, it was used in pattern recognition and statistical estimation in the early 1970s.
A decision tree is a supervised learning model which uses a divide and conquers approach to create a tree with the given data. The correlation between the target class and the features is determined by constructing a tree. It uses various tree pruning methods to reduce noise. The complexity of the tree increases with the increase in the size of the dataset. To decrease the computational costs, fast Decision Trees are introduced which uses two-pronged strategies (preselecting the set of features and training a set of DTs on different subsets of data).
Random Forest is a classification algorithm, that creates a set of decision trees. It uses feature randomness and bagging to create a forest of trees. The result is based on a prediction of each tree in the committee. The main limitation of the random forest is that algorithm is too slow because it tries to build a large number of trees and also tries to overfit the data. Random forest aggregates result from a set of decision trees.
MLP is a feed-forward neural network that connects multiple layers in a directed graph. This approach uses a supervised algorithm called the backpropagation technique for training. It has an input hidden layer with an activation function and output layers. The data is fed into the inpullayer and predictions are made at the output layer.
The value at each layer is calculated based on the value in the previous layers and by applying the weights. Every node (the computational unit) in the hidden layer uses an activation function to calculate a singular value at the node. The results at the hidden layer are computed, aggregated and the error rate is calculated at the output layer. The error rate at the previous epoch (train) is used to tune the weights of the current epoch and follow the same process until the termination condition is reached.For text classification, it considers the input text as the BOW.
In the beginning, CNNs (Convolutional Neural Networks) were introduced for Image classification, but later because of its convolving filters, this concept has performed better on NLP techniques as well. CNN can apply convolutions on multiple chunks of sequence data simultaneously. Like in image representation, the text is represented in vector format to extract features from different angles. The final vector representation of the text is generated from the convolutional and pooling layer. The word vectors created from the text are joined into a matrix and are fed into the convolutional layer. The convolutional layer contains several filters to process these matrices and the results are sent into the pooling layer and combine the results of the pooling layer to obtain the final vector representation of the text. The target class is predicted using this final vector.
The CNNs have better performance in extracting position-invariant and local features, their processing times are better compared to RNNs. But the model has poor interpretability, and the classification errors cannot be explained because of the non-readability nature of the hidden states. So, Attention nets are introduced to get a better interpretation of the results by employing informational components like encoders, attention layers of a text.
LSTM contains a gate structure with input, forget, and output gates. These are used to create a cell that remembers the value. The forget gate structure is used to remove the unwanted information and establish a connection among the context words that can be captured by the LSTM classification method. To improve the performance of this sequential classification model, Bi-LSTMs are introduced. This solves the problem by processing the sequence in both directions. Bi-LSTM duplicates the first recurrent layer in the architecture so that there are two layers side-by-side. The input sequence is provided to the first layer and a reversed copy of the sequence to the second layer to extract contextual features from both sides, left-to-right, and right-to-left. As the model is trained numerous times with many features, they are prone to overfitting and it is difficult to apply a dropout algorithm to curb this issue.
Recurrent Neural Networks is a layer made of memory cells that captures the context of the data, required to make predictions by learning the long-term dependencies. At each element of the sequence, the model also considers the information from the preceding elements and along with the current input. RNN considers the location information of the words to learn historical information, syntax and semantics of text automatically without the feature engineering step.The architecture of the Recurrent Neural Network Model, where word embeddings vector is input to the model. These embedding vectors are passed one by one to the model to capture the sequential information. RNN shares parameters at every epoch, to reduce the computational cost of training a model.
To reduce the time complexity of the model, semantics are extracted from long texts. These experiments use relative tasks for exploring common features. The computation of this neural network and training is relatively very slow. The weights in the model are calculated using the derivatives and the gradientsare adjusted using these weights. RNN face the vanishing gradient problem because of the smaller gradient values.
Bidirectional RNNs are RNNs that offer look-ahead ability as in hidden Markov models by putting two RNNs together. BiRNN runs in two different modes one in the forward mode starting from the first token and the other from the last token running from the back to front. For a more flexible process, the hidden layers in Bidirectional RNNs pass the information in the backward direction. Using Bidirectional RNN, inputs will run in twodifferent ways, one from past to future and the other from future to past.
Gated Recurrent Units are improved version of Recurrent Neural Networks that solves the vanishing gradient problem. To decide the information that should be passed to output, GRU uses two different vectorsnamely update and reset gates. The update and reset gates are trained to remember the information from previous epochs and remove the irrelevant information to generate predictions. The update gate alsocalle as long-term memory controls the flow of information into memory and reset gate also called as short-term memory controls the flow out of memory.
The update gate is powerful for training a model because it eliminates the risk of gradient vanishingproblem by helping the model understand how much of information from previous epochs is necessary to bepassed to the future. The reset gate eliminates the irrelevant information in the epochs. GRU has only one hidden state unlike LSTM that captures both the long term and short term dependencies. The Bidirectional Gated Recurrent Unit (BiGRU) has two GRUs, one takes inputin backward direction and other in forward direction.


Welcome!
Who are you? What is your experience with technology? What would you like to get out of this class?
What we'll learn
- High level overview of web development
- Basic web technologies
- Creating and deploying a small web app
- And plenty in between
What happens when you go to a website?
The World Wide Web
Domain Name Servers
A DNS takes a user friendly domain name and translates it into an Internet Protocol (IP) Address
The Server and You (the Client)
- The server is where the website code is stored
- The client consumes the code
- The client can make requests to the server
- The client can also do things on its own
Chrome, Firefox, Internet Explorer, and more
The browser is on the client computer to help consume and execute code. It will fetch from the website's server, then render the result.
Every browser handles code differently
Code versions, handling of resources
Coding on the server side?
- Ruby
- Java
- C/C++
- Databases! (storage)
The client side?
- HTML
- CSS
- Flash
- Javascript
Won't be storing any sensitive information
Putting them together is not always free
- Hosting a server
- Purchasing a domain name
- Time it takes to code, or hire
Plenty to choose from with plenty why
Server Side
- Speed
- Complexity
- Scalability
Client Side
- Ease
- Availability and Flexibility
- Browser compatibility
Download these:
Google Chrome
Atom
Hyper-text Markup Language
HTML elements are the building blocks of websites
Elements consist of tags and sometimes tag content
Browsers render these elements
Let's open the text editor
More tags!
Open your browser
Paragraphs
Whitespace in your text editor is only to make your code readable
- Extra spaces

- Break!
Headings
Some elements have preset sizes for text that rank importance
Attributes on Elements
This is a hyperlink element. They allow you to jump to other documents and web pages.
Images
Image tags also need attributes to define where the source of the image is.
Your image sources can be web pages or files on your computer
Your first task!
Choose a subject and create an HTML file that holds information about it. It should contain:
- Website title
- A heading with the topic
- A short paragraph explaining why you chose the topic
- An image that represents the topic
- Three (or more!) link to newly created HTML files related to your theme
Bonus: Add an additional image whose source is your computer
Tags within tags within tags...
You can nest elements in order to apply multiple conditions to them
This raises the importance of where you open and close the tags
Spice up your paragraph

Bonus: Nest some of these tags
Lists
Display a set of information simply with lists
This is an unordered list
- All unordered lists start with the tag

- Nested in this tag is what will denote each item of the list
- By default, the items are marked with a black circle
This is an ordered list
- All ordered lists start with the tag

- They use the same child tag to list items
- By default, the items are marked with numbers
- They can be given attributes to change the marker
Let's code
Add four more images and links to your web page
Group each image with a heading and a link
In one of your category files, make an ordered list and an unordered list about the category
Tables
Tables represent information that might be too complex to just display in a list.
They are made up of rows and columns, just like spreadsheets
Take a look at this nest
What information can you display in a table on your web page?
Adding style to elements
Web developers and designers use Cascading Style Sheets to make webpages visually pleasing
Why use CSS?
To describe the look and formatting of a document written in a markup language, eg. hypertext markup language (html)
We can change the colors, sizes, positions, and formatting of any of our HTML elements including the body, paragraphs, links, and images
You can add style directly onto an element
Block and inline elements

 and  are used to better group your elements and can apply style to their children
-
 is a block element. Block elements always start on a new line and fill the width of their space
-  is an inline element and neither starts on a new line nor fills the space

 is general used for grouping and containing elements
 is often used for applying group styling to text
Organize it better
You can add style scripts to your code to better organize your CSS
This can allow you to add more complexity
It also helps prevent code duplication
The structure
Identifying your element
Instead of grouping by tags, you can decide how you group your elements by adding attributes to your tags - IDs and Classes
ID vs Class
- Ids are unique
- Each element can have only one ID
- Each page can have only one element with that ID
- Classes are not unique
- You can use the same class on multiple elements.
- You can use multiple classes on the same element.
Backgrounds and Colors
background-color:grey
background-image:url("gradient_bg.png");
color:#FF5733
www.color-hex.com
www.paletton.com
Let's try jazzing up font again
Here are some more properties
text-align: center; //left; right;
text-decoration: overline; //line-through;
text-transform: uppercase; //capitalize;
font-size: 15em;
Displaying side by side
Sometimes you will need to force your block elements to act like inline elements
display: inline; //block; none;
inline-block allows you to take inline elements and give them width and height
Borders
To better see the space that our element is taking up, lets add a border
Margins and Padding
The margin property puts space around the entire element
The padding property puts space around the content of the element
Floating and clearing
You can apply float to an element to take the element out of the normal document flow
clear will help the parent elements re-establish the normal flow
Group things together
Group and center your headings with their respective images
Add additional text below and change some of it with CSS!
See and try your changes
Webkit Inspector can help you determine what changes you need inside of the browser
More positioning
We can better describe how an element should be positioned
More positioning
Elements position: static;
by default.
position: absolute;
- Place an element anywhere on the page
position: relative;
- Place an element in relation to another element


Hello guys! Welcome to Ett's website.
My favorite sites:
Instagram
Twitter
Reddit
LinkedIn
Tinder
My favorite site is
GitHub
!


Visual Studio 2010 downloads
Published Monday 30 November 2009
http://msdn.microsoft.com/en-us/vstudio/dd582936.aspx
So far, it looks good. Lots of nice touches like editing the start page MRU project list (at long last).
Unfortunately a lot of the really nice new stuff is Ultimate version only (all the new UML diagrams and architecture validation and intell-trace historical debugging). The coded UI tests/ UI test recorder seem to be premium edition only.
I suspect most of these (and the other high-end features) will need to drop to professional SKU to be widely accepted (as MSTest was when VS2008 came out) - oh well, looking forward to 2020 :)
Previously: First (28 Nov 2009)


--- layout: blank title: Ruby and Rails services ---
We do custom Ruby development in both greenfield and legacy projects. We can handle either the full stack of a web application – from databases to backend to frontend – or any part of them. We have vast experience in Ruby and Rails development, web frontend development (HTML, CSS, Javascript), and other development with Ruby.
We provide consulting in all the steps of the Bear Metal Treadmill.
Should you go with a single Rails app or perhaps split it up in a more service-oriented style? We have lots of experience with both and are happy to share it with you.
We have given diverse Rails related training for a decade. We have taught Ruby and Rails at major clients, from Fortune 500 companies to top universities and large conferences. There are few things we love more than people excited about learning new things.
We give tailor-made training classes among other about the following subjects:
You've learned the craft but still feel a bit unsure of the practice. Or maybe you hired a new programmer and want him to get started on the right foot. Or maybe you want you onboard an in-house team to an existing Rails project. We'll help you get up to speed with virtual or in-person pair programming. There are few better ways to get from a journeyman to master than to work together with a more experienced developer.
Do you need someone to make sure your application stays up even in the case you get hit by the perfect Reddit storm? Give us a call. We've operated large virtual server clusters and are experts in Chef.
You're done with gambling on app developers, and you want to hire a team of experts who understand your business.


afcajax Follow ∨ ···
4,591 Posts 685k Followers 148 Following
AFC Ajax This is the official Instagram account of Dutch football club AFC Ajax.
http://Ajax.ms/Miniklassieker.4,591 Posts 685k Followers 148 Following
AFC Ajax This is the official Instagram account of Dutch football club AFC Ajax.
http://Ajax.ms/Miniklassieker.


Anomaly Query Form Step 3
Continue to submit form to search the database for anomalies across various instruments and metadata.
Anomalies to Query
Anomalies chosen given options from chosen instruments: {{chosen_current_anomalies}}
Next


WITNESS LIST Intergovernmental Relations Committee March 6, 2001 - 9:00A SB 94 Registering, but not testifying: For: Young, Jenny Policy Analyst (TX Medical Ass.), Austin, TX SB 299 Registering, but not testifying: For: Patterson, Jerry Director of State Relations (American Red Cross), Austin, TX Wiltse, David Director of Disaster Services-Red Cross (American Red Cross), austin, TX SB 396 FOR: Croslin, Peggy Lobbyist (City of Buda), Austin, TX Long, Larry Asst. City Attorney (City of Odessa), Odessa, TX Registering, but not testifying: For: Igo, Shanna none listed (TX Municipal League), Austin, TX Johnson, Karen President of United Ways of TX (United Ways of TX), Austin, TX Against: Whalen, Ken Director of Legislative Affairs (TX Daily Newspaper Ass.), Austin, TX SB 522 Registering, but not testifying: For: Hernandez, Pat Municipal Judge (The TMRS Board of Trustees), Plainview, TX Igo, Shanna none listed (TX Municipal League), Austin, TX On: Anderson, Gary Exec. Director (TX Municipal Retirement System), Austin, TX SB 647 FOR: Fellers, Vance Merchant (Old Town Spring), Spring, TX SB 669 FOR: Navarro, Wilfred Retired (Houston Police Retired Officers Ass.), Houston, TX Registering, but not testifying: For: Grace, Cary Director-State & Federal Relations (City of Houston), Houston, TX Marticivc, Hans Police Officer (Houston Police Officer's Union), Houston, TX Perales, Erin General Counsel (Houston Municipal Employers Pension System), Houston, TX Sanders, Stephen Police Officer (Houston Police Patrolmen's Union), Houston, TX On: Long, David Executive Director-HMEDS (Houston Municipal Employees Pension), Houston, TX SB 738 FOR: Crutcher, Kirk City Attorney (City of Fritch), Amarillo, TX Wampler, Dee Mayor of Fritch (City of Fritch), Fritch, TX SB 820 Registering, but not testifying: For: Grace, Cary none listed (City of Houston), Houston, TX Myers, Donny Chairman-Houston Fire Pension Fund (City of Houston Fire Fighters), Spring, TX Patterson, Max Executive Director-Houston Firefighter's (Houston Firefighter's Retirement Fund), Spring, TX


Deep learning draws increasing attention from both academe and industries, which owns to its extraordinary deep architectures on learning meaningful representations of input data to significantly improve the performance of associated machine learning tasks. Existing deep learning approaches include the deep neural networks, deep convex net, PCA-Net, deep fisher net, deep forest and deep belief networks. One of the key issues of existing approaches is that the meaningful representations can be learned only when their hyper-parameter settings are properly specified beforehand, and general-parameters are precisely learned during the training process. Because deep learning is an emerging topic, not much researches have been dedicated to automatically set the hyper-parameters, and accurately find the globally optimal general-parameters. The problem in this regard can be formulated as optimization problems including the discrete optimization, constrained optimization, large-scale global optimization and multi-objective optimization, where evolutionary computation methods can play a crucial role. For example, in deep neural network-based deep learning algorithms, the types of building blocks are enumerated, the coefficients of the regularization terms are limited to one particular range, the numbers of weights are large and their values are required to be globally optimal. In addition, a higher performance deep learning algorithm typically requires more computational resources. To this end, the pursing of performance and computational resource are also conflicting objectives.
Evolutionary computation approaches, particularly genetic algorithms, particle swarm optimization and genetic programming, have shown superiority in addressing real-world discrete, constrained, large-scale and multi-objective optimization problems largely due to their powerful abilities in searching for global optima, dealing with non-convex and non-differentiable problems, finding a set of non-dominated solutions in a single run, and requiring no rich domain knowledge. However, most of existing evolutionary computation methods currently work only on relatively shallow architectures algorithms, and cannot provide satisfactory results for deep architectures.
The objectives of this task force are:
Topics of interest include but are not limited to:
Current Events:
Past Events:
Sichuan University, China<EMAIL_ADDRESS>Royal Melbourne Institute of Technology (RMIT), Australia<EMAIL_ADDRESS>University of Kent, UK<EMAIL_ADDRESS>Victoria University of Wellington, New Zealand<EMAIL_ADDRESS>Marley Vellasco, Pontifical Catholic University of Rio de Janeiro, Brazil
Harith Al-Sahaf, Victoria University of Wellington, New Zealand
Aaron Chen, Victoria University of Wellington, New Zealand
Ran Cheng, University of Birmingham, UK
Edgar Galvan, Maynooth University, Ireland
Liang Feng, Chongqing University, China
Min Jiang, Xiamen University, China
Yifeng Li, National Research Council, Canada
Amiram Moshaiov, Tel-Aviv University, Israel
Kourosh Neshatian, University of Canterbury, New Zealand
Xi Peng, Sichuan University, China
Yiming Peng, Victoria University of Wellington, New Zealand
Rongbin Qi, East China University of Science and Technology, China
Hong Qu, University of Electronic Science and Technology of China, China
Nasser R. Sabar,, La Trobe University, Australia
Pablo Mesejo Santiago, University of Granada, Spain
Kay Chen Tan, National University of Singapore, Singapore
Gary Yen, Oklahoma State University, US
Mengjie Zhang, Victoria University of Wellington, New Zealand


There's nothing where he used to lie My inspiration has run dry That's what's going on, nothings right, I'm torn I'm all out of faith, this is how I feel I'm cold and I am shamed lying naked on the floor Illusion never changed into something real I'm wide awake and I can see the perfect sky is torn I'm all out of faith, this is how I feel I'm cold and I'm ashamed bound and broken on the floor You're a little late, I'm already torn |


Revolutionizing
how people find jobs
how people find jobs
Una ujuzi? Una connections na unataka kuzi-expand? Poa! We'll send you free SMS job alerts. Kazi pap!
Una ujuzi? Una connections na unataka kuzi-expand? Poa! We'll send you free SMS job alerts. Kazi pap!


Partisan divide seen in how local news should be propped up
by Associated PressNEW YORK (AP) — The nation’s partisan divide is evident in how Americans believe the struggling local news industry should be propped up.
A survey by Gallup and the Knight Foundation released on Sunday finds Democrats much more willing than Republicans to see government funding help local news sources.
The survey finds that more than half of Americans aren’t aware of the crisis in an industry where more than 2,000 newspapers have closed in the last 15 years.
Six in 10 Americans see newspapers as an important source of civic pride. But many aren’t willing to open their wallets to show the pride. Forty-four percent of people surveyed said they’ve cut off a subscription to a news source within the past five years.


Y_normalized: Mix with shape (..., T, D).
Returns: Affiliations with shape (..., K, T).
affiliations = self.pi[..., None] * ComplexWatson.pdf(
Y_normalized[..., None, :, :],
np.copy(self.W[..., None, :], "C"),
self.kappa[..., None]
)
affiliations /= np.sum(affiliations, axis=-2, keepdims=True)
return affiliations
eps_style="where"
)
return self._predict(Y)
class ComplexWatsonMixtureModel:
Collects all functions related to the cWMM.


Graphic Designer + Illustrator
Hi! My name is Bob! I am a graphic designer and I can help with creating websites and logos.
Just send over your ideas and sketch and watch it come to life.
Education: University College of London, BA in Arts
Languages: English, Gaelic
Skills: Adobe Creative Cloud, Javascript, HTML, CSS, React, Bootstrap, Figma


Woman Loses 11 Stone After Being Turned Away From Roller Coaster On Dream Holiday
by Lucy ConnollyA woman who turned to comfort eating ‘to feel better about herself’ lost 11 stone after being turned away from a roller coaster ride on a dream holiday.
Nicola Wells, 41, from Larkfield, Kent, began overeating around a decade ago after she overcame another addiction – alcohol. So much so that between the ages of 31 and 35, her weight ballooned to 22 stone.
With junk meals from McDonald’s and KFC becoming a main part of her diet, Nicola would eat around 5,000 calories on a ‘good day’, and soon realised she had simply swapped one addiction for another.
Having started drinking in her 20s, Nicola was drinking all day, every day by the time she hit 30, losing her friends and her job. She knew she had to do something about it, so she checked herself into rehab for three months.
She moved back in with her parents and started the ‘next chapter of [her] life’, but it was there she started ‘eating for Britain’ to replace her alcohol cravings.
Nicola explained:
I’d eat huge portions of food at home or scoff unhealthy takeaways, before snacking on bars and bars of chocolate. I could easily eat three bars of Green and Blacks in one sitting.
I swapped one addiction for another. I stopped drinking and started eating for Britain. It got worse and worse and worse until I found myself squeezing into a size 24.
The 41-year-old said she’s had an addiction to food for ‘as long as [she] can remember’, adding, ‘I’d even go as far to call it an eating disorder’. She said while it was ‘no one’s fault’, she believes she was just born that way.
Despite knowing she had put on weight – her BMI increased to 48.3 in a matter of years, while the NHS recommended range is 18.5-25 – it wasn’t until a dream holiday to America that she decided things had to change.
Nicola travelled to Florida’s Universal Studios with her family in 2014 and couldn’t wait to visit The Wizarding World of Harry Potter, so she was horrified when she wasn’t allowed on the roller coasters because of her weight.
She recalled:
Before I stepped on the ride, I was asked to get in the test seat to see if I would fit. They tried to fasten me in, but there was no way it was going to happen. That’s when I had the realisation about my weight. I’d been in denial up until then.
I was so humiliated, I knew I couldn’t go on like that. I just kept thinking, ‘This is really, really bad’. After being turned away, I found my parents and told them what had happened and they could see just how upset I was.
Nicola had to watch in embarrassment as her brother Rob, 28, enjoyed the ride alone, and made the decision there and then that she would take drastic action to lose weight.
As soon as she got home, Nicola started to research gastric bypass surgery because she ‘knew’ this was the only way for her to lose the weight. ‘I’ve tried all the fad diets and they just don’t work,’ she explained.
However, the 41-year-old wasn’t eligible for the procedure on the NHS because she didn’t have any illnesses related to her weight, so she paid for the procedure herself. Less than two months after her holiday, Nicola was booked in for a gastric bypass, in which the patient has the top part of the stomach joined to the small intestine to make them feel full more quickly.
After borrowing the £10,500 for the surgery from her parents, Nicola went in for the procedure on July 1, 2014, and hasn’t looked back since. In less than a year, she had lost seven stone.
Eating just 1,400 calories a day, by swapping junk food and chocolate for chicken salads and fresh fruit, Nicola has since dropped down to 11 stone and has never felt happier.
Not only that, but she transformed from a self-confessed couch potato into a gym bunny, having trained to be a qualified personal trainer in October last year. She now works part-time at her local gym.
Saying she wants to ‘give people inspiration’, Nicola acknowledges she’s had help to get to where she is, but stresses she wants to help people keep the weight off.
The 41-year-old describes every day as a ‘blessing’, saying her biggest fear is turning back into the person she once was because ‘she was miserable’.
A Broadcast Journalism Masters graduate who went on to achieve an NCTJ level 3 Diploma in Journalism, Lucy has done stints at ITV, BBC Inside Out and Key 103. While working as a journalist for UNILAD, Lucy has reported on breaking news stories while also writing features about mental health, cervical screening awareness, and Little Mix (who she is unapologetically obsessed with).


Grand Magister Valdis:
Excellent! I'll spread the word of your mighty Clan's defeat of the Hestia, Guardian Deity of the Hot Springs.

"I want to quit this mission."


Web Application Development
This course provides a basic understanding of the methods and techniques of developing a simple to moderately complex web site. Using the current standard web page language, students will be instructed on creating and maintaining a simple web site. After the foundation language has been established, the aid of a web editor will be introduced. A second web-based language will be included to further enhance the web sites.
Chapter 02. Website Design Principles
Hands on Exercises
- Browse the Web for examples of Web sites that exhibit good Web design.
- Using a screen-capture program, such as the snipping tool in Windows 7.0, capture Web pages from the Web site that show two levels of information. For example, capture the main page of a Web site and a secondary page. Describe how the layouts for the two pages suit their information types and the needs of their users.
- Use the graphic tools in a word-processing program or presentation program to include screen callouts with the Web page images you capture. Th e callouts should indicate the unifying characteristics of the pages, such as shared colors, fonts, graphics, and page layout. (A callout is an arrow or line that connects to explanatory text. For example, Microsoft Word includes a callout tool in its collection of shapes.
- Indicate the areas of active white space and passive white space.
- Describe whether the design of the site is appropriate for the content.
- Browse the Web for examples of poor Web design on mainstream Web sites.
- Using a screen-capture program, capture Web pages from the Web site that show two levels of information. For example, capture the main page of a Web site and a secondary page.
- Indicate with screen callouts the jarring or distracting inconsistencies of the site, such as abrupt changes in any design elements, including theme and layout.
- List detailed recommendations for improving the site design.
- Write a short essay critiquing a Web site's design. Describe the structural layout of the site and determine whether information is presented clearly and is easily accessible.
- Browse the Web for sites that use unique navigation methods. Write a short essay describing why the method is or is not successful, or prepare a short slide presentation that you can present to your class.
- Find a Web site that you think needs improvements in itsÂ design.
- Print two pages from the site.
- Make copies of the originals, and set the originals aside.
- Using scissors, cut out the main elements of each page. Rearrange the elements and paste them in a design you believe improves the site.
- Compare and contrast the original with your improved design.
- Test cross-browser compatibility.
- Make sure you have recent versions of at least two current browsers installed on your computer.
- Browse a variety of Web sites. Make sure to view various pages of the sites in the different browsers.
- Write a detailed description of how the various sites appear in the browsers you have chosen. Look for text, layout, and graphic inconsistencies
- Test accessibility software. Download a trial version of one of the following screen-reading programs. Navigate to different Web sites, and use the tool to read the site. Close your eyes while listening. Write a short essay describing your experience.
- Jaws at http://www.freedomscientific.com/Downloads/JAWS jaws-downloads.asp
- Window Eyes at www.gwmicro.com/Window-Eyes/ Demo
- Note:Record a video of your experience using your cellphones. This will serve as evidence that you did the activity.
>


--- title: Israel shot 103 date: 2004-04-13T00:00:00.000Z filepathOriginal: photos/IMG0101.jpg filepathThumbnail: photos/IMG0101.thumbnail.jpg filepathPreview: photos/IMG0101.preview.jpg ---
The first buildings we'd seen in three days, after desert survival


{% extends "layout.html" %} {% block content %}
* {{message}}
{% endfor %}
Your Catalog |
|---|
{{ c.name }}
Delete |
Edit
|
| No Data |
Last 10 Added recently by Community |
{% if last_catalog == None %}
We fresh, still not have Community yet.Become First one |
{% else %}
{% if last_catalog.count() %}
{% for c in last_catalog %}
|---|
{{ c.name }}
|
| No Community Data |
Join us and Make your Catalog |


Simple use:
Set model from controller
{{tinymceModel}}
Inline:
Set model from controller
{{tinymceModel}}


How to configure AR scene...
1. Find a clear, well lit surface.
2. Scan area until grid appears.
3. Tap grid to start game.
Got it!
Fire!


It is preferrable not to use expressions with side effects in monadic transformations (
map
,
flatMap
and
filter
). Consider to move them to a separate
foreach
pass.


Multidimentional arrays are evil.
I see a lot of noobs get sucked into the vortex that is multidimentional (hereon MD) arrays. MD arrays are a "feature" of C/C++ which allow you to use multiple indeces when looking up things in an array. A typical use is to have a 2D array to represent a grid or a game map with 1 index for X coords and another for Y coords. Another common usage is for a 'Matrix' class or somesuch.
The thing these coders don't know (or don't realize) is that MD arrays suck. They're syntax poison. They're confusing, hard to work with, and (depending on the implementation) may actually consume more memory and yield worse performance.
After seeing more and more posts about MD arrays pop up on these forums, I've decided to sketch out this article to shine some light on MD arrays, and provide a reasonable alternative.
-----------------------------------
MD Array Flavor 1: Straight
The most obvious way to make an MD array is just a straight allocation:
1 2 3
|
int evil[5][7];
evil[3][2] = 5;
| |
Seems harmless enough. And it is, for the most part. This is the friendliest flavor of MD arrays.
However even with this form, problems creep up. What if you need to pass this array to another function? The way you'd do it is like so:
1 2 3 4 5 6 7 8
|
int func(int p[][7])
{
p[2][3] = 1;
}
//...
func(evil);
| |
This will pass 'evil' by pointer to the 'func' function. This isn't all that horrible... but one of its limitations is that it will only accept an array of [x][7]. What if you want to call the function with an MD array of a different size... like
int evil[4][5];
? Bottom line is you can't. You'd have to take the MD array out of the function completely and turn it into a 1D array and pass the width as a seperate parameter:
1 2 3 4 5 6 7 8 9 10 11 12 13 14
|
int func2(int p[], int width)
{
// p[2][3] = 1; // no good anymore, 1D array
p[ (2*width) + 3] = 1; // the 1D apprach to do the same thing
/* It's worth noting here that the above (y*width)+x calculation is basically what
MD arrays compile to anyway. The compiler just hides it from you with MD arrays.
Or at least it's true with Straight Flavor MD arrays. Other flavors are more sinister.
*/
}
//...
int evil[4][5];
func2(evil[0],5); // ugly
| |
You might notice I marked that last line as ugly. It's because it is. It is confusing, has unclear syntax, and it is very easy to make a mistake and pass the wrong value as the width.
The other problem with this flavor is that
it is completely incompatible with other MD flavors. As you'll soon see.
MD Array Flavor 2: Nested New (dynamic)
Once people graduate from straight MD arrays, they'll start wondering how to make the size of their arrays dynamic. This is where the dreaded "nested new" technique comes in:
1 2 3 4 5 6 7 8 9 10 11 12
|
// let's say we want to dynamically make int[Y][X]:
int** superevil = new int*[Y];
for(int i = 0; i < Y; ++i)
superevil<i> = new int[X];
// now we can do this:
superevil[2][3] = 1;
// but cleanup is just as ugly as allocation:
for(int i = 0; i < Y; ++i)
delete[] superevil<i>;
delete[] superevil;
| |
Note that this actually makes a pointer to a pointer, and not really a straight 2D array. And while "superevil" and "evil" (from the previous section) are both accessed with [double][brakets], they both function very differently under the hood.
Nested New MD arrays are inefficient. In addition to allocating all the space for the normal 2D array, you're also allocating space for POINTERS. Not only that, but in order to access any element in the array, you have to dereference
two pointers!
And really... look at the messy allocation/destruction code that's required.
And let's go back to calling functions... let's take our two functions from the previous section:
1 2
|
int func(int p[][7]);
int func2(int p[], int width);
| |
How would you call those functions with 'superevil'?
Guess what. You
can't. That's right... you're hosed. Nested New arrays are not really 2D arrays in the same sense that straight arrays are. Straight MD arrays only require 1 dereference, but nested new requires 2 dereferences. In order to pass this to a function, you'd need to make another one:
|
int func3(int** p,int width);
| |
But now you might notice that func3 doesn't work with straight arrays. They're just totally incompatible.
The one saving grace to the nested new approach is that it doesn't require the entire MD array to be contiguous, which means you have a better chance of getting the memory you need if memory is badly fragmented. But this mild benefit is seldom applicable, and is overshadowed by its faults.
MD Array Flavor 3: Vectors of Vectors
The more STL savvy coder will opt for a vector-of-a-vector approach to MD arrays, rather than Nested New:
1 2 3 4
|
// to make a [5][6] array:
vector< vector<int> > stillevil( 5, vector<int>(6) );
stillevil[2][3] = 1;
| |
Not a lot to say about this one. Like Nested New it's incompatible with other MD array flavors. If you need to pass this array to a function, none of the above functions will work.
Also, like Nested New, it's inefficent in that is requires additional memory and has double indirection.
On the plus side, you don't need cleanup code, and the allocation code is not as ugly (but still pretty ugly!)
Killing MD: 1D Mimics
An MD array can be simulated in a 1D array as long as you know the dims. For example, if you want a 2x3 array, it would conceptually look like the following:
0 1
2 3
4 5
This array has 6 elements (2 * 3). And you'll notice that each row starts with a multiple of the width (2). Therefore you can simulate a 2D array in a 1D array like so:
1 2 3 4 5
|
// int evil[5][6]; // desired 2D array
int mimic[5*6]; // mimicing 1D array
// evil[2][3] = 1; // desired [2][3] index
mimic[ (2*6) + 3] = 1; // mimiced [2][3] index
| |
All that math looks kind of dumb and inefficient. Really, though, the compiler does all that math for MD arrays too, so this isn't any less efficient.
So you might be asking yourself... "what is the benefit of this?"
Well, unlike MD array flavors which are all different and totally incompatible, the respective
1D flavors are all compatible!
Consider the following:
1 2 3 4 5 6 7 8 9 10 11 12 13
|
int func2(int p[], int width); // remember this function?
// it'll work with all flavors of mimicing 1D arrays!
int a[ 5 * 6 ];
func2( a, 6 ); // works
//---
int* b = new int[5*6];
func2( b, 6 ); // works
//---
vector<int> c(5*6);
func2( &c[0], 6 ); // works (but is a little ugly, granted)
| |
See how graceful it all is?  This is the first step to killing MD arrays. The next step is even more fantastic.
Killing MD: Abstracted array classes
So while 1D arrays are easier to work with, They do suffer from a few problems:
- look up requires you to know the dims of the array (can't do
y*width
without knowing the width).
- look up syntax is ugly and easy to botch. It only gets worse as you get into larger dimensions. Imagine a mimiced 3D array:
z*width*height + y*width + x
.
C++ provides an excellent solution to this problem:
classes. Just make an Array2D/Matrix/whatever class to wrap all of this behavior.
(Or don't -- there are probably many premade ones available online that you can use, I never actually bothered looking. Boost probably has one, I'd imagine -- I'll have to research this more some day. If you are reading further in this article I'm going to assume you don't have / don't want to use such a library and want to write your own)
While you can't overload the [bracket] operator to take two parameters*, you
can overload the parenthesis operator. So syntax is a little different:
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// int bad[4][5];
Array2D<int> good(4,5);
// bad[1][2] = 3;
good(1,2) = 3;
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(* Yes you can overload the brakets and return a weird type to make double brakets work, but it causes all sorts of problems and defeats all the elegance of using a fully contained class)
So how do you make this Array2D class? As long as you don't want bells and whistles, it can be very simple.
Here is a very simple class with no bells or whistles:
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template <typename T>
class Array2D
{
public:
// constructor
Array2D(unsigned wd,unsigned ht)
: nWd(wd), nHt(ht), pAr(0)
{
if(wd > 0 && ht > 0)
pAr = new T[wd * ht];
}
// destructor
~Array2D()
{
delete[] pAr;
}
// indexing (parenthesis operator)
// two of them (for const correctness)
const T& operator () (unsigned x,unsigned y) const
{ return pAr[ y*nWd + x ]; }
T& operator () (unsigned x,unsigned y)
{ return pAr[ y*nWd + x ]; }
// get dims
unsigned GetWd() const { return nWd; }
unsigned GetHt() const { return nHt; }
// private data members
private:
unsigned nWd;
unsigned nHt;
T* pAr;
// to prevent unwanted copying:
Array2D(const Array2D<T>&);
Array2D& operator = (const Array2D<T>&);
};
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That's all there is to it!