{% include 'basic/Head.html' %}
Namaste ! I am a student with profound interest in building things. I have a keen interest in the field of web development. This blog is developed as a part of my full stack learning. I have a handful of experiences at data structures and algorithms. Competetive programming is a thing that I enjoy at times.
I prefer using Python as my primary language. I use C++ along with STL for competetive programming. Also, I've learned Java 8, precisely Swing as a part of my school curriculum.
Among web technologies, I use HTML5, CSS3, and JavaScript for most of my project frontend work. I often use Bootstrap and VueJS to enhance my web apps. For backend purpose,I use python based technologies like Flask and Django. I have some experience in PHP based backend tech (It was my first project) and MySQL for database requirements.
I was selected among the top 10% of the programmers at Hackerearth. Apart from this,I also was the national topper of Cyber Olympiad (India) in 2018.
Still, I'm a 17 year old, exploring various dimensions of interests. I have tried my hands on a few other works including translation(in French and German), transcripting, blogging, photoshop, inkspace, etc.
Apart from my daily endeavor to be a better developer, I can be found reading obscure books, solving my rubik's cube, vibing to Ed and Taylor or experimenting with design tools.
Find me at:
I would love to help anyone out with problems related to programming or web concepts.Denting brains since 2002.


We should have a new newspaper out every couple years I guess.
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Precompiler Guidelines
Follow these guidelines when precompiling DB2 programs.
Use the Appropriate DBRM Library
Most shops allocate DBRM libraries. These libraries must be set up as partitioned data sets with 80-byte records.
Retain DBRMs Only When Absolutely Necessary
Although the DBRM produced by the precompiler must be placed in a partitioned data set, DBRMs sometimes do not need to be retained. If the DBRM will be temporary due to the replication of program preparation during the testing process, it can be written to a temporary PDS. When the program is out of the testing phase, the DBRM can be written to a permanent PDS before it is migrated to production status.
Name the DBRM the Same As the Program
Ensure that the DBRM is named the same as the program from which it was created. This eases the administration of objects created and modified by the program preparation process.
Precompile Only When Required
Precompilation is not required by BASIC and APL2 programs that contain SQL statements. Refer to the appropriate BASIC and APL2 programming guides for additional information about these environments.
Use DEC31 to Impact Decimal Precision
DB2 supports decimal precision of either 15 or 31, depending upon the precompiler option. If decimal numbers with a precision greater than 15 are to be utilized, you must specify the DEC31 precompiler option.
When you're using this option, examine the application program to verify that the host variables can accommodate 31-digit decimal precision.
Using LEVEL to Avoid Binding
LEVEL is a precompiler option that can be used when a program is modified but the SQL in the program has not changed. LEVEL is specified as a character string to be used by DB2 for consistency checking in place of the timestamp token. By precompiling a DBRM with the same level as before, a BIND can be avoided. You do not need to bind because SQL has not changed allowing DB2 to use the same access paths and the program to use the same package or plan as before.
Using LEVEL, a programmer can change his program without modifying the embedded SQL, and avoid worrying about having to bind. But care must be taken to ensure that the SQL is not changed. If the SQL is changed but a bind does not occur, unpredictable results can occur.
If LEVEL is used, DB2 will use the level as the consistency token and the default for version (if no version is specified).
CAUTION
Avoid using LEVEL as much as possible. Unpredictable and undesirable results can occur when using the LEVEL option improperly.
Specify the Version with Care
Remember, you basically have two options for specifying the version name. Versions can be automatically defined by DB2 specifying VERSION(AUTO) or explicitly named using the VERSION(name) precompile parameter. When versions are automatically assigned by DB2, a timestamp will be used.
If you explicitly name your versions, they will be more difficult to implement but easier to administer. The difficult part is providing a mechanism to ensure that programmers always specify an appropriate version when precompiling a program.
On the other hand, if you use automatic versioning, packages are easier to implement because DB2 is automatically naming the version for you, but much more difficult to administer. The administration difficulty occurs because the auto timestamp version is unwieldy to manually enter when package administration is necessary. Consider this when deciding how to name versions at your shop.
If your shop does not have an automated means of administering versions, consider explicitly specifying the version when precompiling a program.
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 Well, yes, unless it's in your nose. " date: 2021-01-12 18:32:13 +0530 background: '/assets/images/Health.jpg' published: true categories: [ Health ] tags: [ health, nose, hair, nose hair, trimming, safe, controversial ] ---
First of all, nose hair deserves a little love. Both the larger, visible hairs as well as the ones too tiny to see (known as cilia) provide an important support to the immune system.
They play a front-line role in keeping dust, germs, and debris from entering the body, thereby reducing exposure to allergens.
A 2011 study divided 233 participants up according to the density of their nose hairs — no or less hair, moderate hair, or a lot of hair.
Researchers found that those with the least amount of hair had a significantly greater rate of asthma, combined with seasonal allergies, than those with more hair (via Healthline).
Nose hair also helps retain moisture in the air we breathe.
Trimming tools are the safest way to go. The lowest-risk and cheapest way to remove unwanted hair is to use either tiny scissors designed for the purpose, or an electric nose-hair trimmer.
Laser hair removal is a permanent treatment, but is controversial for nose hair since it can harm the mucous membranes inside the nasal cavity.
Waxing or plucking nose hair can actually be dangerous, and is discouraged by doctors. Not only are these methods painful, but because they remove the entire hair follicle, it leaves the sensitive nasal tissue susceptible to infection and swelling.
Julie Russak, M.D., of Russak Dermatology Clinic in NYC, explains (via Women's Health), "As a result, you're very susceptible to allergy attacks, sinusitis, and respiratory infections. If a nasal infection were to travel upstream and enter the junction where other veins join, it could pose a serious health risk since it now presents a passage for bacteria directly into the brain."


Statistics
Solves over time
This chart (and the ones below) treat solving a supermeta as solving all the remaining puzzles in the round.
Solves vs. Guesses
The emoji is scaled with the size of the team.
Solves vs. Team size
Infinite solves
Animated Graph
Animated Solve Graph over Time
Solve data
Here is a CSV file of all answer submissions during the hunt, and here is a CSV file of all the unlock, find, and solve events. The first time column represents unlock time, the second represents found time, and the third (when present) represents solve time.


--- layout: page title: "Moments" description: "masonry" active: gallery album-title: "流光瞬息" images: - image_path: "http://oss.zoyeal.com/webfile/pics/sky.jpg" caption: 从济南到深圳的飞机上 exif: June 2019 | Flight to Shenzhen, China - image_path: "http://oss.zoyeal.com/webfile/pics/pic2.jpg" caption: 色达佛学院 exif: December 2016 | Seda, China - image_path: "http://oss.zoyeal.com/webfile/pics/pic14.jpg" caption: 黄昏 exif: August 2016 | Newcastle, United Kindom - image_path: "http://oss.zoyeal.com/webfile/pics/pic9.jpg" caption: 巴约之家 exif: July 2016 | Barcelona, Spanish - image_path: "http://oss.zoyeal.com/webfile/pics/pic11.jpg" caption: On The Road exif: July 2016 | Belgium - image_path: "http://oss.zoyeal.com/webfile/pics/pic13.jpg" caption: 水上城市 exif: July 2016 | Amsterdam, Netherlands - image_path: "http://oss.zoyeal.com/webfile/pics/pic7.jpg" caption: Sunset of the Exhibition Park exif: June 2016 | Newcastle, United Kindom - image_path: "http://oss.zoyeal.com/webfile/pics/pic8.jpg" caption: Ferris wheel exif: June 2016 | Newcastle, United Kindom - image_path: "http://oss.zoyeal.com/webfile/pics/pic5.jpg" caption: 海豚 exif: March 2016 | Belford, United Kindom - image_path: "http://oss.zoyeal.com/webfile/pics/pic4.jpg" caption: Ben山脚下的村庄 exif: March 2016 | Fort William, United Kindom ---


Nice to have you here {{user.name}}!
Let's rock and roll!
Hello world! This is dj-kubelet.
Press OK to log in with Spotify.
Microsoft❮R❯ Windows DOS ❮C❯ Copyright Microsoft Corp 1990-2001. C:\WINDOWS\SYSTEM32> type kubeconfig {{ user.kubeconfig }}


Select Process name:
Select
Status
Count
Completed
Started
Halted
Cancelled
Search By :
---Select---
Siebel Circuit Id
Project manager
Client name
Contractor
ServiceType


File Directory - Confirm All Deletions
If you set this radio button, Moderate will ask you to confirm every time you delete a file from a file directory, regardless of whether that file is an orphan or has an entry in the file list.


Documentation : Why only Windows?
People sometimes suggest that Sumatra should be available for some other operating system: Linux, Mac, Android etc.
This won’t happen.
The first reason is that current developers don’t know how to write software for those other platforms.
Second reason is that it would mean writing the whole program from scratch for a new platform.
Sumatra is tightly integrated with and optimized for Windows. Making a version for another operating system is not a matter of making a few changes here and there but writing all the UI code from scratch i.e. redoing years of development that went into Sumatra.
SumatraPDF is an open-source project so anyone is free to take our source code and use as much of it as they want to create equivalent for some other operating system, but we’re unlikely to do it ourselves.


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ExitCode | exitcode () |
| | Generate an exit condition for the problem, either full or reduced precision.
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void | loadSolution () |
| | Loads back the solution of the node with best statistics.
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void | storeSolution () |
| | Stores the solution of the node with best statistics so far.
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void | initializeRootNode () |
| | Initialize the root node of BnB solver.
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int | selectNodeToExplore () |
| | Function to select the node to be explored.
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double | getProblemLowerBound () |
| | This function computes the lower bound out of the nodes explored so far.
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int | optimilityCheck (int node_id) |
| | Convergence check in BnB routine search.
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void | createBranches (int node_id) |
| | Function to create branches for the new variable to explore.
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void | updateNodeBounds (int node_id) |
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void | selectPartitionVariable (int &partition_id, double &partition_val) |
| | Function to select the variable to explore to refine the space search.
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void | updateProblemData (const Eigen::Ref< const Eigen::VectorXi > &bool_node_id) |
| | function to update problem data from node to explore
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ConicProblem & | getProblem () |
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OptimizationInfo & | getInfo () |
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ConicProblem * | conic_problem_ |
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OptimizationInfo | optimization_info_ |
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ExitCode | opt_status_ |
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OptimizationVector | opt_ |
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std::vector< node > | nodes_ |
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Eigen::VectorXi | binvars_ids_ |
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Eigen::VectorXi | binvars_vec_id_ |
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Eigen::MatrixXi | binvars_mat_id_ |
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int | nbin_vars_ |
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int | iteration_ |
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int | node_id_ |
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double | prob_upper_bound_ |
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double | prob_lower_bound_ |
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| ExitCode solver::NcvxBnBSolver::optimize |
( |
| ) |
|
Create branches for the problem
Updating problem bounds
Decision on exploration node
The documentation for this class was generated from the following files:


Process your XLSX file into a
formDef.json
file.
Drop an XLSX file here to convert to JSON.
Select an XLSX file from which to generate a
formDef.json
file
Save to File System
Save
Output (to be saved as
formDef.json
):


| Units | Section | Mini | Days | Begin | End | Teaching Location | Bldg/Room | Instructor | |
|---|---|---|---|---|---|---|---|---|---|
| 9.0 | A | MTR | 06:30PM | 07:50PM | Pittsburgh, Pennsylvania | GHC 4102 |
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| Lec 1 | UT | 10:30AM | 11:50AM | Doha, Qatar | CMB 2147 |
|
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| W | R | 10:30AM | 11:20AM | Doha, Qatar | CMB 2147 |
|
An introduction to the fundamental ideas and models underlying computing: finite automata, regular sets, pushdown automata, context-free grammars, Turing machines, undecidability, and complexity theory.
| Section | Restriction |
|---|---|
| W | Some reservations are for Students in QCS |


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Days Without End by Sebastian Barry
The Sea House by Esther Freud
The Dressmaker by Rosalie Ham
The Last Hours by Minette Walters
The Sunrise by Victoria Hislop
Monsters by Sharon Dogar
All Out: the no-longer secret stories of queer teens throughout the ages, by Saundra Mitchell (ed.)
Daisy Jones & The Six by Taylor Jenkins Reid
Enchantée by Gita Trelease
The Steep & Thorny Way by Cat Winters
Atonement by Ian McEwan
Bone Talk by Candy Gourlay
The Electrical Venus by Julie Mayhew
The Light Between Oceans by M. L. Stedman
The Words in My Hand by Guinevere Glasfurd
Stranger by Keren David
Lady Mary by Lucy Worsley
The Cure for Dreaming by Cat Winters
Crooked Heart by Lissa Evans
Orphan Monster Spy by Matt Kileen
A Sky Painted Gold by Laura Wood
The Guernsey Literary & Potato Peel Pie Society by Mary Ann Shaffer & Annie Barrows
The Muse by Jessie Burton
Longbourn by Jo Baker


STΔЯKTΘKΞΠ
We are still in development phase. Want to contribute to the development? Mail us at<EMAIL_ADDRESS>Feel free to reach out to us on our facebook, instagram or twitter.
We are still in development phase. Want to contribute to the development? Mail us at<EMAIL_ADDRESS>Feel free to reach out to us on our facebook, instagram or twitter.


childrens cake
Our traditional doll cakes feature layers of
frothy sponge and fresh cream, and a butter
cream gown of your own design.
Our traditional doll cakes feature layers of
frothy sponge and fresh cream, and a butter
cream gown of your own design.
Celebrate in style with our mud cake. Our unique
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Hamilton seals his sixth F1 title at US Grand Prix
AUSTIN, Nov 4 — Lewis Hamilton secured his sixth Formula One world championship yesterday in a US Grand Prix won by Mercedes team mate Valtteri Bottas.
The 34-year-old Briton finished second at the Circuit of the Americas to become only the second driver after retired Ferrari great and seven-times champion Michael Schumacher to win six titles.
Red Bull’s Max Verstappen took third place in the race while Ferrari’s Charles Leclerc finished fourth and took an extra point for fastest lap.
A sellout crowd, that included both of Hamilton’s parents, arrived early at a sun-kissed circuit outside Austin expecting to see a coronation and were not disappointed as the Briton battled Bottas to the death.
Much of the drama in the championship chase had already been removed with Hamilton in position to clinch the title without even scoring a point.
But the 34-year-old showed why he is one of most competitive and greatest racers of all-time as he tried to crown his championship with a seventh US Grand Prix victory.
He fell just short of crossing the finishing line first but nothing could stop Hamilton from sealing another driver’s title.
“It’s just overwhelming if I’m really honest. It was such a tough race today,” Hamilton told reporters.
“Valtteri did a fantastic job so huge congratulations to him.
“Today I just really wanted to deliver the one-two for the team. I didn’t think the one-stop was going to be possible but I worked as hard as I could.”
The math was as simple as it was daunting for Bottas, Hamilton would secure the title by finishing eighth or better but would not need any points yesterday to retain the championship if the Finn did not win the race.
Any thoughts that Hamilton might approach the race with some caution were quickly removed as he went on the attack right from the start, shooting from fifth to third before the first lap was completed.
Bottas got away from pole position cleanly with Red Bull’s Max Verstappen and Hamilton in hot pursuit.
With Hamilton on a one-stop strategy and Bottas and Verstappen on two, the Briton took over the lead after his two challengers made their second pit-stops.
Victory now depended on whichever driver could make their tyres last.
With four laps to go and Hamilton grimly hanging on, there was nothing he could do to stop Bottas sweeping past him to take the win.
“My dad told me when I was six or seven years old never to give up and that’s kind of the family motto,” said Hamilton.
“I was pushing as hard as I could, I was hopeful that I might be able to win today but didn’t have it in the tyres unfortunately.” — Reuters


Experienced graphic designer with unique web development skills and a passion for creative innovation
- Crafted a hybrid e-commerce/social platform UI geared towards shoppers and brands
- Implemented a simple user flow for an easier and more seamless shopping experience
- Designed UI/UX for a complex data-driven application
- Utilized Angular.js and wrote custom directives for unique use cases
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- Architected a simple system that can be used powerfully by buyers and sellers
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- Worked with a team of designers to reach a common goal
- Developed design concepts for current and forthcoming apparel
- Created quality designs in an often time-sensitive window


Colorful
Fundamental sorting algorithms visualized with colors
Choose two sorting algorithms
Any sorting algorithm than can operate on integers can sort colors. You can learn more about sorting algorithms
here.
Algorithm A
Merge Sort
Quicksort
Algorithm B
Merge Sort
Quicksort
Choose a color sorting method and speed
There are lots of ways to sort colors, you can learn about the math behind them
here.
Sorting Method
Hue
Red
Luminosity
Visualization Speed
Slow
⚡
Medium
⚡⚡
Fast
⚡⚡⚡
Randomize
Sort


Class FeatureDefn
- java.lang.Object
-
- org.gdal.ogr.FeatureDefn
-
public class FeatureDefn extends java.lang.Object
Definition of a feature class or feature layer.The FeatureDefn class is a binding for the C++ OGRFeatureDefn class.
This object contains schema information for a set of Feature. In table based systems, a FeatureDefn is essentially a layer. In more object oriented approaches (such as SF CORBA) this can represent a class of features but doesn't necessarily relate to all of a layer, or just one layer.
This object also can contain some other information such as a name, the base geometry type and potentially other metadata.
It is reasonable for different translators to derive classes from FeatureDefn with additional translator specific information.
-
-
Constructor Summary
Constructors Constructor Description FeatureDefn()
Constructor.FeatureDefn(java.lang.String name)
Constructor.
-
Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description void
AddFieldDefn(FieldDefn defn)
Add a new field definition.void
AddGeomFieldDefn(GeomFieldDefn defn)
void
delete()
int
DeleteGeomFieldDefn(int idx)
boolean
equals(java.lang.Object obj)
int
GetFieldCount()
Fetch number of fields on this feature.FieldDefn
GetFieldDefn(int ifield)
Fetch field definition.int
GetFieldIndex(java.lang.String name)
Find field by name.int
GetGeomFieldCount()
GeomFieldDefn
GetGeomFieldDefn(int i)
int
GetGeomFieldIndex(java.lang.String field_name)
int
GetGeomType()
Fetch the geometry base type.java.lang.String
GetName()
Get name of this FeatureDefn.int
GetReferenceCount()
Fetch current reference count.int
hashCode()
int
IsGeometryIgnored()
Determine whether the geometry can be omitted when fetching features.int
IsSame(FeatureDefn other_defn)
int
IsStyleIgnored()
Determine whether the style can be omitted when fetching features.void
SetGeometryIgnored(int bIgnore)
Set whether the geometry can be omitted when fetching features.void
SetGeomType(int geom_type)
Assign the base geometry type for this layer.void
SetStyleIgnored(int bIgnore)
Set whether the style can be omitted when fetching features.
-
-
-
Constructor Detail
-
FeatureDefn
public FeatureDefn(java.lang.String name)
Constructor.The FeatureDefn maintains a reference count, but this starts at zero. It is mainly intended to represent a count of Feature's based on this definition.
- Parameters:
name
- the name to be assigned to this layer/class. It does not need to be unique. and may be null.- Since:
- Java bindings 1.7.0
-
FeatureDefn
public FeatureDefn()
Constructor.The FeatureDefn maintains a reference count, but this starts at zero. It is mainly intended to represent a count of Feature's based on this definition. The FeatureDefn will be unnamed.
-
-
Method Detail
-
delete
public void delete()
-
equals
public boolean equals(java.lang.Object obj)
- Overrides:
equals
in classjava.lang.Object
-
hashCode
public int hashCode()
- Overrides:
hashCode
in classjava.lang.Object
-
GetName
public java.lang.String GetName()
Get name of this FeatureDefn.- Returns:
- the name.
-
GetFieldCount
public int GetFieldCount()
Fetch number of fields on this feature.- Returns:
- count of fields.
-
GetFieldDefn
public FieldDefn GetFieldDefn(int ifield)
Fetch field definition.- Parameters:
ifield
- the field to fetch, between 0 and GetFieldCount()-1.- Returns:
- a pointer to an internal field definition object. This object should not be modified or freed by the application.
-
GetFieldIndex
public int GetFieldIndex(java.lang.String name)
Find field by name.The field index of the first field matching the passed field name (case insensitively) is returned.
- Parameters:
name
- the field name to search for.- Returns:
- the field index, or -1 if no match found.
-
AddFieldDefn
public void AddFieldDefn(FieldDefn defn)
Add a new field definition.To add a new field definition to a layer definition, do not use this function directly, but use Layer.CreateField() instead.
This method should only be called while there are no Feature objects in existence based on this FeatureDefn. The FieldDefn passed in is copied, and remains the responsibility of the caller.
- Parameters:
defn
- the definition of the new field.
-
GetGeomFieldCount
public int GetGeomFieldCount()
-
GetGeomFieldDefn
public GeomFieldDefn GetGeomFieldDefn(int i)
-
GetGeomFieldIndex
public int GetGeomFieldIndex(java.lang.String field_name)
-
AddGeomFieldDefn
public void AddGeomFieldDefn(GeomFieldDefn defn)
-
DeleteGeomFieldDefn
public int DeleteGeomFieldDefn(int idx)
-
GetGeomType
public int GetGeomType()
Fetch the geometry base type.Note that some drivers are unable to determine a specific geometry type for a layer, in which case wkbUnknown is returned. A value of wkbNone indicates no geometry is available for the layer at all. Many drivers do not properly mark the geometry type as 25D even if some or all geometries are in fact 25D. A few (broken) drivers return wkbPolygon for layers that also include wkbMultiPolygon.
- Returns:
- the base type for all geometry related to this definition.
-
SetGeomType
public void SetGeomType(int geom_type)
Assign the base geometry type for this layer.All geometry objects using this type must be of the defined type or a derived type. The default upon creation is wkbUnknown which allows for any geometry type. The geometry type should generally not be changed after any Features have been created against this definition.
- Parameters:
geom_type
- the new type to assign.
-
GetReferenceCount
public int GetReferenceCount()
Fetch current reference count.- Returns:
- the current reference count.
-
IsGeometryIgnored
public int IsGeometryIgnored()
Determine whether the geometry can be omitted when fetching features.- Returns:
- ignore state (1 if ignored, 0 otherwise)
- Since:
- OGR 1.8.0
-
SetGeometryIgnored
public void SetGeometryIgnored(int bIgnore)
Set whether the geometry can be omitted when fetching features.- Parameters:
bIgnore
- ignore state (1 to ignore, 0 otherwise)- Since:
- OGR 1.8.0
-
IsStyleIgnored
public int IsStyleIgnored()
Determine whether the style can be omitted when fetching features.- Returns:
- ignore state (1 if ignored, 0 otherwise)
- Since:
- OGR 1.8.0
-
SetStyleIgnored
public void SetStyleIgnored(int bIgnore)
Set whether the style can be omitted when fetching features.- Parameters:
bIgnore
- ignore state (1 to ignore, 0 otherwise)- Since:
- OGR 1.8.0
-
IsSame
public int IsSame(FeatureDefn other_defn)
-
-


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You can add, remove, and rearrange timelines to optimize the data view for specific patients.
To add a timeline, click the plus sign, then select Add Data Timeline.
Select the timeline type you want to add, then select the specific timeline.
You can also add another Custom Timeline or text notes. You can also search for concept through the autocomplete
input.
To remove a timeline, hover your cursor over the timeline you want to remove. Click the X in the upper left
corner of the timeline.
You can undo a deletion of a card, as well.
To rearrange timelines, click the label or colored handle on the left hand side of the timeline you want to
move, and hold the mouse button and drag and drop the timeline to your desired location.


Experienced co-founder with a demonstrated history of working with tech startups in various roles - raised multiple VC investments, recruited and formed tech teams and overseen numerous product launches. Prior background as a UX Manager and Front-end Developer within the Financial Services sector.
RiskTalk is a SaaS tool that empowers employees to speak up about issues that affect core values. Being a Swiss based startup, the Wordpress powered website needed to be available in English, French and German.
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My excellent team and I built the entire Pinipa web app from scratch, based on extensive user research and numerous iterations. The state of the art back-end is powered by Node.js and the front-end is supercharged with React.
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The existing Loyalty Edge website was outdated, confusing and in need of a thorough refresh. After undertaking a comprehensive review, our team developed a modern, resonsive site that integrated with the exsiting CMS. The new site reflected the American Express brand.
The Brighton Grand Challenge was a competition looking for ambitious entrepreneurs, whose viable business concepts can help address humanity's biggest problems. The website featured custom illustrations and a JavaScript powered, smoke spitting, industrial chimney!
Zoli Rutter is the founder of a Brighton based, printing startup called Exhibit Printing. Zoli needed a content managed site that him and his team could easily update. The website is a clean, contemporary site that beatuifully showcases the work Exhibit Printing produde.
Exhibit Printing
I live in Brighton, let's grab a coffee or a beer.


 I was sitting at my computer and I looked at my chocolate chip and saw that it was completely full. Sitting next to it was my chocolate easter bunny that I had never unwrapped.  And my pulse is kinda faster. Oh yeah, and I feel really sick still.
4 hours later: I took some valium and tried to get to sleep. No luck. My stomach feels really sludgy or something. I can't explain it. I feel like shit.
6 hours later: The valium seems to be working..I feel really relaxed..I'm going to go to sleep now.
This was transcribed off of my Trip log (a yellow notebook). My guess is that the best way to get the caffeine/theobromine high isn't through eating 5 lbs. of chocolate. Nothing actually even happened to me besides the extreme body load of eating five pounds of chocolate. (i.e. diarrhea, vomiting, cramps, pain.) On the bright side of this experience, I don't think I'll be craving chocolate for a while.
P.s. I do have this strange feeling at the bridge of my nose that I get whenever I roll. It's like my consciousness or my soul has shifted closer to the front of my face..Like I'm thinking out of that part of my brain. Really strange sensation.
Lixity
going to join C.E.A.
-Chocolate Eaters Anonymous
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.


What makes a running topology: worker processes, executors and tasks
Storm distinguishes between the following three main entities that are used to actually run a topology in a Storm cluster:
- Worker processes
- Executors (threads)
- Tasks
Here is a simple illustration of their relationships:
A worker process executes a subset of a topology. A worker process belongs to a specific topology and may run one or more executors for one or more components (spouts or bolts) of this topology. A running topology consists of many such processes running on many machines within a Storm cluster.
An executor is a thread that is spawned by a worker process. It may run one or more tasks for the same component (spout or bolt).
A task performs the actual data processing — each spout or bolt that you implement in your code executes as many tasks across the cluster. The number of tasks for a component is always the same throughout the lifetime of a topology, but the number of executors (threads) for a component can change over time. This means that the following condition holds true: #threads ≤ #tasks
. By default, the number of tasks is set to be the same as the number of executors, i.e. Storm will run one task per thread.
Configuring the parallelism of a topology
Note that in Storm’s terminology “parallelism” is specifically used to describe the so-called parallelism hint, which means the initial number of executor (threads) of a component. In this document though we use the term “parallelism” in a more general sense to describe how you can configure not only the number of executors but also the number of worker processes and the number of tasks of a Storm topology. We will specifically call out when “parallelism” is used in the normal, narrow definition of Storm.
The following sections give an overview of the various configuration options and how to set them in your code. There is more than one way of setting these options though, and the table lists only some of them. Storm currently has the following order of precedence for configuration settings: defaults.yaml
< storm.yaml
< topology-specific configuration < internal component-specific configuration < external component-specific configuration.
Number of worker processes
- Description: How many worker processes to create for the topology across machines in the cluster.
- Configuration option: TOPOLOGY_WORKERS
- How to set in your code (examples):
Number of executors (threads)
- Description: How many executors to spawn per component.
- Configuration option: ?
- How to set in your code (examples):
- TopologyBuilder#setSpout()
- TopologyBuilder#setBolt()
- Note that as of Storm 0.8 the
parallelism_hint
parameter now specifies the initial number of executors (not tasks!) for that bolt.
Number of tasks
- Description: How many tasks to create per component.
- Configuration option: TOPOLOGY_TASKS
- How to set in your code (examples):
Here is an example code snippet to show these settings in practice:
java
topologyBuilder.setBolt("green-bolt", new GreenBolt(), 2)
.setNumTasks(4)
.shuffleGrouping("blue-spout);
In the above code we configured Storm to run the bolt GreenBolt
with an initial number of two executors and four associated tasks. Storm will run two tasks per executor (thread). If you do not explicitly configure the number of tasks, Storm will run by default one task per executor.
Example of a running topology
The following illustration shows how a simple topology would look like in operation. The topology consists of three components: one spout called BlueSpout
and two bolts called GreenBolt
and YellowBolt
. The components are linked such that BlueSpout
sends its output to GreenBolt
, which in turns sends its own output to YellowBolt
.
The GreenBolt
was configured as per the code snippet above whereas BlueSpout
and YellowBolt
only set the parallelism hint (number of executors). Here is the relevant code:
```java Config conf = new Config(); conf.setNumWorkers(2); // use two worker processes
topologyBuilder.setSpout(“blue-spout”, new BlueSpout(), 2); // set parallelism hint to 2
topologyBuilder.setBolt(“green-bolt”, new GreenBolt(), 2) .setNumTasks(4) .shuffleGrouping(“blue-spout”);
topologyBuilder.setBolt(“yellow-bolt”, new YellowBolt(), 6) .shuffleGrouping(“green-bolt”);
StormSubmitter.submitTopology( “mytopology”, conf, topologyBuilder.createTopology() ); ```
And of course Storm comes with additional configuration settings to control the parallelism of a topology, including:
- TOPOLOGY_MAX_TASK_PARALLELISM: This setting puts a ceiling on the number of executors that can be spawned for a single component. It is typically used during testing to limit the number of threads spawned when running a topology in local mode. You can set this option via e.g. Config#setMaxTaskParallelism().
How to change the parallelism of a running topology
A nifty feature of Storm is that you can increase or decrease the number of worker processes and/or executors without being required to restart the cluster or the topology. The act of doing so is called rebalancing.
You have two options to rebalance a topology:
- Use the Storm web UI to rebalance the topology.
- Use the CLI tool storm rebalance as described below.
Here is an example of using the CLI tool:
``` # Reconfigure the topology “mytopology” to use 5 worker processes, # the spout “blue-spout” to use 3 executors and # the bolt “yellow-bolt” to use 10 executors.
$ storm rebalance mytopology -n 5 -e blue-spout=3 -e yellow-bolt=10 ```
References for this article
- Concepts
- Configuration
- Running topologies on a production cluster]
- Local mode
- Tutorial
- Storm API documentation, most notably the class
Config


Veggie Thai Red Curry
- 4 servings
- 40 minutes
- Cookie & Kate
1
small white onion, chopped (about 1 cup)1
red bell pepper, sliced into thin 2-inch long strips1
yellow, orange or green bell pepper, sliced into thin 2-inch long strips3
carrots, peeled and sliced on the diagonal into 1/4-inch thick rounds (about 1 cup)1 1/2 cups
packed thinly sliced kale (tough ribs removed first), preferably the Tuscan/lacinato/dinosaur variety1 cup
basil (garnish)1 tbsp.
finely grated fresh ginger (about a 1-inch nub of ginger)2
cloves garlic, pressed or minced2 tbsp.
Thai red curry paste1 can
(14 ounces) regular coconut milk1 1/4 cups
jasmine rice1 tbsp.
coconut oil or olive oil- Pinch of salt, more to taste
1/2 cup
water1 1/2 tsp.
brown sugar1 tbsp.
soy sauce2 tsp.
rice vinegar or fresh lime juice
Cook rice.
To make the curry, warm a large skillet with deep sides over medium heat. Once it’s hot, add the oil. Add the onion and a sprinkle of salt and cook, stirring often, until the onion has softened and is turning translucent, about 5 minutes. Add the ginger and garlic and cook until fragrant, about 30 seconds, while stirring continuously.
Add the bell peppers and carrots. Cook until the bell peppers are fork-tender, 3 to 5 more minutes, stirring occasionally. Then add the curry paste and cook, stirring often, for 2 minutes.
Add the coconut milk, water, kale and sugar, and stir to combine. Bring the mixture to a simmer over medium heat. Reduce heat as necessary to maintain a gentle simmer and cook until the peppers, carrots and kale have softened to your liking, about 5 to 10 minutes, stirring occasionally.
Remove the pot from the heat and season with soy sauce and rice vinegar. Add salt (I added 1/4 tsp.
for optimal flavor), to taste. If the curry needs a little more punch, add 1/2 tsp.
more soy sauce, or for more acidity, add 1/2 tsp.` more rice vinegar. Divide rice and curry into bowls and garnish with basil. If you love spicy curries, serve with sriracha or chili garlic sauce on the side.


Ohio’s men’s basketball team notched its first win in the NCAA Tournament since 2012, and the women’s team overcame a double-digit deficit in two minutes. The football team won the Battle of the Bricks for the first time in four years, and two Bobcats etched their names into Ohio lore.
There were many bright spots for Ohio in 2021, but we filtered them down to the top five. Here are the five best moments from Ohio’s 2021 athletic year:
Men’s Basketball stuns Virginia in the NCAA Tournament
After a dominant run in the Mid-American Conference Tournament, Ohio earned its first bid to the NCAA Tournament since 2012. It was selected as the No. 13 seed in the West Region, and it was picked to face No. 4 seeded Virginia in the first round.
Whatever the odds were, the Bobcats didn’t care. They upset the Cavaliers with a 62-58 victory and put the nation on notice. After trailing by a point at halftime, the Bobcats rallied. Ben Vander Plas scored 10 points in just over three minutes to give the Bobcats a lead they held for the remainder of the game.
The Bobcats’ luck ran out after they fell to Creighton in the second round, but they had made their mark. They’d dethroned the reigning national champions after not appearing in the NCAA Tournament for nine years.
Women’s Basketball overcomes Buffalo in two-minute rally
After battling back from deficits all game, Ohio found itself in another hole after Buffalo opened the fourth quarter with a 15-0 run. Ohio whittled away at the score but still found itself down by 11 with under two minutes to play.
That was, until the Bobcats went on a 16-2 run in the final two minutes. Ohio got the go-ahead when Kaylee Bambule sank a 3-pointer — her only shot of the game — with 11.4 seconds left on the clock. Cece Hooks closed the game out with a layup for an 84-81 win.
Ohio Football beats Miami at Peden Stadium
Ohio’s season had gone awry well before it hosted Miami for the 97th Battle of the Bricks. It’d dropped seven games and its only win up to that point had been against Akron in early October. What’s more, it hadn’t beaten Miami since 2017.
But the Bobcats bucked that trend after they outlasted the RedHawks 35-33 at Peden Stadium. Quarterback Kurtis Rourke, who’d battled inconsistency through the first half of Ohio’s schedule, completed 23-of-32 passes for 289 yards and three touchdowns. The Bobcats’ defense held back the RedHawks’ rushing corps to just 77 yards and forced quarterback Brett Gabbert to do the heavy lifting.
In a season full of more lows than highs, a rivalry win was a bright spot for Ohio.
Rock’s no-hitter against Morehead State
Joe Rock pitched perhaps the best game of his collegiate career in Ohio’s 2021 home opener. The leftie pitched seven innings on the mound against Morehead State, striking out six and walking two. Behind Rock’s pitching, Ohio snatched the lead easily and dominated Morehead State 6-0 in the first game of a doubleheader.
The game was Rock’s first no-hitter of his college career as well as his last. He was drafted by the Colorado Rockies following the end of the season.
Wexell’s ninth shutout of the season
Ohio ended its regular season with a draw against Kent State — losing the regular season MAC title in the process — but Sam Wexell had good reason to celebrate. The goalkeeper recorded three saves for her ninth shutout of the season as well as her sixth against conference opponents.
Wexell’s nine shutouts are the second-most in a single season in program history. She eclipsed Michelle Meglaughlin, who’d held the second place spot with eight shutouts since 2004.


--- layout: page title : Hobby header : Post Archive group: navigation img : https://lh5.googleusercontent.com/-xPld4FWtqoE/VKOOx7zlDMI/AAAAAAAAAHE/oeRGcrxSNkY/w1024-h335-no/hobby.jpg --- {% include JB/setup %}


In this project, you'll use generative adversarial networks to generate new images of faces.
You'll be using two datasets in this project:
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir
to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images
.
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputs
discriminator
generator
model_loss
model_opt
train
This will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Implement the model_inputs
function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width
, image_height
, and image_channels
.z_dim
.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
# TODO: Implement Function
real_images = tf.placeholder(tf.float32, [None, image_width, image_height, image_channels], name='real_images')
input_z = tf.placeholder(tf.float32, [None, z_dim], name='input_z')
lr = tf.placeholder(tf.float32, [], name='learn_rate')
return real_images, input_z, lr
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Implement discriminator
to create a discriminator neural network that discriminates on images
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).
def discriminator(images, reuse=False):
"""
Create the discriminator network
:param images: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
# TODO: Implement Function
#Based on code from github.com/udacity/deep-learning/dcgan-svhn/DCGAN.ipynb
alpha = 0.2
with tf.variable_scope('discriminator', reuse=reuse):
# Input layer is 28*28*image_channels
cnv1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
relu1 = tf.maximum(alpha * cnv1, cnv1)
# 14x14x64
cnv2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')
# In the batch normalization for the discriminator,
# training is always True because we only use the discriminator
# in training for this project and then discard it after training.
bn2 = tf.layers.batch_normalization(cnv2, training=True)
relu2 = tf.maximum(alpha * bn2, bn2)
# 7x7x128
cnv3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same')
bn3 = tf.layers.batch_normalization(cnv3, training=True)
relu3 = tf.maximum(alpha * bn3, bn3)
# 4x4x256, or is it 3x3x256?
# Flatten it
flat = tf.reshape(relu3, (-1, 4*4*256)) #might need to update this
logits = tf.layers.dense(flat, 1)
out = tf.sigmoid(logits)
return out, logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Implement generator
to generate an image using z
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim
images.
7.0*7*256/(8*8)
def generator(z, out_channel_dim, is_train=True):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
# TODO: Implement Function
#Based on code from github.com/udacity/deep-learning/dcgan-svhn/DCGAN.ipynb
# Using alpha smaller in the generator than the discriminator, since it is closer to a normal ReLU,
# and Radford et al (2016) used a normal ReLU in their generator in their DCGAN paper.
alpha = 0.1
reuse = not is_train #If training, do not reuse. If inference, then reuse.
with tf.variable_scope('generator', reuse=reuse):
# First fully connected layer
x1 = tf.layers.dense(z, 4*4*512)
# Reshape it to start the convolutional stack
x1 = tf.reshape(x1, (-1, 4, 4, 512))
x1 = tf.layers.batch_normalization(x1, training=is_train)
x1 = tf.maximum(alpha * x1, x1)
# 4*4*512 now
x2 = tf.layers.conv2d_transpose(x1, 196, 5, strides=2, padding='same')
x2 = tf.layers.batch_normalization(x2, training=is_train)
x2 = tf.maximum(alpha * x2, x2)
# 8*8*196 now
assert 8*8*196 == 7*7*256, 'Do not expect reshape to work as you expect!'
x2 = tf.reshape(x2, (-1, 7, 7, 256))
# 7*7*256 now
x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=2, padding='same')
x3 = tf.layers.batch_normalization(x3, training=is_train)
x3 = tf.maximum(alpha * x3, x3)
# 14*14*128 now
# Output layer
logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=2, padding='same')
# 28*28*out_channel_dim now
out = tf.tanh(logits)
return out
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Implement model_loss
to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)
def model_loss(input_real, input_z, out_channel_dim):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
# TODO: Implement Function
smooth = 0.01
#Based on code from github.com/udacity/deep-learning/dcgan-svhn/DCGAN.ipynb
g_model = generator(input_z, out_channel_dim)
d_model_real, d_logits_real = discriminator(input_real)
d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
d_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=(1.0-smooth)*tf.ones_like(d_model_real)))
d_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
g_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
d_loss = d_loss_real + d_loss_fake
return d_loss, g_loss
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Implement model_opt
to create the optimization operations for the GANs. Use tf.trainable_variables
to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
# TODO: Implement Function
#Based on code from github.com/udacity/deep-learning/dcgan-svhn/DCGAN.ipynb
# Get all variables to train (weights and biases)
t_vars = tf.trainable_variables()
d_vars = [v for v in t_vars if v.name.startswith('discriminator')]
g_vars = [v for v in t_vars if v.name.startswith('generator')]
# Optimize
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
return d_train_opt, g_train_opt
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Implement train
to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)
Use the show_generator_output
to show generator
output while you train. Running show_generator_output
for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator
output every 100 batches.
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
# TODO: Build Model
real_images, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
d_loss, g_loss = model_loss(real_images, input_z, data_shape[3])
d_train_opt, g_train_opt = model_opt(d_loss, g_loss, lr, beta1)
total_batches_so_far = 0
n_imgs_to_show = 25
print_every_n = 10 # number of batches to wait between printing status message
show_every_n = 100 # number of batches to wait between showing examples
losses = []
samples = []
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch_i in range(epoch_count):
for batch_images in get_batches(batch_size):
# TODO: Train Model
# Adapted some code below from github.com/udacity/deep-learning/dcgan-svhn/DCGAN.ipynb
total_batches_so_far += 1
# Sample random noise for G
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
# Run optimizers
_ = sess.run(d_train_opt, feed_dict={real_images: batch_images, input_z: batch_z, lr: learning_rate})
_ = sess.run(g_train_opt, feed_dict={input_z: batch_z, real_images: batch_images, lr: learning_rate})
if total_batches_so_far % print_every_n == 0:
# At the end of each epoch, get the losses and print them out
train_loss_d = d_loss.eval({real_images: batch_images, input_z: batch_z, lr: learning_rate})
train_loss_g = g_loss.eval({input_z: batch_z, lr: learning_rate})
print("Epoch {}/{}...".format(epoch_i+1, epochs),
"Discriminator Loss: {:.4f}...".format(train_loss_d),
"Generator Loss: {:.4f}".format(train_loss_g))
# Save losses to view after training
losses.append((train_loss_d, train_loss_g))
if total_batches_so_far % show_every_n == 0:
show_generator_output(sess, n_imgs_to_show, input_z, data_shape[3], data_image_mode)
#gen_samples = sess.run(
# generator(z, 3, reuse=True, training=False),
# feed_dict={input_z: batch_z})
#samples.append(gen_samples)
#_ = view_samples(-1, samples, 6, 12, figsize=figsize)
#plt.show()
saver.save(sess, './generator.ckpt')
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
print(mnist_dataset.shape)
batch_size = 128
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
batch_size = 128
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


zarafa_selinux
Section: zarafa SELinux Policy documentation (8)Updated: zarafa
Index Return to Main Contents
NAME
zarafa_selinux - Security Enhanced Linux Policy for the zarafa processesDESCRIPTION
SELinux Linux secures zarafa (Zarafa collaboration platform) processes via flexible mandatory access control.
FILE CONTEXTS
SELinux requires files to have an extended attribute to define the file type.You can see the context of a file using the -Z option to lsP Policy governs the access confined processes have to these files. SELinux zarafa policy is very flexible allowing users to setup their zarafa processes in as secure a method as possible.
The following file types are defined for zarafa:
zarafa_deliver_exec_t
- Set files with the zarafa_deliver_exec_t type, if you want to transition an executable to the zarafa_deliver_t domain.
zarafa_deliver_log_t
- Set files with the zarafa_deliver_log_t type, if you want to treat the data as zarafa deliver log data, usually stored under the /var/log directory.
zarafa_deliver_tmp_t
- Set files with the zarafa_deliver_tmp_t type, if you want to store zarafa deliver temporary files in the /tmp directories.
zarafa_deliver_var_run_t
- Set files with the zarafa_deliver_var_run_t type, if you want to store the zarafa deliver files under the /run directory.
zarafa_etc_t
- Set files with the zarafa_etc_t type, if you want to store zarafa files in the /etc directories.
zarafa_gateway_exec_t
- Set files with the zarafa_gateway_exec_t type, if you want to transition an executable to the zarafa_gateway_t domain.
zarafa_gateway_log_t
- Set files with the zarafa_gateway_log_t type, if you want to treat the data as zarafa gateway log data, usually stored under the /var/log directory.
zarafa_gateway_var_run_t
- Set files with the zarafa_gateway_var_run_t type, if you want to store the zarafa gateway files under the /run directory.
zarafa_ical_exec_t
- Set files with the zarafa_ical_exec_t type, if you want to transition an executable to the zarafa_ical_t domain.
zarafa_ical_log_t
- Set files with the zarafa_ical_log_t type, if you want to treat the data as zarafa ical log data, usually stored under the /var/log directory.
zarafa_ical_var_run_t
- Set files with the zarafa_ical_var_run_t type, if you want to store the zarafa ical files under the /run directory.
zarafa_indexer_exec_t
- Set files with the zarafa_indexer_exec_t type, if you want to transition an executable to the zarafa_indexer_t domain.
zarafa_indexer_log_t
- Set files with the zarafa_indexer_log_t type, if you want to treat the data as zarafa indexer log data, usually stored under the /var/log directory.
zarafa_indexer_tmp_t
- Set files with the zarafa_indexer_tmp_t type, if you want to store zarafa indexer temporary files in the /tmp directories.
zarafa_indexer_var_run_t
- Set files with the zarafa_indexer_var_run_t type, if you want to store the zarafa indexer files under the /run directory.
zarafa_monitor_exec_t
- Set files with the zarafa_monitor_exec_t type, if you want to transition an executable to the zarafa_monitor_t domain.
zarafa_monitor_log_t
- Set files with the zarafa_monitor_log_t type, if you want to treat the data as zarafa monitor log data, usually stored under the /var/log directory.
zarafa_monitor_var_run_t
- Set files with the zarafa_monitor_var_run_t type, if you want to store the zarafa monitor files under the /run directory.
zarafa_server_exec_t
- Set files with the zarafa_server_exec_t type, if you want to transition an executable to the zarafa_server_t domain.
zarafa_server_log_t
- Set files with the zarafa_server_log_t type, if you want to treat the data as zarafa server log data, usually stored under the /var/log directory.
zarafa_server_tmp_t
- Set files with the zarafa_server_tmp_t type, if you want to store zarafa server temporary files in the /tmp directories.
zarafa_server_var_run_t
- Set files with the zarafa_server_var_run_t type, if you want to store the zarafa server files under the /run directory.
- Paths:
-
/var/run/zarafa, /var/run/zarafa-server.pid
zarafa_share_t
- Set files with the zarafa_share_t type, if you want to treat the files as zarafa share data.
zarafa_spooler_exec_t
- Set files with the zarafa_spooler_exec_t type, if you want to transition an executable to the zarafa_spooler_t domain.
zarafa_spooler_log_t
- Set files with the zarafa_spooler_log_t type, if you want to treat the data as zarafa spooler log data, usually stored under the /var/log directory.
zarafa_spooler_var_run_t
- Set files with the zarafa_spooler_var_run_t type, if you want to store the zarafa spooler files under the /run directory.
zarafa_var_lib_t
- Set files with the zarafa_var_lib_t type, if you want to store the zarafa files under the /var/lib directory.
- Paths:
-
/var/lib/zarafa-webaccess(/.*)?, /var/lib/zarafa(/.*)?
Note: File context can be temporarily modified with the chcon command. If you want to permanently change the file context you need to use the semanage fcontext command. This will modify the SELinux labeling database. You will need to use restorecon to apply the labels.
PORT TYPES
SELinux defines port types to represent TCP and UDP ports.You can see the types associated with a port by using the following command:
semanage port -l
Policy governs the access confined processes have to these ports. SELinux zarafa policy is very flexible allowing users to setup their zarafa processes in as secure a method as possible.
The following port types are defined for zarafa:
- zarafa_port_t
-
Default Defined Ports: tcp 8021
PROCESS TYPES
SELinux defines process types (domains) for each process running on the systemYou can see the context of a process using the -Z option to psP Policy governs the access confined processes have to files. SELinux zarafa policy is very flexible allowing users to setup their zarafa processes in as secure a method as possible.
The following process types are defined for zarafa:
zarafa_gateway_t, zarafa_spooler_t, zarafa_deliver_t, zarafa_monitor_t, zarafa_indexer_t, zarafa_server_t, zarafa_ical_t
Note: semanage permissive -a PROCESS_TYPE can be used to make a process type permissive. Permissive process types are not denied access by SELinux. AVC messages will still be generated.
COMMANDS
semanage fcontext can also be used to manipulate default file context mappings.semanage permissive can also be used to manipulate whether or not a process type is permissive.
semanage module can also be used to enable/disable/install/remove policy modules.
semanage port can also be used to manipulate the port definitions
system-config-selinux is a GUI tool available to customize SELinux policy settings.
AUTHOR
This manual page was autogenerated by genman.py.SEE ALSO
selinux(8), zarafa(8), semanage(8), restorecon(8), chcon(1)
Index
This document was created by man2html, using the manual pages.
Time: 05:34:31 GMT, December 24, 2015


public class MaterialDebugAppState extends AbstractAppState
initialized
| Constructor and Description |
|---|
MaterialDebugAppState() |
| Modifier and Type | Method and Description |
|---|---|
void |
initialize(AppStateManager stateManager,
Application app)
Called by
AppStateManager when transitioning this AppState
from initializing to running.This will happen on the next iteration through the update loop after AppStateManager.attach(com.jme3.app.state.AppState) was called. |
void |
registerBinding(java.lang.String shaderName,
Filter filter)
Will reload the filter's materials whenever the shader file is changed
on the hard drive
|
void |
registerBinding(java.lang.String shaderName,
Spatial spat)
Will reload the spatials's materials whenever the shader file is changed
on the hard drive
|
void |
registerBinding(Trigger trigger,
Filter filter)
Will reload the filter's materials whenever the trigger is fired.
|
void |
registerBinding(Trigger trigger,
Spatial spat)
Will reload the spatial's materials whenever the trigger is fired
|
Material |
reloadMaterial(Material mat) |
void |
update(float tpf)
Called to update the
AppState . |
cleanup, isEnabled, isInitialized, postRender, render, setEnabled, stateAttached, stateDetached
public void initialize(AppStateManager stateManager, Application app)
AppState
AppStateManager
when transitioning this AppState
from initializing to running.AppStateManager.attach(com.jme3.app.state.AppState)
was called.
AppStateManager
will call this only from the update loop
inside the rendering thread. This means is it safe to modify the scene
graph from this method.
initialize
in interface AppState
initialize
in class AbstractAppState
stateManager
- The state managerapp
- The applicationpublic void registerBinding(Trigger trigger, Spatial spat)
trigger
- the triggerspat
- the spatial to reloadpublic void registerBinding(Trigger trigger, Filter filter)
trigger
- the triggerfilter
- the filter to reloadpublic void registerBinding(java.lang.String shaderName, Filter filter)
shaderName
- the shader name (relative path to the asset folder or
to a registered asset path)filter
- the filter to reloadpublic void registerBinding(java.lang.String shaderName, Spatial spat)
shaderName
- the shader name (relative path to the asset folder or
to a registered asset path)spat
- the spatial to reloadpublic void update(float tpf)
AppState
AppState
. This method will be called
every render pass if the AppState
is both attached and enabled.update
in interface AppState
update
in class AbstractAppState
tpf
- Time since the last call to update(), in seconds.


serialize(node, format)
This function takes two arguments: the first is a node (generally a document or element node)
to be serialized. The second is the name of an xsl:output
element in the stylesheet.
The second argument must be known at compile time (it will typically be supplied as a string literal.)
The function serializes the specified document, or the subtree rooted at the specified element, according
to the parameters specified in the named xsl:output
element, and returns the serialized
document as a string.
This function is useful where the XSLT stylesheet wants to manipulate the serialized output, for example by embedding it as CDATA inside another XML document, or prefixing it with a DOCTYPE declaration, or inserting it into a non-XML output file.


#{extends 'admin.html' /} Start iframe Your browser does not support iframes. End iframe
Your browser does not support iframes.


group
dill 🌿 lab
I lead the Datasets, Interpretability, Language and Learning (DILL 🌿) Lab at USC.
prior interns and student collaborators
I had the pleasure of mentoring and collaborating with several students and interns during my postdoc at AI2, as well as during my PhD.


Here, you can find a list of things (mostly books, but also papers, essays, theses etc.) that I either read or am planning to read.
For those I already worked my way through, there will most likely be a short summary or review. For some, there will (hopefully) be lengthier reviews.


In our Discover IntelliJ IDEA guide you’ll find information about the most important IntelliJ IDEA features and shortcuts. Our Quick Start guide provides essential information to help you get started. If you don’t feel like reading, try our video tutorials instead.


{% if title %}
You entered {{data.num1}} and {{data.num2}}. You chose to {{data.operator}}. The solution is {{data.solution}}.


Module: Sinatra::UrlForHelper
- Defined in:
- lib/sinatra/url_for.rb
Instance Method Summary (collapse)
- - (Object) root_url
-
- (Object) url_for(url_fragment, mode = :path_only)
Construct a link to url_fragment, which should be given relative to the base of this Sinatra app.
Instance Method Details
- (Object) root_url
45 46 47 |
# File 'lib/sinatra/url_for.rb', line 45 def root_url url_for '/' end |
- (Object) url_for(url_fragment, mode = :path_only)
Construct a link to url_fragment, which should be given relative to the base of this Sinatra app. The mode should be either :path_only, which will generate an absolute path within the current domain (the default), or :full, which will include the site name and port number. (The latter is typically necessary for links in RSS feeds.) Example usage:
url_for "/" # Returns "/myapp/" url_for "/foo" # Returns "/myapp/foo" url_for "/foo", :full # Returns "http://example.com/myapp/foo"
— See README.rdoc for a list of some of the people who helped me clean up earlier versions of this code.
18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 |
# File 'lib/sinatra/url_for.rb', line 18 def url_for url_fragment, mode=:path_only case mode when :path_only base = request.script_name when :full scheme = request.scheme if (scheme == 'http' && request.port == 80 || scheme == 'https' && request.port == 443) port = "" else port = ":#{request.port}" end request_host = HOSTNAME ? HOSTNAME : request.host base = "#{scheme}://#{request_host}#{port}#{request.script_name}" else raise TypeError, "Unknown url_for mode #{mode}" end url_escape = URI.escape(url_fragment) # Don't add the base fragment if url_for gets called more than once # per url or the url_fragment passed in is an absolute url if url_escape.match(/^#{base}/) or url_escape.match(/^http/) url_escape else "#{base}#{url_escape}" end end |


Responsive Design
With the emergence of more and more devices with different form factors and resolutions its increasingly difficult to accomadate every possible visitor with the old practice of specific mobile sites. This has lead to the creation of Responsive Design. Responsive Design is not a single piece of technology, but rather, a set of techniques and ideas that form a whole.1 The three main techniques behind responsive design are fluid grids, flexible images, and media queries.2
The idea of a fluid grid is that rather than base the layout of the site on an arbitrary number of pixels, the layout will be based on the size of the window available. This is achieved by assigning containers width and height values of a corresponding percentage to match the planned layout.
The simplest way to achieve flexible images is simply to create fluid images. This is done much in the same way that we create a fluid grid, we tell all image tags to take up 100% of their container element and make that element sized properly in the fluid grid.3
The inclusion of media queries is essentially what separates a responsive design from a site that is just a fluid grid layout. Media queries allow you to discover the resolution of the device used and conditionally apply different CSS styles. For instance if your layout calls for two columns on a standard resolution, it might change the design to only one column for mobile devices.
So in conclusion, responsive design enables you to create site layouts that respond to changing screen resolutions. With fluid grids and flexible images, the content gets displayed proportionally to the screen as the resolution changes. And with media queries, you can set conditions by which the layout might change based on the device that's being used or the resolution of the window.


This is currently testing the gallery module, not the sandbox module. The gallery module is more up to date, but not structured like the sandbox code.
other()


Blair Witch is coming to PlayStation 4 next month
by Stephany NunneleyBloober Team’s take on the horror franchise Blair Witch is coming to PlayStation 4 next month.
Blair Witch will be released on PlayStation 4 December 3, Bloober Team announced today.
Originally released in August on PC and Xbox One, the game is a first-person psychological horror title based on the cinematic lore of the films.
Set in 1996, you play as a former Maryland police officer who is helping search for a missing boy. You will explore the Black Hills Forest near Burkittsville, and not only face your past but evil forces within the forest.
You won’t be traversing the woods alone with just a flashlight, because your trusty German Shepherd, Bullet, will be by your side. He will alert you to danger and find important items, and how you treat him will affect his behavior.
Additionally, on PS4, players will be able to customize their canine companion with the Good Boy Pack. It includes new dog skins; new in-game cellphone games; wallpapers; content; and new animations for Bullet.
The Good Boy Pack will also be available for PC via Steam and on Xbox One.
The game will be available as a digital download through the PlayStation Store for $29.90, or the regional equivalent. It will be made available at retail on a later date.


The PWG held its February 2020 Virtual Face-to-Face Meeting on February 5-6, 2020 via WebEx teleconferences. Representatives from Canon, Google, High North, HP Inc., Kyocera, Lakeside Robotics, Lexmark, Ricoh, TIC, TCS and Xerox attended the meetings. Attendees reviewed work in progress, including drafts of a number of in-progress specifications, and discussed liaisons with partner groups. Here is a summary of the proceedings.
As in previous PWG Face to Face events, the PWG Chair presented the Plenary.
We reviewed the overall state of the PWG,
its programs and initiatives, and briefly discussed upcoming
face-to-face meeting scheduling. We noted that there are
now 412 printers certified under the PWG's IPP Everywhere™
Self Certification program, with more certifications believed to
be in progress from multiple vendors. We discussed the PWG Steering Committee's
activities and initiatives, including its 2020 goals, the status of Process 4.0, PR efforts, recently approved
documents, process document updates, the website migration initiative,
and "toolset" alternatives for producing specifications. Each active PWG
workgroup (IDS Workgroup
and IPP
Workgroup) summarized their current status. Finally, we
reviewed the status of our partners' work in Trusted Computing
Group (TCG), IETF, Linux Foundation OpenPrinting Workgroup, Mopria Alliance and 3MF Consortium.
Complete minutes are available here: https://ftp.pwg.org/pub/pwg/general/minutes/pwg-plenary-minutes-20200205.htm
On the first day, Ira McDonald (IPP WG Co-Chair) and Mike Sweet
(IPP WG Secretary) surveyed the
status of current IPP Workgroup works in progress, noting that IPP
Transaction-Based Printing Extensions v1.1 and IPP Label Printing
Extensions v1.0 are both in IPP WG Last Call, ending on February 13,
2020. We then briefly
reviewed the status of IPP
Everywhere™ and IPP Everywhere Self Certification,
noting that a beta release of the IPP Everywhere Self Certification Test
Suite 1.0 Update 4 was recently announced and made available, and
encouraged attendees to test this in the next two weeks. After
a break, we discussed the status of IPP Production Printing Extensions v2.0. Following that,
we began a review of the latest
draft of IPP Driverless Printing Extensions v2.0, that had received a
number of updates. That review didn't complete, so the group committed
to reviewing it further the next day. We
finished the day discussing Job Accounting with IPP v1.0.
On the second day, after the IDS Workgroup session, the IPP
Workgroup sessions started with a
review of IPP Encrypted Jobs and Documents v1.0. We then resumed
our review of IPP Driverless Printing Extensions v2.0, still not
completing a full review but making substantive progress, and committed
to conclude the review of this revision in the next IPP WG
teleconference. After a much needed break,
Paul Tykodi (IPP WG Co-Chair and lead PWG 3D Printing Liaison) led a
discussion on the status of the PWG's 3D Printing
related liaisons and outreach efforts, noting that we were in the
process of establishing a liaison with ITC TC171 and some upcoming
engagements with ACI and presentations at Drupa 2020. We concluded the
IPP WG
sessions with next steps.
Complete minutes are available here: https://ftp.pwg.org/pub/pwg/ipp/minutes/ippv2-f2f-minutes-20200205.pdf
At the start of the second day, Alan Sukert (outgoing PWG Vice-Chair
and IDS WG Chair) led the IDS Workgroup
status and progress discussion. We went through the minutes of the HCD
Technical Community (TC) Conference Calls held recently, including the
status of the initiative to approve generation of an HCD international
TC (iTC) and some thoughts on what the content of HCD collaborative
Protection Profile (cPP) v1.0 should include. Our hope is that the vote
will be done before the CCUF Workshop Mar 15-17 in Burlington MA. Ira
then went through his slides on TLS 1.2 and TLS 1.3 that are part of the
meeting slides. Ira then led us through the changes made in the PDF rev
version of the 1/20/2020 draft of the PWG Hardcopy Device Security
Guidelines v1.0. Alan indicated that he is retiring from Xerox on April
1st so this is his last IDS Face-to-Face Meeting, and the other
attendees thanked him for his efforts. We wrapped up with next steps.
Complete minutes are available here: https://ftp.pwg.org/pub/pwg/ids/minutes/ids-f2f-minutes-20200206.pdf
The next PWG Face-to-Face meeting will be May 5-7, 2020 at the Lexmark headquarters in Lexington, KY. Please check the PWG Meetings page for updates on plans for upcoming meetings.


Honeycombe Drive
[50, 20]
|
Nurton Walk Fire Station
[51, 20]
|
Chenery Cinema
[52, 20]
|
Edmund General Hospital
[53, 20]
|
Nulty Lane
[54, 20]
|
Dunham Way
[55, 20]
|
Holroyd Bank
[56, 20]
|
Club Bu
[57, 20]
|
the Sowler Monument
[58, 20]
|
a junkyard
[59, 20]
|
Hatchard Road
[50, 21]
|
Rodford Plaza
[51, 21]
|
a warehouse
[52, 21]
|
Allder Row
[53, 21]
|
Ranahan Library
[54, 21]
|
Farrant Crescent Police Dept
[55, 21]
|
a factory
[56, 21]
|
a junkyard
[57, 21]
|
Giffard Square
[58, 21]
|
the Gabe Building
[59, 21]
|
the Fifield Motel
[50, 22]
|
a junkyard
[51, 22]
|
wasteland
[52, 22]
|
the Whippey Building
[53, 22]
|
St Christopher's Church
[54, 22]
|
a junkyard
[55, 22]
|
Borde Bank
[56, 22]
|
Club Williames
[57, 22]
|
Greedy Street
[58, 22]
|
Grandfield Walk
[59, 22]
|
St Luke's Cathedral
[50, 23]
|
the Bridle Museum
[51, 23]
|
Stickling Mall
[52, 23]
|
Stickling Mall
[53, 23]
|
wasteland
[54, 23]
|
Lessey Lane Police Dept
[55, 23]
|
Bryan Walk
[56, 23]
|
Cleall Walk
[57, 23]
|
a carpark
[58, 23]
|
Hambeln Alley
[59, 23]
|
St Luke's Cathedral
[50, 24]
|
Slatter Crescent
[51, 24]
|
Stickling Mall
[52, 24]
|
Stickling Mall
[53, 24]
|
Hulme Park
[54, 24]
|
Deanesly Park
[55, 24]
|
Keedwell Plaza School
[56, 24]
|
the Soper Motel
[57, 24]
|
Aslin Crescent
[58, 24]
|
St Herman's Church
[59, 24]
|
the Mallows Building
[50, 25]
|
Lord Road
[51, 25]
|
the Dundas Building
[52, 25]
|
Owsley Crescent Police Dept
[53, 25]
|
MacGilvray Alley
[54, 25]
|
Polden Way
[55, 25]
|
Lazarus General Hospital
[56, 25]
|
Clewlow Drive
[57, 25]
|
Belben Lane
[58, 25]
|
the Newnam Motel
[59, 25]
|
the Derrington Building
[50, 26]
|
Robey Avenue
[51, 26]
|
Meaker Lane Fire Station
[52, 26]
|
Chalderwood Way
[53, 26]
|
wasteland
[54, 26]
|
the Thorne Arms
[55, 26]
|
Krinks Boulevard
[56, 26]
|
Denmead Walk School
[57, 26]
|
Gay Avenue
[58, 26]
|
a factory
[59, 26]
|
Fritz Lane
[50, 27]
|
the Parkinson Hotel
[51, 27]
|
the Stacey Monument
[52, 27]
|
Borrer Street Police Dept
[53, 27]
|
Abrahall Auto Repair
[54, 27]
|
Male Way
[55, 27]
|
the McMahon Motel
[56, 27]
|
Club Pengelly
[57, 27]
|
Waddon Crescent
[58, 27]
|
wasteland
[59, 27]
|
Upham Auto Repair
[50, 28]
|
Manuel Towers
[51, 28]
|
the Sartin Monument
[52, 28]
|
Sage Avenue
[53, 28]
|
the Eford Motel
[54, 28]
|
the Nisbet Building
[55, 28]
|
St Athanasius's Church
[56, 28]
|
Pledger Alley
[57, 28]
|
Bridell Square
[58, 28]
|
wasteland
[59, 28]
|
wasteland
[50, 29]
|
Josaphat General Hospital
[51, 29]
|
the Turner Building
[52, 29]
|
a factory
[53, 29]
|
the Rush Building
[54, 29]
|
the Fitkin Hotel
[55, 29]
|
Gajewski Avenue
[56, 29]
|
a warehouse
[57, 29]
|
the Pratt Building
[58, 29]
|
Leigh Library
[59, 29]
|
|
Menu
City Map
Find Location
Route Finder
World View
Map Mirror
|


Some quick example text to build on the card title and make up the bulk of the card's content. which is really long and over extra because I got a log to say
Go somewhereSome quick example text to build on the card title and make up the bulk of the card's content.
Go somewhereSome quick example text to build on the card title and make up the bulk of the card's content.
Go somewhereSome quick example text to build on the card title and make up the bulk of the card's content.
Go somewhereSome quick example text to build on the card title and make up the bulk of the card's content.
Go somewhereSome quick example text to build on the card title and make up the bulk of the card's content.
Go somewhereSome quick example text to build on the card title and make up the bulk of the card's content.
Go somewhere


Click to play
(W, A, S, D = Move, MOUSE = Look around)
If you're near to a poster, press ENTER to see information about the movie.


I love my family, the beach, and technology. Once a year I join other family members in a 60 mile bicycle ride that starts and ends in a family reunion in Mexico (you can find out more about it here). I was born in Los Angeles and lived most of my early life in Mexico but now live in Tucson Arizona where almost all of my brothers and sisters currently live. I like to stay physically active and like watching a good movie once in a while. Life has been good to me and I’m blessed with a beautiful wife that complements me and keeps me honest. You can see some of my family pictures here.
Social media is the big thing nowadays and even though I’m not very active in the social media scene I do have a presence in some of the major sites such as:
I love technology and the Internet is what’s happening. But what would the Internet be without websites? I currently host more than ten websites that I have designed and created. I will be the first to admit that I’m not a great designer of websites but I can safely say that my websites are better than 90% of what’s out there. My sites are all responsive and can be viewed and navigated using a smartphone, a tablet, and/or a traditional desktop computer. If you want to see what I’m talking about just resize the browser you are using to view this particular site; you will see how the contents of this website adjust themself. Some of the websites and domain names I own are: gaxiola.info, abelgaxiola.com, tensignals.com, composicionesmusicales.com, and carteldesinaloa.com just to name a few.
If you do what you like and get paid for doing it, is it really work? I wish that was always the case but the truth of the matter is that this is the case most of the time; at least those periods of time when I’m writing code or doing some web development. My current work title is Applications Developer and work for the IT Department of Pima County in Tucson Arizona. I have been working as a software developer for the past 15 years and been working with computers since the late 80s. For more information about my professional life click on the following: LinkedIn
Writing and keeping a blog is a lot of work, and I do it as an exercise on thinking and writing. For that reason I have a couple of blogs: one for just writing about whatever I have in mind, the other I try to focus on technology. Writing was the hardest subject for me in college and I still struggle a lot. I still remember my teacher’s advice regarding getting better at putting my thoughts in writing: she said “just write whatever comes to your mind, do it often until it gets easier”. To this date I still try to follow her advice. My blogs are Thoughts Expressed and Mostly Technical
Currently I own 12 domain names and for economic and professional reasons I create my own websites and host my own domains. I created a Limited Liability Corporation and named it Ten Signals LLC. I believe that the Internet, computer technology, and the mobile revolution are only getting started. My advice to anyone that’s willing to listen is: claim your future and put a stake in the ground by having a web presence. Leasing your domain name is getting harder by the minute and I sincerely doubt that it is even available. If you’re lucky enough to find the domain name you want, or one you like, you can host it for less than 25 cents a day.


WITNESS LIST General Investigating Committee May 8, 2003-8:00A Tx. Health and Human Services Comm On: Cooke, Jason (Tx. Health and Human Services Commission) Hagger, Dominic (Clarendon National Insurance Company) Masters, Michael (Community Health Solutions of America)


It's Always Sunny in ... the Request, Response Cycle
15 Nov 2014A Rails Application and the Web Server
With an internet browser, the user can interact with your application. My job is to build this application. It is difficult to become a master of developing rails applications if I don't understand what is happening behind the scenes. The web server is a central part of the operation. The request, response cycle describes the sequence of events through which the server handles and processes information.
Frank, Dee, Mac, Dennis, and Charlie could create a rails application but it would be horrible. Everything would go wrong and nothing would work. It is one of my favorite shows because it's full of hilarious irony. So we can refer to the user as Charlie since the others would vote him out due to creative differences. Charlie interacts with the browser which shows your web application. Then Charlie clicks a button and that makes a request to the server. This request hits the web server where it first sees Rack. Rack wraps HTTP requests into a single method call to interface with Ruby web applications. It's a very useful API especially if you're creating a Rails application.
At this point you would probably think Dennis would announce himself as the manager. It normally would be but Dennis is too obsessed with writing the about page to be just about himself. The request now sees the Rails application. The routes ( location in the application: config/routes.rb ) are used to figure out where the application needs to go. It matches the request with the specified controller and controller action to perform.
The controller can be seen as the director of the rails application. It
organizes the actions and contains their definitions made by the developer. Frank
would in charge of the controller and would make Dee in charge of the Model.
Frank is funding the operation so whatever he wants will happen. He corrupts
Dee into taking care of the information due to his manipulative ways. The
controller calls out to the model if it needs to access the business logic.
Databases are what models use to organize the information for an application.
Postgresql is a database which uses SQL to access, update, delete, and create
data objects. The controller can also renders views at the end of the interaction
with the model.
Mac wants to be the face of the application. He wants the fame and success of
this whole process. Mac ends up being the view after all the actions have been
processed. He will secure the app and restore the order, like he always does.
The views are what the user sees. The controller specifies a view after it
gathers all the information and conducts the actions which we define. The view
could contain JavaScript, CSS, and ruby code. Our HTML documents are named
according to which programmable language we embed in the files (mac.html.erb,
charlie.html.css, frank.html.js, etc.). The response is now sent back to the
web server and then to the browser where the user can view the newly rendered page.
After all this, the gang just decided to scrap the whole idea and figure out a way to get their money back. It's Always Sunny in the Request Response Cycle.


CRFHeartRateStep
open class CRFHeartRateStep : RSDActiveUIStepObject, RSDRestartableRecorderConfiguration
Custom subclass of the active step that can decode configuration details specific to the heart rate recorder used by this step view controller.
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The camera settings.
Declaration
Swift
public var cameraSettings: CRFCameraSettings
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Is this the resting heart rate?
Declaration
Swift
public var isResting: Bool
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Is this the training task?
Declaration
Swift
public var isTraining: Bool
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Should the file used in a previous run of a recording be deleted? Default =
true
.Declaration
Swift
public private(set) var shouldDeletePrevious: Bool
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This recorder requires permission to use the camera.
Declaration
Swift
public var permissionTypes: [RSDPermissionType] { get }
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The start and stop identifers are for this step.
Declaration
Swift
public var startStepIdentifier: String? { get }
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The start and stop identifers are for this step.
Declaration
Swift
public var stopStepIdentifier: String? { get }
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Override the copy into method.
Declaration
Swift
override open func copyInto(_ copy: RSDUIStepObject)
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Override to decode the configuration if there is one.
Declaration
Swift
override open func decode(from decoder: Decoder, for deviceType: RSDDeviceType?) throws
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This step has multiple results so use a collection result to store them.
Declaration
Swift
override open func instantiateStepResult() -> RSDResult
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By default, return the task view controller from the storyboard.
Declaration
Swift
public func instantiateViewController(with parent: RSDPathComponent?) -> (UIViewController & RSDStepController)?


I am a front end developer in training. Right now I am currently unemployed and working hard to get trained so that I can purse a career in the tech field!
I am a front end developer in training. Right now I am currently unemployed and working hard to get trained so that I can purse a career in the tech field!
I was born in D.C but was raised in Maryland. I am full-bloodied Jamacian but unlike my parents I don't have the thick accent. I moved around a lot when I was younger so I am really good at adapting to my surroundings.
My passion: I am gamer and love to vibe out to music. Although I don't play as many games that I have in the pass or make as much music, I still do so whenever I have some free time.
Fun fact: A few years ago, when I was still pursing music as a career, I had the honor to meet 21 Savage and Brandy whilst working as an intern at music studio. It was cool experience that made me remember that although they are famous, they are still human just like me and you.


Hi. I am fine.
Thanks,
Alex
From: Foo<EMAIL_ADDRESS>On Behalf Of<EMAIL_ADDRESS>Sent: Monday, January 01, 2000 12:00 AM To<EMAIL_ADDRESS>Cc<EMAIL_ADDRESS>Subject: Conversation
Hello! How are you?


{% include head.html %}
{% include nav_front.html %} {% include header_front.html %} {% include aboutus.html %} {% include research.html %} {% include teaching.html %} {% include dev.html %} {% include team.html %} {% include contact.html %} {% include footer.html %} {% include modals.html %} {% include js.html %}


ESW members collaborate on sustainability projects that improve the community and the world.
Sharing knowledge related to sustainable practices and technologies is a fundamental goal of ESW at UCI.
ESW brings together like-minded individuals who are concerned with finding effective solutions to the worlds sustainability needs.


Toronto, Ontario
Rock of the Matterhorn
This half-ton piece of the Alps sits at the base of the CN Tower.
by jbartlett2000In the shadow of an iconic Toronto landmark, the 1,000-pound “Rock of the Matterhorn” sits alone and almost forgotten. You have to bend down to read the plaque alongside the craggy, egg-shaped piece of rock.
The rock was presented to the city in celebration of the “Salute to Switzerland”, an event held at the CN Tower in May 1981. It measures just over three feet tall, 33 inches wide and 15 inches thick, and is encased in an iron frame.
The plaque notes occasion of the gift and notes some facts about the Matterhorn: it sits on the border of Switzerland and Italy, is 14,692 feet fall and was first ascended by Englishman Edward Thymper in 1865 (though the plaque does not note that four of the seven climbers died on the descent). The rock itself is made up of gneiss and quartzite, two types of metamorphic rock.
The CN Tower was just five years old when the rock was presented, and at the time it was the world’s tallest free-standing structure at 1,815 feet high. It held the title until 2007, when the Burj Khalifa overtook it.
In the intervening years, further development in the area—including the 3D Canada150 sign in 2017, one of a number placed across the country for the Sesquicentennial—have almost entirely hidden the rock from view.
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