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29 Mar 2017 by Timo Walther (@twalthr)
Stream processing can deliver a lot of value. Many organizations have recognized the benefit of managing large volumes of data in real-time, reacting quickly to trends, and providing customers with live services at scale. Streaming applications with well-defined business logic can deliver a competitive advantage.
Flink’s DataStream abstraction is a powerful API which lets you flexibly define both basic and complex streaming pipelines. Additionally, it offers low-level operations such as Async IO and ProcessFunctions. However, many users do not need such a deep level of flexibility. They need an API which quickly solves 80% of their use cases where simple tasks can be defined using little code.
To deliver the power of stream processing to a broader set of users, the Apache Flink community is developing APIs that provide simpler abstractions and more concise syntax so that users can focus on their business logic instead of advanced streaming concepts. Along with other APIs (such as CEP for complex event processing on streams), Flink offers a relational API that aims to unify stream and batch processing: the Table & SQL API, often referred to as the Table API.
Recently, contributors working for companies such as Alibaba, Huawei, data Artisans, and more decided to further develop the Table API. Over the past year, the Table API has been rewritten entirely. Since Flink 1.1, its core has been based on Apache Calcite, which parses SQL and optimizes all relational queries. Today, the Table API can address a wide range of use cases in both batch and stream environments with unified semantics.
This blog post summarizes the current status of Flink’s Table API and showcases some of the recently-added features in Apache Flink. Among the features presented here are the unified access to batch and streaming data, data transformation, and window operators. The following paragraphs are not only supposed to give you a general overview of the Table API, but also to illustrate the potential of relational APIs in the future.
Because the Table API is built on top of Flink’s core APIs, DataStreams and DataSets can be converted to a Table and vice-versa without much overhead. Hereafter, we show how to create tables from different sources and specify programs that can be executed locally or in a distributed setting. In this post, we will use the Scala version of the Table API, but there is also a Java version as well as a SQL API with an equivalent set of features.
Data Transformation and ETL
A common task in every data processing pipeline is importing data from one or multiple systems, applying some transformations to it, then exporting the data to another system. The Table API can help to manage these recurring tasks. For reading data, the API provides a set of ready-to-use TableSources
such as a CsvTableSource
and KafkaTableSource
, however, it also allows the implementation of custom TableSources
that can hide configuration specifics (e.g. watermark generation) from users who are less familiar with streaming concepts.
Let’s assume we have a CSV file that stores customer information. The values are delimited by a “|”-character and contain a customer identifier, name, timestamp of the last update, and preferences encoded in a comma-separated key-value string:
42|Bob Smith|2016-07-23 16:10:11|color=12,length=200,size=200
The following example illustrates how to read a CSV file and perform some data cleansing before converting it to a regular DataStream program.
// set up execution environment
val env = StreamExecutionEnvironment.getExecutionEnvironment
val tEnv = TableEnvironment.getTableEnvironment(env)
// configure table source
val customerSource = CsvTableSource.builder()
.path("/path/to/customer_data.csv")
.ignoreFirstLine()
.fieldDelimiter("|")
.field("id", Types.LONG)
.field("name", Types.STRING)
.field("last_update", Types.TIMESTAMP)
.field("prefs", Types.STRING)
.build()
// name your table source
tEnv.registerTableSource("customers", customerSource)
// define your table program
val table = tEnv
.scan("customers")
.filter('name.isNotNull && 'last_update > "2016-01-01 00:00:00".toTimestamp)
.select('id, 'name.lowerCase(), 'prefs)
// convert it to a data stream
val ds = table.toDataStream[Row]
ds.print()
env.execute()
The Table API comes with a large set of built-in functions that make it easy to specify business logic using a language integrated query (LINQ) syntax. In the example above, we filter out customers with invalid names and only select those that updated their preferences recently. We convert names to lowercase for normalization. For debugging purposes, we convert the table into a DataStream and print it.
The CsvTableSource
supports both batch and stream environments. If the programmer wants to execute the program above in a batch application, all he or she has to do is to replace the environment via ExecutionEnvironment
and change the output conversion from DataStream
to DataSet
. The Table API program itself doesn’t change.
In the example, we converted the table program to a data stream of Row
objects. However, we are not limited to row data types. The Table API supports all types from the underlying APIs such as Java and Scala Tuples, Case Classes, POJOs, or generic types that are serialized using Kryo. Let’s assume that we want to have regular object (POJO) with the following format instead of generic rows:
class Customer {
var id: Int = _
var name: String = _
var update: Long = _
var prefs: java.util.Properties = _
}
We can use the following table program to convert the CSV file into Customer objects. Flink takes care of creating objects and mapping fields for us.
val ds = tEnv
.scan("customers")
.select('id, 'name, 'last_update as 'update, parseProperties('prefs) as 'prefs)
.toDataStream[Customer]
You might have noticed that the query above uses a function to parse the preferences field. Even though Flink’s Table API is shipped with a large set of built-in functions, is often necessary to define custom user-defined scalar functions. In the above example we use a user-defined function parseProperties
. The following code snippet shows how easily we can implement a scalar function.
object parseProperties extends ScalarFunction {
def eval(str: String): Properties = {
val props = new Properties()
str
.split(",")
.map(\_.split("="))
.foreach(split => props.setProperty(split(0), split(1)))
props
}
}
Scalar functions can be used to deserialize, extract, or convert values (and more). By overwriting the open()
method we can even have access to runtime information such as distributed cached files or metrics. Even the open()
method is only called once during the runtime’s task lifecycle.
Unified Windowing for Static and Streaming Data
Another very common task, especially when working with continuous data, is the definition of windows to split a stream into pieces of finite size, over which we can apply computations. At the moment, the Table API supports three types of windows: sliding windows, tumbling windows, and session windows (for general definitions of the different types of windows, we recommend Flink’s documentation). All three window types work on event or processing time. Session windows can be defined over time intervals, sliding and tumbling windows can be defined over time intervals or a number of rows.
Let’s assume that our customer data from the example above is an event stream of updates generated whenever the customer updated his or her preferences. We assume that events come from a TableSource that has assigned timestamps and watermarks. The definition of a window happens again in a LINQ-style fashion. The following example could be used to count the updates to the preferences during one day.
table
.window(Tumble over 1.day on 'rowtime as 'w)
.groupBy('id, 'w)
.select('id, 'w.start as 'from, 'w.end as 'to, 'prefs.count as 'updates)
By using the on()
parameter, we can specify whether the window is supposed to work on event-time or not. The Table API assumes that timestamps and watermarks are assigned correctly when using event-time. Elements with timestamps smaller than the last received watermark are dropped. Since the extraction of timestamps and generation of watermarks depends on the data source and requires some deeper knowledge of their origin, the TableSource or the upstream DataStream is usually responsible for assigning these properties.
The following code shows how to define other types of windows:
// using processing-time
table.window(Tumble over 100.rows as 'manyRowWindow)
// using event-time
table.window(Session withGap 15.minutes on 'rowtime as 'sessionWindow)
table.window(Slide over 1.day every 1.hour on 'rowtime as 'dailyWindow)
Since batch is just a special case of streaming (where a batch happens to have a defined start and end point), it is also possible to apply all of these windows in a batch execution environment. Without any modification of the table program itself, we can run the code on a DataSet given that we specified a column named “rowtime”. This is particularly interesting if we want to compute exact results from time-to-time, so that late events that are heavily out-of-order can be included in the computation.
At the moment, the Table API only supports so-called “group windows” that also exist in the DataStream API. Other windows such as SQL’s OVER clause windows are in development and planned for Flink 1.3.
In order to demonstrate the expressiveness and capabilities of the API, here’s a snippet with a more advanced example of an exponentially decaying moving average over a sliding window of one hour which returns aggregated results every second. The table program weighs recent orders more heavily than older orders. This example is borrowed from Apache Calcite and shows what will be possible in future Flink releases for both the Table API and SQL.
table
.window(Slide over 1.hour every 1.second as 'w)
.groupBy('productId, 'w)
.select(
'w.end,
'productId,
('unitPrice * ('rowtime - 'w.start).exp() / 1.hour).sum / (('rowtime - 'w.start).exp() / 1.hour).sum)
User-defined Table Functions
User-defined table functions were added in Flink 1.2. These can be quite useful for table columns containing non-atomic values which need to be extracted and mapped to separate fields before processing. Table functions take an arbitrary number of scalar values and allow for returning an arbitrary number of rows as output instead of a single value, similar to a flatMap function in the DataStream or DataSet API. The output of a table function can then be joined with the original row in the table by using either a left-outer join or cross join.
Using the previously-mentioned customer table, let’s assume we want to produce a table that contains the color and size preferences as separate columns. The table program would look like this:
// create an instance of the table function
val extractPrefs = new PropertiesExtractor()
// derive rows and join them with original row
table
.join(extractPrefs('prefs) as ('color, 'size))
.select('id, 'username, 'color, 'size)
The PropertiesExtractor
is a user-defined table function that extracts the color and size. We are not interested in customers that haven’t set these preferences and thus don’t emit anything if both properties are not present in the string value. Since we are using a (cross) join in the program, customers without a result on the right side of the join will be filtered out.
class PropertiesExtractor extends TableFunction[Row] {
def eval(prefs: String): Unit = {
// split string into (key, value) pairs
val pairs = prefs
.split(",")
.map { kv =>
val split = kv.split("=")
(split(0), split(1))
}
val color = pairs.find(\_.\_1 == "color").map(\_.\_2)
val size = pairs.find(\_.\_1 == "size").map(\_.\_2)
// emit a row if color and size are specified
(color, size) match {
case (Some(c), Some(s)) => collect(Row.of(c, s))
case _ => // skip
}
}
override def getResultType = new RowTypeInfo(Types.STRING, Types.STRING)
}
Conclusion
There is significant interest in making streaming more accessible and easier to use. Flink’s Table API development is happening quickly, and we believe that soon, you will be able to implement large batch or streaming pipelines using purely relational APIs or even convert existing Flink jobs to table programs. The Table API is already a very useful tool since you can work around limitations and missing features at any time by switching back-and-forth between the DataSet/DataStream abstraction to the Table abstraction.
Contributions like support of Apache Hive UDFs, external catalogs, more TableSources, additional windows, and more operators will make the Table API an even more useful tool. Particularly, the upcoming introduction of Dynamic Tables, which is worth a blog post of its own, shows that even in 2017, new relational APIs open the door to a number of possibilities.
Try it out, or even better, join the design discussions on the mailing lists and JIRA and start contributing!


{{ctrl.lastQueryMeta.sql}
Plugin Version: {{ctrl.target.version}} First, choose the datasource you wish to query. Query Type - Built Query: Essential: From - Select Table: - Click 'Select Table' and type in the table you wish to query. If not tables appear in a drop down, check your datasource is connected. Time Column - Select Time: - Click 'Select Time' and type the column you wish to use as the time axis for the graph. This field can be diabled in on table visualisation. Select - Select Column: - Click 'select column' and type the column you wish to select for your visualisation. More columns may be added using the plus button beside and typing 'Column'. - To remove a column, click 'Column:' and press 'Remove' in the drop-down. - The identification of a column can be customised by clicking the plus button bside the column and entering 'Alias'. - If conflation is used, the aggregate function applied to a column can be changed by clicking the plus button, typing 'Aggregate' and choosing from the drop-down menu. - If you are creating a table and selecting a time coloumn, you may need to change the 'type' to 'time' in the visualisation menu. Row Limit: - This limits the number of records that can be returned by the server to aid the performance of Grafana. If row limit is reached, either change the number or use conflation. Optional: Where - Expression: - Click the plus button and type 'Expression' to add a where-clause template. - Each element can be changed in the where-clause by clicking on them. - Click 'Expr:' and click 'Remove' to remove an expression. Grouping - Select Field: - To enable grouping, check the box. Then click 'Select Field', type the field by which to categorise your data. Conflation - Duration, Units, Default Aggregation: - To enable conflation, check the box. Then adjust the time-buckets by changing the number and units. - The Default Aggregation is the operation applied across the records per time-bucket. This can be overriden under the 'Select Column' section by using the plus button. Example of using Alias, Where and Conflation: FROM: trade TIME COLUMN: time SELECT: Column: price; Alias: GOOG WHERE: Expr: sym = GOOG USE CONFLATION: Duration - 30; Units - Seconds; Aggregation - Last Query Type - Free-form Query: Esential: Function - Enter function: - Enter a kdb select statement into the textbox. - If using graph visualisation, ensure time is selected in your statement. Time Column - time (Graph visualisation): - Enter here the time column against which Grafana will plot your timeseries data. - This can be disabled in table visualisation using the checkbox. Optional: Grouping - Select Field: - In the function, ensure to select the column by which to group your data. DO NOT put it in the by-clause of the function for graphical visualisation. - Type the column name into the grouping field. Conflation - Duration, Units, Default Aggregation: - To enable conflation, check the box. Then adjust the time-buckets by changing the number and units. - The Default Aggregation is the operation applied across the records per time-bucket. - This is not currently independently configurable. To aggregate columns differently, add duplicate your query and change the column and aggregation. Query Type - Function: - Click 'Select Function' and type/choose the function to be used. - The plugin will attempt to detect the number of arguments and create segments to be adjusted. If this fails, more argument segments can be added using the plus button. - Conflation and row limit functions are explaining in the Built Query mode.
ctrl.FunctionCode
{{ctrl.target.lastQueryError}}


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Look at the data in the other pages and fix errors as you find them
This is an idea of how you might do the backend of a site. All of the authentication/admin stuff is missing, as are forms for entering orders and customer details. You just see what the orders are, and who ordered what.
Supply a dashboard to show the number of orders, and the amount spent by customers.


Write Your Tiltfile¶
A Tiltfile can be broken down in to three things: Kubernetes YAML, Docker images and resources. In this doc we’ll step through all three concepts so you can learn to write your own Tiltfiles with gusto!
To start here’s an illustrative example that uses most of the features of Tilt.
## Part 1: Kubernetes YAML
# one yaml file
k8s_yaml("k8s.yaml")
# list of yaml files
k8s_yaml(['foo.yaml', 'bar.yaml'])
# run kustomize to generate yaml
k8s_yaml(kustomize('config_dir'))
# run a custom command to general yaml
k8s_yaml(local('gen_k8s_yaml.py'))
## Part 2: Images
# docker build ./frontend
docker_build("companyname/frontend", "frontend")
# docker build with specific Dockerfile
docker_build("companyname/backend", "backend", dockerfile="backend/Dockerfile.dev")
# docker build with build args
docker_build("companyname/graphql", "graphql", args={"target": "local"})
## Part 3: Resources
# give a resource a name that's different than the base name of the image
k8s_resource("backend", image="companyname/backend/server")
# connect to a specific local port
k8s_resource("frontend", port_forwards=9000)
Let’s dig in to each of these sections in detail.
Part 1: Kubernetes YAML¶
Start by telling Tilt about your Kubernetes YAML:
# one yaml file
k8s_yaml("k8s.yaml")
Now Tilt will deploy any Kubernetes objects defined in the YAML. A k8s_yaml
function call is all you need to have a functioning Tiltfile.
Sometimes you organize your YAML in to multiple files or generate it via a script. Tilt supports those cases too:
# list of yaml files
k8s_yaml(['foo.yaml', 'bar.yaml'])
# run kustomize to generate yaml
k8s_yaml(kustomize('config_dir'))
# run a custom command to general yaml
k8s_yaml(local('gen_k8s_yaml.py'))
Part 2: Images¶
Next tell Tilt about all your Dockerfiles but to get the most out of Tilt you need to tell it about your Dockerfiles. You tell Tilt to build a Dockerfile like so:
# docker build ./frontend
docker_build("companyname/frontend", "frontend")
Tilt extracts the Kubernetes objects that reference this image in to a new “Resource”, discussed below.
Options to Docker build¶
You can also specify advanced configuration options, such as custom Dockerfile paths or build arguments:
# docker build ./frontend
docker_build("companyname/frontend", "frontend")
# docker build with specific Dockerfile
docker_build("companyname/backend", "backend", dockerfile="backend/Dockerfile.dev")
# docker build with build args
docker_build("companyname/graphql", "graphql", args={"target": "local"})
Part 3: Resources¶
Tilt automatically defines Tilt Resources. Tilt Resources represent logical groupings of images and Kubernetes YAML. Tilt by default takes the last path segment in your image name and uses that as the name of the resource. So for example:
# name: server
docker_build("companyname/backend/server", ".")
If you want to modify Tilt Resources you can make them explicit.
# by default, resources are named after the basename of the image (here, `server`).
# This gives it a different name (`backend`)
k8s_resource("backend", image="companyname/backend/server")
You can also add a Kubernetes port forward to this resource.
# connect to a specific local port
k8s_resource("backend", image="companyname/backend/server", port_forwards=9000)
Next steps¶
That’s it! We just covered everything you need to know to get your microservices running locally using Tilt. To see everything you can do with Tilt check out the Tiltfile API reference.


Introduction
Vulkan is a new low-level API for 3D graphics and GPU-based computations, which was brought to us by Khronos Group. Its low-level abstraction allows a developer to work more closely to the hardware giving wider control on handling hardware, and hence achieving the increased performance of a graphics application if programmed well.
Vulkan is designed to reduce CPU overhead and latency, reaching new level of performance. Simply speaking, it is able to avoid CPU bottlenecks because of its low CPU usage and its ability to distribute work efficiently among multiple CPU cores. Because of this, Vulkan is able to draw out the maximum potential of GPU.
Vukan is often seen as a competitor to Microsoft's new DirectX 12. Although they both have a different approach internally as APIs, they are both targeted towards the sumilar goal—massive framerate increase in games, drastic decrease in CPU workload (low CPU overhead), and better use of CPU's multiple cores.
Built Upon AMD's Mantle API
AMD's Mantle has been used as a foundation for Vulkan API. Mantle is also a low-level API targeted at 3D graphics espically video games. The goals of AMD for Mantle were to achieve higher framerates, reduced rendering latency, reduced GPU power consumption, and better handling of multi-core CPUs. However, with its idea of proprietary trademark and support for only AMD graphic cards, the game support list was pretty small, and it was bound to fall.
That's where Vulkan API came into the scene. Before Mantle fell completely, it was miraculuously resurrected as a Vulkan. Vulkan has assimilated the 'best and brightest' parts of Mantle while still being a non-proprietary tool supporting many Operating Systems, and both Nvidia and AMD graphic cards.
Successor To OpenGL
OpenGL is also a graphics redering API initially developed by Silicon Graphics Inc. (SGI) and now managed by Khronos Group. OpenGL has been constantly evolving for over two decades and has numerous improvements in that time. However, only doings patchups and improvements on same base will not always be enough. Sometimes, a full rework has to be done to beget something more game changing.
Yes, that's how Vulkan API came into being, and seen as a successor to OpenGL. Also, it was initially referred to as "next generation OpenGL initiative".
Used In 3D Graphics Devices and Applications
Vulkan can be used in various graphics devices—from tiny wearables and drones to 3D printers to cars to VR kit to anything that has vulkan-compatible GPU inside.
Currently, Vulkan is being used in gaming industry. Games like The Talos Principle, W.N.C: Infantry, and Ashes Of The Singularity are on the Vulkan support line for now. Although the game support list is pretty small right now, many professionals foresee the high potential graphics API being used more extensively across many games over different platforms in near future.


| Week | Date | Time | Day | Subject |
|---|---|---|---|---|
| 5 | 2/2 - 2017 | 11:00 - 15:45 | #1 | Introduction to Web Development with ASP.NET Core MVC |
| 6 | #2a | Brush Up Week: 'No teachings, but do these tutorials and exercises' | ||
| 7 | 16/2 - 2017 | 8:30 - 23:59 | #2b | Mandatory Assignment 1 |
| 8 | 23/2 - 2017 | 11:00 - 15:45 | #3 | MVC |
| 10 | 9/3 - 2017 | 11:00 - 15:45 | #4 | Entity Framework & Strongly Typed Views |
| 12 | 23/3 - 2017 | 11:00 - 15:45 | #5 | Entity Framework |
| 14 | 6/4 - 2017 | 11:00 - 15:45 | #6 | Client-Side Development (Bootstrap, grid system, Mediaquirries) |
| 17 | 27/4 - 2017 | 11:45 - 15:45 | #7 | Repositories, Query related Database Tables, Boostrap Grid System |
| 18 | 4/5 - 2017 | 11:00 - 15:45 | #8 | View Models, Partial Views, Repositories |
| 19 | 10/5 - 2017 | 08:30 - 12:30 | #9 | |
| 19 | 11/5 - 2017 | 11:00 - 15:45 | #10 | RESTFul API, JSON & |
| 22 | 31/5 - 2017 | 08:30 - 12:30 | #12 | Mandatory Assignment 2 work & Feedback |
| 22 | 1/6 - 2017 | 11:00 - 15:45 | #13 | Mandatory Assignment 2 |
| 24 | 15/6 - 2017 | 11:00 - 15:45 | #14 | Q&A before the exame |


{% extends 'helpcentre-detail.html' %} {% block pageTitle %} How to add a remembered device {% endblock %} {% block beforeContent %} {% endblock %} {% block content %}
When you log in and enter your 6-digit security code, you have the option to select ‘remember this device and stop sending security codes’. If you select this, you will not need to enter a security code each time you log in with the same device in future.
To keep your NHS login secure, you should only do this on your own personal or trusted devices. For security, we may ask you from time to time if you still want us to remember your device.


Self-taught and unrivaled in motivation, from corporate applications and full stack web services to data analysis and machine learning, my goal is to provide you with the best return on your investment.
See My SkillsHTML / Pug
CSS / Bootstrap 4
JavaScript
jQuery
Express.js
PHP
SQL
MongoDB / Mongoose
C#
R
Each skill was developed on a minimum of one project, some of which are in commercial use now. All skills are fundamentally developed and project-ready.
See My Key TraitsAny knowledge gaps are quickly filled with effective and efficient learning through my team and self-study on my own time.
During employment, I will constantly be enrolled in certification and training programs on latest practices.
Through employment in the US, Europe, and Asia, I have developed a thorough understanding of the role of culture and language in teams.
My drive and eagerness to learn are my true advantages that have enabled me to learn multiple spoken languages and programming languages independently.
Native English with advanced grammar and business certification along with entirely self-taught business German and Japanese provide a major language for every continent.
With a background in business development, international marketing, sales, logistics, finance, and entrepreneurship, I am capable of providing support for all business-related matters.
Currently in Japan with proper visa and ability to relocate ensures a smooth hiring process.
Desire to stay in Japan and settle into a surrounding community provide secure employment options minimally influenced by outside circumstances.
Direct contact is available for all potential employers by e-mail.


This track contains chromatin interaction data from the University of Washington ENCODE group generated using 5C (Chromatin Conformation Capture Carbon Copy). The 5C method is used here to define short and long-range range interactions between transcription start sites (TSS) and DNaseI hypersensitive sites (DHS) or other genomic features. The 5C method is summarized below.
Transcription factors bind to promoter-associated proteins, bringing the associated DNA sequences in close proximity to each other. Cross linking the DNA and proteins immobilizes these interactions and thus maintains their close proximity. Cleavage of the sample with restriction endonuclease followed by ligation results in hybrid molecules where a fragment with a regulatory element is physically associated with a fragment containing a TSS. The interactions are then detected by oligonucleotide-dependent, ligation-mediated assays, where one set of primers is complementary to the end of fragments with a TSS and the second set of primers are complementary to fragments with a feature. Primers are designed to the forward strand of the feature and the reverse strand of the TSS so that ligation only occurs between TSS and feature, not between different features. Specific interactions are detected by massively parallel sequencing.
The data in this track comprises two different experiment types focusing on targeted regions:
Gene-targeted project
Analysis of DNase I hypersensitive sites reveals many genes where there are multiple sites restricted to the cell type where a protein is observed to be expressed. These sites potentially identify regulatory sites for the gene. This set of experiments attempts to observe interactions between these DHS sites and transcription starts in 25 regions selected based on genes expressed in GM06990 (B-lymphocyte), BJ (foreskin fibroblast), HepG2 (liver cancer cell line), or SK-N-SH_RA (neuroblastoma cell line, SKNSH, differentiated with retinoic acid).
Myc projectGenome wide association studies have identified SNPs linked to prostate, colon, and breast cancer in the gene desert region upstream of the myc gene. 5C of HindIII fragments interacting with those containing refSeq txStarts in this region were performed in 5 cell types: GM12878 (B-lymphocyte), CaCo2 (colon cancer cell line), LNCaP (prostate cancer cell line), MCF7 (breast cancer cell line), and K562 (erythroleukemia cell line).
The following types of data are available for download:
Cells were grown according to the approved ENCODE cell culture protocols. The isolated nuclei were formaldehyde cross-linked. The DNA isolated from the nuclei was cleaved with restriction enzyme, ligated, and cross-links removed to create a 3C library (Dekker et al., 2002). Primers complementary to the TSS and feature were added, annealed and ligated to produce a 5C library (Dostie et al., 2006). The DNA fragments generated in the ligation mediated-reactions were partially digested with DNaseI, end-repaired and ligated to adapters, before sequencing. The sequencing reads generated were mapped to the predicted ligation products. The number of sequences mapping to predicted junction fragments were tabulated from sequencing runs. The number of times a sequence was detected for a given interaction between a TSS and feature indicates the relative strength of the interation.
Gene-targeted projectForward primers were designed to HindIII sites in a 230-415 kb sequence centered on the DNase I hypersensitive sites of interest. Reverse primers were designed to HindIII sites for all transcription starts extending 1 Mb on either side of the region targeted by the forward primer set. Matrix files are labeled by the coordinates of the region covered by the forward primer set. These experiments were done in a multiplex manner with the forward and reverse primers for all 25 regions mixed together in a single reaction. Two replicates were performed for 4 cell lines for 25 regions. High-throughput sequencing was performed on an ABI SOLiD instrument collecting 50 bp reads. The interaction files provided map all the reads in the output sequence without a mismatch threshold.
Myc projectForward primers were designed to HindIII fragments of 4.29 Mb section of human chromosome 8 centered on the gene desert 5~R of the myc gene. Reverse primers were designed to all HindIII fragments containing refseq txStarts in a 7.6 Mb region extending > 2 Mb on either side of the forward primer set. High-throughput sequencing was performed on an ABI SOLiD instrument collecting 50 bp reads. The interaction files provided map all the reads in the output sequence without a mismatch threshold.
Data were verified by sequencing biological replicates displaying correlation coefficient > 0.9.
These data were generated by the University of Washington ENCODE Group.
Contact: Richard SandstromDekker J, Rippe K, Dekker M, Kleckner N. Capturing chromosome conformation. Science 2002 Feb 15;295(5558):1306-11.
Dostie J, Richmond TA, Arnaout RA, Selzer RR, Lee WL, Honan TA, Rubio ED, Krumm A, Lamb J, Nusbaum C et al. Chromosome Conformation Capture Carbon Copy (5C): a massively parallel solution for mapping interactions between genomic elements. Genome Res 2006 Oct;16(10):1299-309.
Data users may freely use ENCODE data, but may not, without prior consent, submit publications that use an unpublished ENCODE dataset until nine months following the release of the dataset. This date is listed in the Restricted Until column, above. The full data release policy for ENCODE is available here.


You Can Watch ‘Pee Wee’s Big Adventure’ With Pee Herman in Texas
by Stryker'THE STARS AT NIGHT ARE BIG AND BRIGHT!'
Coming up in 2020, it will be the 35th anniversary of the classic movie 'Pee Wee's Big Adventure'. Where Pee Wee Herman is trying to make it to Texas to get his awesome bike. Why Texas? A psychic told him that his bike was in the Alamo's basement.
As you know, the Alamo has no basement. Along the way, Pee Wee runs into some crazy characters.  Large Marge. I think I legit would fast forward through that scene for years.
It's a great movie and if you love it. You will want to be at the 35th anniversary screening over in Austin. Pee Wee Herman himself will be there. His real name is Paul Reubens in case you did not know. He will be there for the screening and afterwards will tell some behind the scenes stories.
The screening is happening March 6th, 2020 at ACL Live at The Moody Theater. Tickets go on sale this Friday. If you need to remember one thing, remember the Alamo.


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OSSL_STORE_attach - Functions to read objects from a BIO
#include <openssl/store.h>
OSSL_STORE_CTX *OSSL_STORE_attach(BIO *bio, const char *scheme,
OSSL_LIB_CTX *libctx, const char *propq,
const UI_METHOD *ui_method, void *ui_data,
const OSSL_PARAM params[],
OSSL_STORE_post_process_info_fn post_process,
void *post_process_data);
OSSL_STORE_attach() works like OSSL_STORE_open(3), except it takes a BIO bio instead of a uri, along with a scheme to determine what loader should be used to process the data. The reference count of the BIO object is increased by 1 if the call is successful.
OSSL_STORE_attach() returns a pointer to a OSSL_STORE_CTX on success, or NULL on failure.
ossl_store(7), OSSL_STORE_open(3)
OSSL_STORE_attach() was added in OpenSSL 3.0.
Copyright 2020-2021 The OpenSSL Project Authors. All Rights Reserved.
Licensed under the Apache License 2.0 (the "License"). You may not use this file except in compliance with the License. You can obtain a copy in the file LICENSE in the source distribution or at https://www.openssl.org/source/license.html.


VidGear is a High-Performance Framework that provides an all-in-one complete Video Processing solution for building real-time applications in python. VidGear provides an easy-to-use, highly extensible, Multi-Threaded + Asyncio wrapper around many state-of-the-art specialized libraries like OpenCV, FFmpeg, ZeroMQ, picamera, starlette, pafy and python-mss at its backend, and enable us to flexibly exploit their internal parameters and methods, while silently delivering robust error-handling and unparalleled real-time performance.
TAUGV-CSR stands for “Tactically Assisted Unmanned Ground Vehicle with Crawling Surveillance Robot”, can provide remote surveillance and maneuver capabilities in a variable topography. The project consists of an Unmanned Ground Vehicle (UGV) and an attached Crawling Surveillance Robot (CSR)
Manav is an autonomous Artificial Intelligence driven Robot with human body structure, behavior (biomechanics) and replicates human cognition. Cognition involves developing computational models of human behavior and improving it over the period of time. It interacts with the external environment using its prodigious Natural Language Processing Engine and a state-of-the-art Artificial Intelligence perception enhanced by various internal Deep-Learning and Computer Vision Algorithms.
KropiT (formally known as PiciT) is your everyday use app. Either you want a great DP for your social media account or a quick passport-size photo, this app will come in handy to get you the image of the desired dimension and quality. Just add your gallery of images and start cropping faces immediately. Our AI-powered App helps you do your job faster by generating Cropped and perfectly Aligned Faces from the image just by CLICKING A BUTTON!
The Idea behind creating Matson was to able to integrate various present-day Deep-Learning and Computer Vision algorithms in Jasper(i.e. an open source platform for developing always-on, voice-controlled applications and your own virtual voice assistant on Raspberry Pi) to make a hybrid of three major AI domains i.e. Speech Recognition, Deep learning, and Computer Vision all at one place so as to achieve human brain like capabilities on any existing hardware that requires onboard intelligence involving learning, recognizing, processing and executing complex deliberate responses in real-time. It made exclusively for Internet of Things connected devices with memory constraints(such as Raspberry Pi), and was created in the first place so as to able to embed it on any existing system/hardware that requires onboard intelligence on the go.
This was a research project, where I re-implemented a state-of-the-art Video Stabilization method completely from scratch in python using OpenCV & scientific libraries such as NumPy, Scipy etc., and it uses a novel MeshFlow motion model. The MeshFlow is a spatial smooth sparse motion field with motion vectors only at the mesh vertexes. The proposed method handles spatially-variant motions robustly and its performance is somewhat compareable to YouTube's Video Stabilization. It offers a great potential for a variety of Computer Vision applications such as security systems, robotics, UAVs, etc.
Automated CLI Bash Scripts to produce precompiled Raspberry pi GCC Cross & Native Compilers Binaries/Toolchains, which can be used as a native compilers for Raspberry Pi with full C++17, Or can be used as the Cross-Compiler on any Linux System to compile memory exhustive programs for Raspberry Pi Machine.
Fastest Optimized OpenCV for Rpi3 : Latest pre-compiled binary of Pre-released & Stable OpenCV (4.0.0) along with TBB (2018-Update 6) for the Raspberry Pi.


Pig Dice
Play
Player
One
Roll Dice
Hold
Player
Two
Roll Dice
Hold
Player One Score:
Player Two Score:


Big JSON Viewer Demo
Watch large JSON structures in the Browser.
On GitHub
JSON text
Simple test data
|
Large data
|
JavaScript data
Big JSON Viewer output
Open paths
Copied paths


{{ define "main" }}
We create quality software solutions and great development teams. Self-funded or venture backed (Sequoia, Google Capital), idea stage or with legacy code of 7+ years - we have helped them all build and release usable solutions.
Work with our outstanding engineers and integrate them into your processes.
Let us transform your concepts into business-ready software solutions.
One of our leadership team/co-founders is hands on with each project. This personal ownership is a key reason why clients choose to work with a boutique development studio like our’s.
Seeing is believing. Some of our best work.
Atleast one leadership team member actively works on every project.
To help you decide if we are the right fit for you.
We have active projects in JAVA, PHP, Django, Angular, Node.js, ElasticSearch, Wordpress, Firebase, Ionicframework, AWS, Lambda, Celery.
In the past, we have been able to build teams around new technologies fairly quickly. For Weddingz.in, we built a 15-people Django team in 6 months.
A well rounded software developer should be able to understand business requirements, translate it into system design and then write quality code to deliver usable and bug free features. At the same time, she needs to have good communication skills, especially in a remote + multi team setup.
We have struggled with finding all these skills in a single developer (or training them for this). Hence we prefer to have a team engagement which rounds up all the above rather than provide individual developers on hire.
Application development projects are fixed scope and cost projects. They begin with a discovery phase (1-4 weeks) where we prepare a detailed scope document and project plan. Post signoff, this then becomes the basis for a 3 to 6 month application development project. This works great for building a minimum viable product, test a new idea or build a largely independent module in your existing product.
Product development is an ongoing process and after the initial pilot, it requires a dedicated team. A well rounded team needs skills across development, front-end, project management and QA. Depending on the nature and scale of the project, the team that we put together will have a mix of dedicated and shared members.


{% extends 'weblab-master.html' %} {% block body %}
{{ gettext("Select what kind of permission you are going to grant, and to who (a group, a role, a particular user):") }}


Class Sessions
Sessions are used to tell Game Jolt when a user is playing a game, and what state they are in while playing (active or idle).
Inherited Members
Namespace: GameJolt.Services
Assembly: GameJolt.dll
Syntax
public sealed class Sessions : Service
Methods
| Improve this Doc View SourceCheck(Credentials, Action<Response>)
Checks to see if there is an open session for the user. Can be used to see if a particular user account is active in the game.
Caution: GameJolt does not distinguish between a failed request due to an error and a request that doesn't succeed because there is no open session. You can indirectly check this by looking at the message field. If Success is false and a message is set, an error has occured. If Success is false and no message is set, the query has succeeded, but there just is no open session.
Declaration
public void Check(Credentials credentials, Action<Response> callback)
Parameters
| Type | Name | Description |
|---|---|---|
| Credentials | credentials | The user's credentials. |
| System.Action<Response> | callback | Action that is called on completion or error. |
CheckAsync(Credentials)
Checks to see if there is an open session for the user. Can be used to see if a particular user account is active in the game.
Caution: GameJolt does not distinguish between a failed request due to an error and a request that doesn't succeed because there is no open session. You can indirectly check this by looking at the message field. If Success is false and a message is set, an error has occured. If Success is false and no message is set, the query has succeeded, but there just is no open session.
Declaration
public Task<Response> CheckAsync(Credentials credentials)
Parameters
| Type | Name | Description |
|---|---|---|
| Credentials | credentials | The user's credentials. |
Returns
| Type | Description |
|---|---|
| System.Threading.Tasks.Task<Response> |
Close(Credentials, Action<Response>)
Closes the active session.
Declaration
public void Close(Credentials credentials, Action<Response> callback)
Parameters
| Type | Name | Description |
|---|---|---|
| Credentials | credentials | The user's credentials. |
| System.Action<Response> | callback | Action that is called on completion or error. |
CloseAsync(Credentials)
Closes the active session.
Declaration
public Task<Response> CloseAsync(Credentials credentials)
Parameters
| Type | Name | Description |
|---|---|---|
| Credentials | credentials | The user's credentials. |
Returns
| Type | Description |
|---|---|
| System.Threading.Tasks.Task<Response> |
Open(Credentials, Action<Response>)
Opens a game session for a particular user and allows you to tell Game Jolt that a user is playing your game. You must ping the session to keep it active and you must close it when you're done with it.
Declaration
public void Open(Credentials credentials, Action<Response> callback)
Parameters
| Type | Name | Description |
|---|---|---|
| Credentials | credentials | The user's credentials for whom a session should be opened. |
| System.Action<Response> | callback | Action that is called on completion or error. |
OpenAsync(Credentials)
Opens a game session for a particular user and allows you to tell Game Jolt that a user is playing your game. You must ping the session to keep it active and you must close it when you're done with it.
Declaration
public Task<Response> OpenAsync(Credentials credentials)
Parameters
| Type | Name | Description |
|---|---|---|
| Credentials | credentials | The user's credentials for whom a session should be opened. |
Returns
| Type | Description |
|---|---|
| System.Threading.Tasks.Task<Response> |
Ping(Credentials, Boolean, Action<Response>)
Pings an open session to tell the system that it's still active. If the session hasn't been pinged within 120 seconds, the system will close the session and you will have to open another one. It's recommended that you ping about every 30 seconds or so to keep the system from clearing out your session. You can also let the system know whether the player is in an active or idle state within your game.
Declaration
public void Ping(Credentials credentials, bool active, Action<Response> callback)
Parameters
| Type | Name | Description |
|---|---|---|
| Credentials | credentials | The user's credentials. |
| System.Boolean | active | Whether the user is active or not. |
| System.Action<Response> | callback | Action that is called on completion or error. |
PingAsync(Credentials, Boolean)
Pings an open session to tell the system that it's still active. If the session hasn't been pinged within 120 seconds, the system will close the session and you will have to open another one. It's recommended that you ping about every 30 seconds or so to keep the system from clearing out your session. You can also let the system know whether the player is in an active or idle state within your game.
Declaration
public Task<Response> PingAsync(Credentials credentials, bool active)
Parameters
| Type | Name | Description |
|---|---|---|
| Credentials | credentials | The user's credentials. |
| System.Boolean | active | Whether the user is active or not. |
Returns
| Type | Description |
|---|---|
| System.Threading.Tasks.Task<Response> |


| Pre-Rinse |
Tank Temperature: 10C - 40C
Pump Pressure: 0.5 bar - 1.0 bar
Duration: Greater than or equal to 1 min |
|
| Caustic |
Tank Temperature: 50℃ - 90℃
Pump Pressure: 0.5 bar - 1.0 bar
Duration: Greater than or equal to 5 min
|
|
| Acid |
Tank Temperature: 10℃ - 80℃
Pump Pressure: 0.5 bar - 1.0 bar
Concentration: Greater than 22% |
|
| Final WFI Rinse |
Tank Temperature: Greater than 60℃
Conductivity: 0.0 - 3.0µS/cm
|
|
| Drying |
Air Temperature: 100℃ - 130℃
Duration: Greater than or equal to 13 mins
|
|


My projects
Config loader
Provides a loader for YAML, TOML and JSON configuration files.
HttpServer
A simple HTTP server for PHP.
Toml parser for PHP
A PHP parser for TOML compatible with TOML v0.4.0.
Provides a loader for YAML, TOML and JSON configuration files.
A simple HTTP server for PHP.
A PHP parser for TOML compatible with TOML v0.4.0.


healthcare-ai provides a clean interface to create and compare multiple models on your data and then deploy the model that is most accurate. healthcareai also includes functions for data exploration, data cleaning, and model evaluation.
Modeling is done in a three-step process: First, loading, understanding, and preparing the data. Second, tuning and selecting a best model. Third, making predictions, deploying the model, and monitoring over time.
Load data
selectData
to pull data directly from a SQL
database
Profile data
control_chart
to plot data over time.
pivot
if you multiple rows for each observation you want
a prediction on (e.g. multiple lab values for patients on separate rows), pivot
can help reshape the data.
Prepare data for modeling
missingness
to examine how much of each variable is missing.
impute
to impute missing values, or...
prep_data
to impute and perform additional data
manipulation to get data ready for machine learning.
split_train_test
to split data into one data frame for
developing a model and another for evaluating the model.
Tune machine learning models
machine_learn
does all the work for you, from imputing
missing values to finding optimal models via cross validation. If you want to
keep the guts under the hood (apologies for the mixed metaphor!),
this is the function for you!
tune_models
gives you more control over how models are trained.
Assess models
machine_learn
and tune_models
both return
model_list
objects. You can get high level information about your models by simply
printing the model_list object (i.e. type the name of the variable holding the models).
summary(model_list)
provides even more information about model tuning.
plot(model_list)
plots model performance over hyperparameter space.
Make predictions and deploy the model
predict.model_list
generates predictions
writeData
to push the predicted values into a SQL environment.


Select to compare:
Click a button then the map to select the nearest station
Select other crimes to overlay on to map and compare in graphs
Select type of chart to be displayed


High Court raps BBMP for failing to begin tree census in city despite having time
by Bangalore Mirror BureauThe High Court on Tuesday expressed its displeasure at the Bruhat Bangalore Mahanagara Palike (BBMP) for failing to initiate the tree census in the city.
The civic agency was ordered in June this year by the Court to undertake the survey.
Despite giving sufficient time, the survey has not begun.
During the hearing of a petition in this regard on Wednesday, the court said it would have to initiate contempt of court proceedings against the Government and the BBMP for their failure to follow court orders.


Colleges and Universities - Poland
(C)Copyright 1995,1996 Christina DeMello. Reproduction and distribution are
permissible for non-profit purposes only, but no changes are to be
made to this document without the author's written consent.
Please send additions, corrections, and comments to<EMAIL_ADDRESS>Please
give me a few days to respond as I no longer have a
direct net connection at work every day. Also, this list may be
updated more quickly than I can respond personally to email.
Last modified: Thu Jul 11 20:53:17 1996
- Academy of Economics, Wroclaw
- Adam Mickiewicz University
- American Academy of English
- College of Science, Warsaw
- Cracow University of Technology
- Franco-Polish School of New Information and Communication Technologies
- Gdansk Medical Academy
- Gdansk Technical University
- Gdynia Maritime Academy
- Jagiellonian University
- Katolicki Uniwersytet Lubelski (KUL)
- Lodz Technical University
- Maria Curie-Sklodowska University
- Nicolaus Copernicus University
- Pedagogical University of Krakow
- Politechnika Poznanska
- Politechnika Szczeciñska
- Silesian Technical University
- Stanislaw Staszic University of Mining And Metallurgy
- Uniwersytetu Gdanskiego
- Warsaw School of Economics
- Warsaw University
- Warsaw University of Technology
- Wroclaw Technical University
Return to Top Level
<EMAIL_ADDRESS><EMAIL_ADDRESS>Last updated:
Mon Jul 15 00:42:25 PDT 1996


Seaman injured in stormy seas, died
by NIKOLAY TORKINBulk carrier ALMA contacted MRCC Corsen, France, and requested medical assistance to 57-year old Ukrainian crew in critical condition, injured reportedly, due to rough seas conditions, at 2110 UTC Nov 22 in Celtic sea some 150 nm west of Brest. Paramedics team was delivered on board of bulk carrier by helicopter, at 0140 UTC Nov 23 paramedics pronounced seaman’s death. The ship interrupted her voyage from UK to Cuba and headed for Brest, to undergo all formalities. Exact cause of mortal accident unknown, it may be anything, from wave hit to fall or hit by some heavy object.
Bulk carrier ALMA, IMO 9231676, dwt 30085, built 2002, flag Malta, manager ALLOCEANS SHIPPING CO LTD, Greece.


root@haktuts:~$ pwd
[pwd will show the Present working directory]
root@haktuts:~$ ls
[ls will show the list of file and directory of present working directory.]
root@haktuts:~$ ls b*
[ls b* will show the list of file and directory starting with b and end with anything.]
root@haktuts:~$ ls -l
[Listing of all files and directory along with attributes(i.e. more detailed format like file permission,file size etc.]
root@haktuts:~$ ls -a
[Listing of all files and directory along with hidden file or file starting with periods (i.e. file stating with * like *bashrc]
root@haktuts:~$ ls -R
[show the listing of file and directory in tree structure.]
root@haktuts:~$ cat Haktuts.txt
[user can use the command cat to see the preview of Haktuts.txt file.]
root@haktuts:~$ cat > Hak.txt
[use to create file]
root@haktuts:~$ cat Haktuts.txt Hak.txt >>new.txt
[transfer the content of Haktuts.txt and Hak.txt to new.txt file.]
root@haktuts:~$ cd
[To change the directory.]
root@haktuts:~$ cd com
[go to directory name com .]
root@haktuts:~$ cd ..
[change the directory one level back.]
root@haktuts:~$ cd ../..
[change the directory two level back.]
root@haktuts:~$ cd ~
[change the directory to home directory.]
root@haktuts:~$ Vi <filename>
[It will create a Blank file.]
root@haktuts:~$ Vi <file1> <file2> <file3>
[It will create multiple file at once.]
root@haktuts:~$ Vi file1.txt
[to open file1.txt using Vi]
Command For Saving and editing the file in Vi Editor
[i- to insert text in file1.txt]
[press Shift+: and then type wq to save and quit.
: q to exit without any changes in file
: x to save changes and exit.]
root@haktuts:~$ mkdir com
[It will create a directory name com]
root@haktuts:~$ mkdir com lap mob
[To create multiple directories]
root@haktuts:~$ mkdir -p com/lap/mob
[To create a directory with their subdirectory]
root@haktuts:~$ cd -
[go to last working directory.]
[eg:currently user in /home/Haktuts/Desktop directory and user last working directory is /root/Desktop . so as and when user type cd - user directly go to /root/Desktop directory.]
root@haktuts:~$ rm Haktuts.txt
[rm remove the file Haktuts.txt.]
root@haktuts:~$ rmdir com
[rmdir remove the directory name called com only if the com directory is empty.]
root@haktuts:~$ rm -rf com
[rm -rf remove the directory and also the content or data available in directory.]
root@haktuts:~$ cp Hak.txt /root/Desktop
[copy the file Hak.txt to /root/Desktop Directory.]
root@haktuts:~$ cp -r com /root/Desktop
[copy the directory com to /root/Desktop directory.]
root@haktuts:~$ mv Haktuts.txt /home
[move the file Haktuts.txt to /home directory]
root@haktuts:~$ mv Hak.txt Hak2.txt
[rename the file Hak.txt to Hak2.txt]
© 2014 HaKTuts. All rights reserved.


Travel
Back to Welcome
Michigan
Mexico
Canada
Washington
Michigan
Mexico
Canada
Washington
Back to top


top
created using DESMO-J Version 2.5.1e at Fri Jul 23 14:42:37 CEST 2021 - DESMO-J is licensed under Apache License, Version 2.0


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William Kimbel, Director
Julie Russ, Assistant Director
Lindsay Mullen, Program Manager
Arpit Jaiswal-iOS
Gowtham Nayak-Desktop
Mihir Bhatt-Android
Senthamil Sindhu-Desktop
Sweta Singhal-iOS
Timothy Lindquist, Faculty Advisor
Krishna Tanmaya Nelapatla-iOS
Barathi Baburaj-Android
Timothy Lindquist, Faculty Advisor
Thanks to the mobile app team
Jennifer Davidson
Kyle Johnson
April Lenard
Harold Ridley
Richard Whitehouse, Faculty Advisor
Thank you IHO graduate student Neysa Grider-Potter for the "Lucy" facts.
Special thanks to IHO Board Member Ross Leventhal for supporting this project!


Monitor and Control your Activity and Food
Current Weight: 140 .lbs
Add your daily meals :
Add your daily excercise :
How many hours have you slept today ?
Log your current weight :
Today's Weight :


Alloy - Messaging Demo
Host this file on Domain A (currently hardcoded to Nate's machine name of http://miles.local)
Hello, my little child frame on another domain. You're all grown up now and living on your own domain. Why don't you call more often?


Sed ut perspiciatis unde omnis iste natus error sit
Address : Sed ut perspiciatis unde omnis istte natus error sit volutptatem
TEL : 03-1234-5678
Traffic : Train
But I must explain to you how all this mistaken idea of denouncing pleasure and praising pain was born and I will give you a complete account of the system, and expound the actual teachings of the great explorer of the truth, the master-builder of human happiness. No one rejects, dislikes, or avoids pleasure itself, because it is pleasure, but because those who do not know how to pursue pleasure rationally encounter consequences that are extremely painful. Nor again is there anyone who loves or pursues or desires to obtain pain of itself, because it is pain, but because occasionally circumstances occur in which toil and pain can procure him some great pleasure. To take a trivial example, which of us ever undertakes laborious physical exercise, except to obtain some advantage from it? But who has any right to find fault with a man who chooses to enjoy a pleasure that has no annoying consequences, or one who avoids a pain that produces no resultant pleasure?
Idea of denouncing pleasure and praising pain was born and I will give you a complete account of the system, and expound the actual teachings of the great explorer of the truth, the master-builder of human happiness. No one rejects, dislikes, or avoids pleasure itself, because it is pleasure, but because those who do not know how to pursue pleasure rationally encounter consequences that are extremely painful. Nor again is there anyone who loves or pursues or desires to obtain pain of itself, because it is pain, but because occasionally circumstances occur in which toil and pain can procure him some great pleasure. To take a trivial example, which of us ever undertakes laborious physical exercise, except to obtain some advantage from it? But who has any right to find fault with a man who chooses to enjoy a pleasure that has no annoying consequences, or one who Avoids a pain that produces no resultant pleasure?
URL : http://playsense.me
Tags : ***, ****, ******, ****


Beta SCSS
black
grey-darkest
grey-darker
grey-dark
grey
grey-light
grey-lighter
grey-lightest
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red-light
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orange-light
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yellow-light
yellow-lighter
yellow-lightest
green-darkest
green-darker
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green
green-light
green-lighter
green-lightest
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teal-darker
teal-dark
teal
teal-light
teal-lighter
teal-lightest
blue-darkest
blue-darker
blue-dark
blue
blue-light
blue-lighter
blue-lightest
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indigo-darker
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