### CNS*2021 Satellite Tutorial SA4 ## Methods from Data Science for Model Simulation, Analysis, and Visualization **Schedule:** 8-11:30am New York time on June 30, 2021 1. *Cengiz Gunay*, "From High Performance Computing to Hadoop and Spark" 2. *Anca Doloc-Mihu*, "High-dimensional data visualizations" (Lectures followed by hands-on practice and 15 min break)
### CNS*2021 Tutorial SA4 - Session 1 ## From High Performance Computing to Hadoop and Spark #### Cengiz Gunay, June 30, 2021
### Data! - Prediction: the world will have 44 _zettabytes_ by year 2020`$^*$` - Scale: mega, giga, tera, peta, exa, zetta (`$10^{21}$`) Data producers: - NY Stock Exchange: 4-5 terabytes/day - Facebook: 7 petabytes/month - Ancestry.com: 10 petabytes - Internet Archive: 18 petabytes - Computational neuroscience: ??? `$^*$` Hadoop: The definitive guide (2015); seem to match [current data](https://techjury.net/blog/big-data-statistics/#gref)
### Large-scale simulations and data analysis in computational neuroscience Typical needs: - Running large number of simulations (e.g. parameter search) - Analyzing large batches of data - Others? Workflows commonly used: 1. High-performance computing clusters (SGE, PBS, NSG, ...) 2. Cloud deployment of virtual machines (AWS, GCP, Azure, ...) 3. Specialized distributed computing platforms (Hadoop, Spark, ...)
### High-performance computing (HPC) Classical *scientific computing* environment: - Cluster of similar computers - Submit "jobs", scheduler software will run on available nodes - Results can be collected on network drives, or collected at end Cons: - Cluster usually administered by central authority - Can't install custom programs into OS, except in home folder - Must compete with other users and wait turn in queue - Errors come with delay
### Cloud deployment of virtual machines Popular and "easy" method on several vendors: - Amazon Web Services, Google Cloud Platform, Microsoft Azure, etc - Freedom to deploy own virtual machine (VM) "images" - Can quickly create copies of same machine - No waiting, but have to pay Cons: - More expensive than the HPC approach - More administrative work needed to orchestrate VMs - Not efficient because it involves creating whole machines
### Data science field has parallel computing solutions Not unique to computational neuroscience: - Large data requires more than one machine - Parallelization is painful - Load balancing is a problem, otherwise you wait for the slowest one A general solution: - **MapReduce** algorithm, implemented in **Apache Hadoop** - Divides data and processing across physical machines - Based on 2004 paper from Google, widely adopted in industry
### A MapReduce example for finding average spike rates Let's assume our input files look like this: ``` neuronID trialnum numspikes n678 1 12 n554 3 0 n554 4 2 n678 2 15 ... ``` What would the Map and Reduce steps would look like? - Map: Associate neuron ID with number of spikes: `(n678=>12)`, ... - Reduce: Average for each neuron ID: `n678=>avg(12,15)`, ...
### What are advantages of using MapReduce? - Works with large files without any consideration - Parallelizes task on multiple machines to cut down processing time - Parallel operation is automatic! But.. - Algorithm must be converted into MapReduce
### Data is distributed automatically on the Hadoop Distributed Filesystem (HDFS) - Optimized to work on parallel hardware:
### HDFS _local_ data replication - Data shipped out to each machine via _input splits_ for Map tasks
### HDFS input _splits_ merged into output _part_
### Workflow with multiple Reduce tasks - Output of *map* can be used for different *reduce* steps:
### Back to spike rates example for multiple reduce tasks Keep single map step: - Map: Associate neuron ID with number of spikes: `(n678=>12)`, ... Add multiple reduce steps: 1. Reduce: **Average** for each neuron ID: `n678=>avg(12,15)`, ... 1. Reduce: **Max** for each neuron ID: `n678=>max(12,15)`, ... 1. Reduce: **Mode** for each neuron ID: `n678=>mode(12,15)`, ... ...
### Reduce step can be omitted - Sometimes *map* is enough for "embarassingly parallel" simulations!
### Example without reduce step Assume input files with maximal conductance parameters for model: ``` gmaxNa gmaxK1 gmaxK2 ... 5e-9 1.2e-9 1.4e-9 ... 6e-9 1.2e-9 1.4e-9 ... 7e-9 1.2e-9 1.4e-9 ... ... ``` What would a Map step look like? - Map: run simulation with parameter values, and output results file
### Hadoop is dead. Long live Hadoop! - Hadoop created an ecosystem of projects: - Avro: Data serialization system - Flume: Work with data streams - Sqoop: Interface with traditional relational DBs - Hive: SQL queries converted to MapReduce - [Pig](https://www.slideshare.net/kevinweil/hadoop-pig-and-twitter-nosql-east-2009/): Hadoop processing with custom high-level language - Parquet: Columnar storage for nested data - Crunch: High-level API for using Hadoop - [Kafka](https://kafka.apache.org/): Distributed streaming platform - [Spark](https://spark.apache.org/): Another distributed computing framework - We will give a brief intro to Spark
### Spark Features - Developed in 2009, using Scala running on JVM - Speed: - Up to 100 times faster than Hadoop - In-memory storage of intermediate results - Multiple languages supported: - Python, Scala, R, Java - Advanced Analytics: - SQL queries, Streaming data - Machine learning, Graph algorithms
### Spark and Hadoop Differences Hadoop: - Batch process - Processes data from the HDFS and writes intermediate results to HDFS - Results are written to the HDFS Spark: - Can read data from streaming, local file system, HDFS or other distributed file system
### Spark Components
### Resilient Distributed Datasets (RDD) - Fundamental data structure for Spark - Immutable distributed collection of objects - Divided into logical partitions - Can be computed on different nodes - Can contain any data type or Python, Java, or Scala - Can contain User Defined Types (UDTs)
### Spark SQL - Integrates relational processing - Supports queries in SQL and HQL (Hive Query Language) - Supports various data sources - CSV, Text, JSON, etc. - Libraries - DataSource API - DataFrame API - Interpreter and Optimizer - SQL Service
### Spark Streaming - Process real-time streaming data - Fundamental stream unit is DStream - Series of RDDs to process real-time data - Viewed as a continually growing table
### MLib (Machine Learning Library) - Tools - ML Algorithms - Classification, Regression, Clustering, Collaborative Filtering - Featurization - Feature extraction, transformations, and dimension reduction - Pipelines - Contruct and training ML pipelines - Persistence - Saving and loading algorithms, models and pipelines - Utilities - Linear algebra, statistics, data handling
### GraphX - API for graphs and graph-parallel computation - Extends RDD with a Resilient Distributed Property Graph - Can have parallel edges to define multiple relationships between vertices ### GraphFrames - Similar to GraphX but built on top of DataFrames
### DataSet and DataFrame - DataSet - Distributed collection of data - Strong typing - Available in Scala and Java - DataFrame - DataSet organized into named columns - Representation of DataSet in rows - Available in Scala, Java, Python and R
### Practice time! Connect to our Spark server - See our [Sched page](https://cns2021online.sched.com/event/kFFq/sa4-methods-from-data-science-for-model-simulation-analysis-and-visualization) for the IP address and downloads On Mac and Linux: - Download `spark-key` from [Sched](https://cns2021online.sched.com/event/kFFq/sa4-methods-from-data-science-for-model-simulation-analysis-and-visualization) - Open terminal and run: ```bash $ ssh -i spark-key cnsuser@IP address ``` On Windows: - Download `spark-windows.ppk` from [Sched](https://cns2021online.sched.com/event/kFFq/sa4-methods-from-data-science-for-model-simulation-analysis-and-visualization) - Download [Putty](https://www.chiark.greenend.org.uk/~sgtatham/putty/) and follow [instructions](https://devops.ionos.com/tutorials/use-ssh-keys-with-putty-on-windows/#connect-to-server-with-private-key) to use ppk key file - [Open SSH connection](https://www.ssh.com/ssh/putty/windows/) to IP address with username "cnsuser"
### Practice time! (Cont) Once connected to Spark server, run: ```bash $ docker exec -it klt-spark-wwym bin/pyspark ``` which should result in: ```bash Python 3.6.11 (default, Jul 15 2020, 18:47:56) [GCC 8.3.0] on linux Type "help", "copyright", "credits" or "license" for more information. Welcome to ____ __ / __/__ ___ _____/ /__ _\ \/ _ \/ _ `/ __/ '_/ /__ / .__/\_,_/_/ /_/\_\ version 3.0.0 /_/ Using Python version 3.6.11 (default, Jul 15 2020 18:47:56) SparkSession available as 'spark'. >>> ```
### Spark works with _resilient distributed datasets_ (RDDs) - Try these on the `pyspark` command line: - A line count program example: ```python >>> lines = sc.textFile("data/tutorial.md") # Create an RDD called lines >>> lines.count() # Count the number of items in this RDD 67 >>> lines.first() # First item in this RDD, i.e. first line of tutorial.md '---' ``` - Parallel operation completely transparent! - `sc` is the `SparkContext` _driver_ to access Spark and create RDDs - RDD is broken into pieces to run `count()` in parallel
### Inside Spark
- Try another example with a custom filter function: ```python >>> sparkLines = lines.filter(lambda line: "Spark" in line) # like grep on Hadoop >>> sparkLines.count() # a lower number than before >>> sparkLines.first() 'tools, such as [Apache Spark](https://spark.apache.org/). These tools' ```
### Map-Reduce operations generalize to
Transformation-Action with RDDs _Transformations_ create new RDDs; e.g.: ```python >>> sparkLines = lines.filter(lambda line: "Spark" in line) ``` _Actions_ calculate results from RDDs; e.g.: ```python >>> sparkLines.first() ```
### RDD transformations: `map()` and `filter()`
```python >>> inputRDD = sc.parallelize([1, 2, 3, 4]) # Create a numeric input RDD ``` ```python >>> outputRDD = inputRDD.map(lambda x: x * x) # MAP ``` ```python >>> outputRDD.collect() # Show all results ``` ```python >>> outputRDD = inputRDD.filter(lambda x: x != 1) # FILTER, run collect() again ```
### RDD transformations (Continued) - For `map()`, if multiple outputs for each input, then use `flatMap()` - Set operations: - `distinct()` - `union()` - `intersection()` - `subtract()` - `cartesian()` -> expensive
### RDD actions - `reduce()`: Takes two inputs and outputs one; output is a scalar ```python >>> sum = inputRDD.reduce(lambda x, y: x + y) >>> sum 10 ``` - `fold()`: Similar, also takes a _zero_ value for initialization ```python sum = inputRDD.fold(0, lambda x, y: x + y) ``` - `aggregate()` asks for accummulation and combine functions. Example that calculates a running sum and count of elements to calculate an average value: ```python sumCount = inputRDD.aggregate((0, 0), (lambda acc, value: (acc[0] + value, acc[1] + 1)), (lambda acc1, acc2: (acc1[0] + acc2[0], acc1[1] + acc2[1]))) return sumCount[0] / float(sumCount[1]) ```
### RDD actions (continued) - `collect()`: Return all elements - `count()`: Number of elements - `countByValue()`: Number of times each element occurs in RDD (histogram) - and more...
### RDDs can act as DataFrames! - Can read/write data in JSON, CSV, Hive Tables, and Parquet - _DataFrame_ holds semi-structured data, supports SQL-like querying ```python df = spark.read.json("examples/src/main/resources/people.json") # also see read.csv() # Displays the content of the DataFrame to stdout df.show() # +----+-------+ # | age| name| # +----+-------+ # |null|Michael| # | 30| Andy| # | 19| Justin| # +----+-------+ # # Select only the "name" column df.select("name").show() # Select people older than 21 df.filter(df['age'] > 21).show() ```
### Spark can run SQL queries in parallel - SQL interface to be used inside or outside Spark
(e.g. JDBC connection or from Tableau) ```python # Register the DataFrame as a SQL temporary view df.createOrReplaceTempView("people") sqlDF = spark.sql("SELECT * FROM people") sqlDF.show() # +----+-------+ # | age| name| # +----+-------+ # |null|Michael| # | 30| Andy| # | 19| Justin| # +----+-------+ ```
### Spark Dataframes can be convert from and to Pandas Dataframes - [Pandas](https://pandas.pydata.org/) provides the standard Dataframe object in Python - Enable Apache Arrow in Spark [to use Pandas objects](https://spark.apache.org/docs/latest/sql-pyspark-pandas-with-arrow.html) ```python import numpy as np import pandas as pd # Enable Arrow-based columnar data transfers spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true") # Generate a Pandas DataFrame pdf = pd.DataFrame(np.random.rand(100, 3)) # Create a Spark DataFrame from a Pandas DataFrame using Arrow df = spark.createDataFrame(pdf) # Convert the Spark DataFrame back to a Pandas DataFrame using Arrow result_pdf = df.select("*").toPandas() ```
### A Spark-ling challenge for you! Load our CSV file with measurements from model neuron simulations: ```python df = spark.read.csv("data/AnalySim.csv", header="true") # case sensitive # show info df.printSchema() df.show() ``` Question: - Use [RDD](https://spark.apache.org/docs/latest/rdd-programming-guide.html) and [Dataframe](https://spark.apache.org/docs/latest/sql-getting-started.html) operations to calculate one of min, max, average, standard deviation for one column, or an arithmetic operation between multiple columns to find a ratio, slopes, etc. - You can also filter rows based on column constraints. - Post your code and results on the Discord `#sa4` channel or Zoom.
### If you want to install Spark locally - Installation could become complex, [Hadoop and Spark](https://spark.apache.org/docs/latest/#downloading) are bundled together - Can also just download and run Docker image from [bitnami/spark](https://hub.docker.com/r/bitnami/spark/), and run it like in our example above.
### Closing notes for Spark
Spark also provides: - Real-time processing via streaming (Chapter 10) - Machine learning library _MLlib_ (Chapter 11) More resources: - [Book's Github repo with examples](https://github.com/databricks/learning-spark) - [Spark API documentation](https://spark.apache.org/docs/latest/) - [Spark Quick Start](https://spark.apache.org/docs/latest/quick-start.html) - [RDD Programming Guide](https://spark.apache.org/docs/latest/rdd-programming-guide.html)
### Questions and comments? - Conversation can continue if you post on our the Discord `#sa4` channel or post comments on our [tutorial website](https://cengique.github.io/cns2020-tutorials-website/2021/06/30/cns2021-tutorial-data-science-methods.html). - Please fill [our survey](https://ggc.az1.qualtrics.com/jfe/form/SV_b9R3rk4dE3Fxx7E) before leaving the tutorial - Thank you for coming to our tutorial! :)
### What is MapReduce? An example A weather dataset from White Chapter 2 - Live streaming data from weather stations all around world - Each row is one reading from one station at a time
### Back to the command-line: inspect the data ```bash % ls raw/1990 | head<PHONE_NUMBER>9-1990.gz<PHONE_NUMBER>9-1990.gz<PHONE_NUMBER>9-1990.gz<PHONE_NUMBER>9-1990.gz<PHONE_NUMBER>9-1990.gz<PHONE_NUMBER>9-1990.gz<PHONE_NUMBER>9-1990.gz<PHONE_NUMBER>9-1990.gz<PHONE_NUMBER>9-1990.gz<PHONE_NUMBER>9-1990.gz ```
- Many small files; can be analyzed sequentially with `awk`: ```bash % ./max_temperature.sh 1901 317 1902 244 1903 289 1904 256 1905 283 ... ```
### Enter Map+Reduce: Can be partitioned to run on parallel hardware
### Map in Java: (ID, row of text) `$\Rightarrow$` (year, temp) ```java public class MaxTemperatureMapper extends Mapper
{ private static final int MISSING = 9999; @Override public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { String line = value.toString(); String year = line.substring(15, 19); int airTemperature; if (line.charAt(87) == '+') { // parseInt doesn't like leading plus signs airTemperature = Integer.parseInt(line.substring(88, 92)); } else { airTemperature = Integer.parseInt(line.substring(87, 92)); } String quality = line.substring(92, 93); if (airTemperature != MISSING && quality.matches("[01459]")) { context.write(new Text(year), new IntWritable(airTemperature)); } } } ```
### Reduce: (year, [temps]) `$\Rightarrow$` (year, max temp) ```java public class MaxTemperatureReducer extends Reducer
{ @Override public void reduce(Text key, Iterable
values, Context context) throws IOException, InterruptedException { int maxValue = Integer.MIN_VALUE; for (IntWritable value : values) { maxValue = Math.max(maxValue, value.get()); } context.write(key, new IntWritable(maxValue)); } } ```
### Putting it all together ```java public class MaxTemperature { public static void main(String[] args) throws Exception { if (args.length != 2) { System.err.println("Usage: MaxTemperature
"); System.exit(-1); } Job job = new Job(); job.setJarByClass(MaxTemperature.class); job.setJobName("Max temperature"); FileInputFormat.addInputPath(job, new Path(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1])); job.setMapperClass(MaxTemperatureMapper.class); job.setReducerClass(MaxTemperatureReducer.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); System.exit(job.waitForCompletion(true) ? 0 : 1); } } ```
### A sample run ```bash % export HADOOP_CLASSPATH=hadoop-examples.jar % hadoop MaxTemperature input/ncdc/sample.txt output 14/09/16 09:48:39 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable 14/09/16 09:48:40 WARN mapreduce.JobSubmitter: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this. 14/09/16 09:48:40 INFO input.FileInputFormat: Total input paths to process : 1 14/09/16 09:48:40 INFO mapreduce.JobSubmitter: number of splits:1 14/09/16 09:48:40 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_local26392882_0001 14/09/16 09:48:40 INFO mapreduce.Job: The url to track the job: http://localhost:8080/ 14/09/16 09:48:40 INFO mapreduce.Job: Running job: job_local26392882_0001 14/09/16 09:48:40 INFO mapred.LocalJobRunner: OutputCommitter set in config null 14/09/16 09:48:40 INFO mapred.LocalJobRunner: OutputCommitter is org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter 14/09/16 09:48:40 INFO mapred.LocalJobRunner: Waiting for map tasks 14/09/16 09:48:40 INFO mapred.LocalJobRunner: Starting task: attempt_local26392882_0001_m_000000_0 14/09/16 09:48:40 INFO mapred.Task: Using ResourceCalculatorProcessTree : null 14/09/16 09:48:40 INFO mapred.LocalJobRunner: 14/09/16 09:48:40 INFO mapred.Task: Task:attempt_local26392882_0001_m_000000_0 is done. And is in the process of committing 14/09/16 09:48:40 INFO mapred.LocalJobRunner: map 14/09/16 09:48:40 INFO mapred.Task: Task 'attempt_local26392882_0001_m_000000_0' done. 14/09/16 09:48:40 INFO mapred.LocalJobRunner: Finishing task: attempt_local26392882_0001_m_000000_0 14/09/16 09:48:40 INFO mapred.LocalJobRunner: map task executor complete. 14/09/16 09:48:40 INFO mapred.LocalJobRunner: Waiting for reduce tasks 14/09/16 09:48:40 INFO mapred.LocalJobRunner: Starting task: attempt_local26392882_0001_r_000000_0 14/09/16 09:48:40 INFO mapred.Task: Using ResourceCalculatorProcessTree : null 14/09/16 09:48:40 INFO mapred.LocalJobRunner: 1 / 1 copied. 14/09/16 09:48:40 INFO mapred.Merger: Merging 1 sorted segments 14/09/16 09:48:40 INFO mapred.Merger: Down to the last merge-pass, with 1 segments left of total size: 50 bytes 14/09/16 09:48:40 INFO mapred.Merger: Merging 1 sorted segments 14/09/16 09:48:40 INFO mapred.Merger: Down to the last merge-pass, with 1 segments left of total size: 50 bytes 14/09/16 09:48:40 INFO mapred.LocalJobRunner: 1 / 1 copied. 14/09/16 09:48:40 INFO mapred.Task: Task:attempt_local26392882_0001_r_000000_0 is done. And is in the process of committing 14/09/16 09:48:40 INFO mapred.LocalJobRunner: 1 / 1 copied. 14/09/16 09:48:40 INFO mapred.Task: Task attempt_local26392882_0001_r_000000_0 is allowed to commit now 14/09/16 09:48:40 INFO output.FileOutputCommitter: Saved output of task 'attempt...local26392882_0001_r_000000_0' to file:/Users/tom/book-workspace/ hadoop-book/output/_temporary/0/task_local26392882_0001_r_000000 14/09/16 09:48:40 INFO mapred.LocalJobRunner: reduce > reduce 14/09/16 09:48:40 INFO mapred.Task: Task 'attempt_local26392882_0001_r_000000_0' done. 14/09/16 09:48:40 INFO mapred.LocalJobRunner: Finishing task: attempt_local26392882_0001_r_000000_0 14/09/16 09:48:40 INFO mapred.LocalJobRunner: reduce task executor complete. 14/09/16 09:48:41 INFO mapreduce.Job: Job job_local26392882_0001 running in uber mode : false 14/09/16 09:48:41 INFO mapreduce.Job: map 100% reduce 100% 14/09/16 09:48:41 INFO mapreduce.Job: Job job_local26392882_0001 completed successfully 14/09/16 09:48:41 INFO mapreduce.Job: Counters: 30 File System Counters FILE: Number of bytes read=377168 FILE: Number of bytes written=828464 FILE: Number of read operations=0 FILE: Number of large read operations=0 FILE: Number of write operations=0 Map-Reduce Framework Map input records=5 Map output records=5 Map output bytes=45 Map output materialized bytes=61 Input split bytes=129 Combine input records=0 Combine output records=0 Reduce input groups=2 Reduce shuffle bytes=61 Reduce input records=5 Reduce output records=2 Spilled Records=10 Shuffled Maps =1 Failed Shuffles=0 Merged Map outputs=1 GC time elapsed (ms)=39 Total committed heap usage (bytes)=226754560 File Input Format Counters Bytes Read=529 File Output Format Counters Bytes Written=29 ```


Watch Dave Grohl perform the wholesome ‘Here We Go Song’ on ‘Sesame Street’
He'll feature in the show's 50th season
Dave Grohl‘s appearance on Sesame Street has been shared online – watch below.
The Foo Fighters frontman recorded a track with Elmo and Big Bird last year to mark the US children’s TV show’s 50th anniversary. Images of his time on set subsequently appeared online courtesy of the series’ puppeteers.
With Sesame Street due to reach its half-century milestone as its new season premieres on November 16, Grohl’s wholesome ‘Here We Go Song’ has now been shared in full.
Arriving on YouTube earlier today (November 12), the clip sees Elmo introduce the Foos singer to viewers. “Oh, hi everybody. This is our friend Mr Dave Grohl,” says the iconic character.
“Hi, it’s nice to come here and be with my friends Big Bird and Elmo,” an enthusiastic Grohl replies.
Grohl and his new pals then travel to various US locations to meet new friends as the trio perform ‘Here We Go Song’. “All across America, whoa,” they sing in the song’s chorus. “From my street to your street, whoa.”
Earlier tonight, Foo Fighters shared the video on their official Twitter page. “Here we go @sesamestreet!” they captioned the post.
“This better be a new addition to the setlist,” said one fan in the comments section. Another wrote: “I’d heard the Foos were working on a new album… But I never thought this…”
Grohl previously appeared in 2015’s The Muppets movie, which saw him take part in a drum battle with Animal.
Foo Fighters, meanwhile, have shared a new EP of B-sides called ‘02050525’. It was released in celebration of the band’s 25th anniversary and features ‘Spill’ from the ‘In Your Honour’ era.


Hi, my name is Thomas Keyes and I am a Computer Science major from the University of Tennessee. My goals are to be involved in mobile application development, but have been exposed in many other techincal areas as well.
My passions include the sport of golf, playing guitar, and obviously, computers. My hope for this page is to show you more on who I am and what I enjoy working on apart from the resume. Hope you enjoy!


One of the biggest cities in the US.
| By the numbers | |
|---|---|
| Population (2010): | 2,195,914 |
| Metro Population (2010): | 6,313,158 |
| Area (sq mi): | 627.8 |
| Cars (2012): | 2 per household |
A 25-mile-long complex of public and private facilities.
A few hours from the Gulf of Mexico via ship.
Relies heavily on additional transportation systems
Human spaceflight training.
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Operated by The National Aeronautics and Space Administration (NASA).
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Includes labs to study those samples.
Underwater astronaut training facility.
Projected sea level rise, increased hurricane intensity, and associated storm surge may lead to further erosion, flooding, and property damage in the Southeast.
Low-lying coastal communities that sit on subsiding land are especially sensitive to sea level rise.
Areas like the ones where the Port and the Space Center are located.
Texas coastal marches and wetlands are fertile breeding grounds for a wide variety of marine life, serve to counter erosion, and help to block inland flooding. Sea level rise threatens to reduce marsh and wetland areas, depriving the Texas gulf coast of these benefits.
Climate change is projected to concentrate rainfall into more intense storms. Heavy rains may result in flooding, which could disrupt traffic, delay construction activities, and weaken or wash out the soil and culverts that support roads, tunnels, and bridges.
Rissing sea level will impact the Port and Space Center facilities.
Emerged from the President’s Climate Change Action Plan and being part of the Climate Change Preparedness and Resilience Exercise Series.
On Oct 6th, 2014, 93 participants including local, state, and federal representatives, as well as private sector, non-governmental, and academic partners.
Mayor Annise Parker challenged participants to think broadly about future climate impacts and how to handle and mitigate those impacts.
NASA Jhonson Space Center will co-lead the effort in unifying the comunity.
Our comunity needs to drive the first steps to address this problems.
Small changes can help, but some changes need to be tackled by the goverment.
reveal.js comes with a few themes built in:
Black (default) -
White -
League -
Sky -
Beige -
Simple
Serif -
Night -
Moon -
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The number_format filter formats numbers. It is a wrapper around PHP's number_format function:
200.35: 200
You can control the number of decimal places, decimal point, and thousands separator using the additional arguments:
9800.333: 9,800.33
To format negative numbers, wrap the number with parentheses (needed because of Twig's precedence of operators:
-9800.333: -9,800.33
-9800.333: -9,800.33


Cabins – our most popular option, so you’ll need to be quick. For the best value, look out for our Sleeper Advance tickets. They’re great value, so sell out fast too.
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Raw length of the text the user must type.
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Gateway
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31-Aug-18 02:59 PM
[dscrd-hush] <HUSH Bridge-Bot> [dscrdkmd] <xRobeSx> and hopefully stakeable as well
[dscrd-hush] <dukeleto> it looks very probable that BTCH will be 50/50 PoW/PoS to protect current HODLers positions while also be attractive to miners
[dscrd-hush] <dukeleto> also, we are very seriously looking at VerusHash instead of Equihash, which would mean CPU mining versus ASIC mining
[dscrd-hush] <dukeleto> BTCH could be CPU-friendly to mine and have 51% attack protection, not sure many coins have that combination
[dscrd-hush] <dukeleto> if any
[dscrd-hush] <dukeleto> it's not possible to make a new KMD AC with VerusHash right this second, but it's coming very soon. CHIPS is also looking at converting to VerusHash as well, and I am helping a bit with that to learn all the details about VerusHash internals
[dscrd-hush] <dukeleto> @Berg interesting
[dscrd-hush] <dukeleto> ASIC-resistance is such a weird marketing term
[dscrd-hush] <Berg> One of those blanket terms that piques everyones interest. Seems to be all people talk about when it comes to mining
[dscrd-hush] <dukeleto> VerusHash is CPU-optimized, that is what I like to say. CPUs mine it best. There exist some un-released GPU miners for VerusHash, but they perform worse that CPU miners, and are not compatible with using risers and you cannot CPU+GPU mine at the same time. So GPU mining Verus is pretty much not a good idea
[dscrd-hush] <dukeleto> and building an ASIC to do VerusHash faster than a recent CPU would cost bajillions of dollars
[dscrd-hush] <dukeleto> so that is why CPUs are so good. VerusHash leverages what CPUs do best, by using AES/AVX CPU instructions
[dscrd-hush] <dukeleto> VerusHash rests on decades of Intel/AMD research to make CPUs as powerful and cheap as possible. It will be a long time until VerusHash ASICs make sense, but one day they could
[dscrd-hush] <dukeleto> I lean toward VerusHash for BTCH, even as somebody who has a z9 in the mail. The process of buying a z9, the cost, the international customs and taxes, .... , it's not something the masses have access to. CPU mining with VerusHash just seems a lot more "fair", in the sense that everybody has to use the same hardware. You can't buy hw that is exponentially better than the average person can. That levels things
[dscrd-hush] <dukeleto> i think it will be cool to have Hush mineable via ASICs and then BTCH CPU-mineable, it will give us quite a cross-section of miners, people can choose either or
[dscrd-hush] <dukeleto> i also have plans to port the Hush webminer to be a BTCH webminer. VerusHash is much friendlier to webmining
[dscrd-hush] <dukeleto> i already have a prototype that can mine KMD or some asset chain in the browser, what's left is to port the current WebAssembly from Equihash to VerusHash. If anybody wants to help, let me know


Objectives
Usage of objectives
An objective function (or loss function, or optimization score function) is one of the two parameters required to compile a model:
Scala:
model.compile(loss = "mean_squared_error", optimizer = "sgd")
Python:
model.compile(loss='mean_squared_error', optimizer='sgd')
Scala:
model.compile(loss = MeanSquaredError(sizeAverage = true), optimizer = "sgd")
Python:
model.compile(loss=MeanSquaredError(size_average=True), optimizer='sgd')
Available objectives
MeanSquaredError
The mean squared error criterion e.g. input: a, target: b, total elements: n
loss(a, b) = 1/n * sum(|a_i - b_i|^2)
Scala:
loss = MeanSquaredError(sizeAverage = true)
Parameters:
sizeAverage
a boolean indicating whether to divide the sum of squared error by n. Default: true
Python:
loss = MeanSquaredError(size_average=True)
Parameters:
size_average
a boolean indicating whether to divide the sum of squared error by n. Default: True
MeanAbsoluteError
Measures the mean absolute value of the element-wise difference between input and target
Scala:
loss = MeanAbsoluteError(sizeAverage = true)
Parameters:
sizeAverage
a boolean indicating whether to divide the sum of squared error by n. Default: true
Python:
loss = MeanAbsoluteError(size_average=True)
Parameters:
size_average
a boolean indicating whether to divide the sum of squared error by n. Default: True
BinaryCrossEntropy
Also known as logloss.
Scala:
loss = BinaryCrossEntropy(weights = null, sizeAverage = true)
Parameters:
weights
A tensor assigning weight to each of the classessizeAverage
whether to divide the sequence length. Default is true.
Python:
loss = BinaryCrossEntropy(weights=None, size_average=True)
Parameters:
weights
A tensor assigning weight to each of the classessize_average
whether to divide the sequence length. Default is True.
SparseCategoricalCrossEntropy
A loss often used in multi-class classification problems with SoftMax as the last layer of the neural network. By default, input(y_pred) is supposed to be probabilities of each class, and target(y_true) is supposed to be the class label starting from 0.
Scala:
loss = SparseCategoricalCrossEntropy(logProbAsInput = false, zeroBasedLabel = true, weights = null, sizeAverage = true, paddingValue = -1)
Parameters:
logProbAsInput
Boolean. Whether to accept log-probabilities or probabilities as input. Default is false and inputs should be probabilities.zeroBasedLabel
Boolean. Whether target labels start from 0. Default is true. If false, labels start from 1.weights
Tensor. Weights of each class if you have an unbalanced training set. Default is null.sizeAverage
Boolean. Whether losses are averaged over observations for each mini-batch. Default is true. If false, the losses are instead summed for each mini-batch.paddingValue
Integer. If the target is set to this value, the training process will skip this sample. In other words, the forward process will return zero output and the backward process will also return zero gradInput. Default is -1.
Python:
loss = SparseCategoricalCrossEntropy(log_prob_as_input=False, zero_based_label=True, weights=None, size_average=True, padding_value=-1)
Parameters:
log_prob_as_input
Boolean. Whether to accept log-probabilities or probabilities as input. Default is false and inputs should be probabilities.zero_based_label
Boolean. Whether target labels start from 0. Default is true. If false, labels start from 1.weights
A Numpy array. Weights of each class if you have an unbalanced training set. Default is None.size_average
Boolean. Whether losses are averaged over observations for each mini-batch. Default is True. If False, the losses are instead summed for each mini-batch.padding_value
Integer. If the target is set to this value, the training process will skip this sample. In other words, the forward process will return zero output and the backward process will also return zero gradInput. Default is -1.
MeanAbsolutePercentageError
Compute mean absolute percentage error for intput and target
Scala:
loss = MeanAbsolutePercentageError()
Parameters:
sizeAverage
a boolean indicating whether to divide the sum of squared error by n. Default: true
Python:
loss = MeanAbsolutePercentageError()
Parameters:
size_average
a boolean indicating whether to divide the sum of squared error by n. Default: True
MeanSquaredLogarithmicError
Compute mean squared logarithmic error for input and target
Scala:
loss = MeanSquaredLogarithmicError()
Python:
loss = MeanSquaredLogarithmicError()
CategoricalCrossEntropy
This is same with cross entropy criterion, except the target tensor is a one-hot tensor.
Scala:
loss = CategoricalCrossEntropy()
Python:
loss = CategoricalCrossEntropy()
Hinge
Creates a criterion that optimizes a two-class classification hinge loss (margin-based loss) between input x (a Tensor of dimension 1) and output y.
Scala:
loss = Hinge(margin = 1.0, sizeAverage = true)
Parameters:
margin
if unspecified, is by default 1.sizeAverage
whether to average the loss, is by default true
Python:
loss = Hinge(margin=1.0, size_average=True)
Parameters:
margin
if unspecified, is by default 1.size_average
whether to average the loss, is by default True
RankHinge
Hinge loss for pairwise ranking problems.
Scala:
loss = RankHinge(margin = 1.0)
Parameters:
margin
if unspecified, is by default 1.
Python:
loss = RankHinge(margin=1.0)
Parameters:
margin
if unspecified, is by default 1.
SquaredHinge
Creates a criterion that optimizes a two-class classification squared hinge loss (margin-based loss) between input x (a Tensor of dimension 1) and output y.
Scala:
loss = SquaredHinge(margin = 1.0, sizeAverage = true)
Parameters:
margin
if unspecified, is by default 1.sizeAverage
whether to average the loss, is by default true
Python:
loss = SquaredHinge(margin=1.0, size_average=True)
Parameters:
margin
if unspecified, is by default 1.size_average
whether to average the loss, is by default True
Poisson
Compute Poisson error for intput and target
Scala:
loss = Poisson()
Python:
loss = Poisson()
CosineProximity
Computes the negative of the mean cosine proximity between predictions and targets.
Scala:
loss = CosineProximity()
Python:
loss = CosineProximity()
KullbackLeiblerDivergence
Loss calculated as:
y_true = K.clip(y_true, K.epsilon(), 1)
y_pred = K.clip(y_pred, K.epsilon(), 1)
and output K.sum(y_true * K.log(y_true / y_pred), axis=-1)
Scala:
loss = KullbackLeiblerDivergence()
Python:
loss = KullbackLeiblerDivergence()


Welcome to SCCM
Configure and manage all aspects of this service as follows:
Security Configuration
Allows you to set up the following security aspects:
- Create, modify and delete users and groups, and assign roles.
- Configure the import of users and groupts from your LDAP server.


--- permalink: /404.html layout: page title: Oops! ---
Sorry, the page you're looking for cannot be accessed


"DOCTOR "(Luke 2:46; 5:17; Acts 5:34), a teacher. The Jewish doctors taught and disputed in synagogues, or wherever they could find an audience. Their disciples were allowed to propose to them questions. They assumed the office without any appointment to it. The doctors of the law were principally of the sect of the Pharisees. Schools were established after the destruction of Jerusalem at Babylon and Tiberias, in which academical degrees were conferred on those who passed a certain examination. Those of the school of Tiberias were called by the title“rabbi,” and those of Babylon by that of “master.”
"DOCTOR "(Luke 2:46; 5:17; Acts 5:34), a teacher. The Jewish doctors taught and disputed in synagogues, or wherever they could find an audience. Their disciples were allowed to propose to them questions.
They assumed the office without any appointment to it. The doctors of the law were principally of the sect of the Pharisees. Schools were established after the destruction of Jerusalem at Babylon and Tiberias, in which academical degrees were conferred on those who passed a certain examination. Those of the school of Tiberias were called by the title“rabbi,” and those of Babylon by that of “master.”


Single file µOS++ RTOS definitions.
Namespaces | |
| namespace | os |
| System namespace. | |
| namespace | os::rtos |
| RTOS namespace. | |
| namespace | os::rtos::interrupts |
| Interrupts namespace. | |
| namespace | os::rtos::scheduler |
| Scheduler namespace. | |
Functions | |
| thread::threads_list & | os::rtos::scheduler::children_threads (thread *th) |
| Get the children threads. | |
| template<typename T , typename ... Args> | |
| std::enable_if<!std::is_array< T >::value, std::shared_ptr< T > >::type | os::rtos::make_shared (Args &&... args) |
Create an object that is owned by a shared_ptr and is allocated using the RTOS system allocator. | |
| class thread::stack * | os::rtos::interrupts::stack (void) |
| Get the interrupts stack. | |
Single file µOS++ RTOS definitions.
The code was originally inspired by ARM CMSIS <cmsis_os.h>
file, v1.02, and tries to remain functionally close to the CMSIS specifications.
References are to C++ Standard ISO/IEC 14882:2011(E) Third edition (2011-09-01).
Major improvements:
Differences from POSIX:
Definition in file os.h.


ynicpn - ynicpn_aerr = uynicp*(@recode( (ynin - yniln - yniin - ynisen - tfibn - tsibn + gfsubn + gssubn) >(tfcin + tscin + .01*xgdpn), ynin - yniln - yniin - ynisen - tfibn - tsibn + gfsubn + gssubn, tfcin + tscin + .01*xgdpn))
ynicpn - ynicpn_aerr = @recode((ynin - yniln - ynirn + uynicpnr*xgdpn)>(tcin + .01*xgdpn), (ynin - yniln - ynirn + uynicpnr*xgdpn), (tcin + .01*xgdpn))
uynicpnr - uynicpnr_aerr = y_uynicpnr(1)*(1 - y_uynicpnr(2)) + y_uynicpnr(2)*uynicpnr(-1)
The equation was simplified so that the dynamics of corporate profits are primarly determined by the difference between total income and the sum of the household labor and rental&proprietors’ income. The wedge between measured corporate income and the aformentioned difference is assumed to have a stable relationship with nominal GDP (XGDPN); their ratio, YNICPNR, follows a simple autogressive process.
The following variables, which either did not have any feedback into or need in the model were eliminated:


Example of providing custom triggers for icons.
This example provide support for custom element attributes and behaviour (reverse animation playing after second turn).
| description | preview |
| click this icon twice |
|
| click this icon twice |
|


{% extends "base.html" %} {% load i18n %} {% block contentHeader %}
{% trans "We are discovering better ways of building communities by connecting people." %}
{% trans "Through this endeavor we have come to value:" %}
{% trans "Together, these elements build the foundation of a Small Act capable of transforming people's lives." %}


>>
<<
Usr
Pri
JfC
LJ
Phr
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Voc
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Dictionary
C. Adverbs and Conjunctions
Unlike verbs, adverbs and conjunctions have fixed valence:
an adverb is monadic (applying to a single argument to its left),
and a conjunction is dyadic.
Conjunctions and adverbs apply to noun or verb arguments;
a conjunction may produce as many as four distinct classes of results.
For example, u&v produces a composition of the
verbs u and v; and ^&2 produces
the square by bonding the power function with the right
argument 2; and 2&^ produces the
function 2-to-the-power. The conjunction & may
therefore be referred to by different names for the different cases,
or it may be referred to by the single term and (or with),
which roughly covers all cases.
>>
<<
Usr
Pri
JfC
LJ
Phr
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!:
Help
Dictionary


SJParks
Administrative Dashboard
<<<<<<< HEAD =======
logout
>>>>>>> Lgin Sessin
Municipal Rose Garden
Willow Street Frank Bramhall Park
Del Monte Park
Add New User
isAdministrator
Create User
Clear All
Add New Park
Create New Park


Rackspace is dedicated to open source and open community. We want to empower and support developers to rapidly innovate, build applications on OpenStack clouds and to make a difference in the community.
We're dedicated to making all developers' lives better, and we're passionate about supporting open source projects, communities, and developers.
So here's $300 in Rackspace Cloud credit for you to go forth and make something awesome.
» Sign up for the Developer DiscountFrom OpenStack, to a wide range of Open Source projects spread across the developer community; we encourage Rackers to contribute back to the engineering & developer community.
Follow our official open source projects on Github. All projects in the Rackspace organization are backed by Fanatical Support for Developers and dedicated staff.
Rackspace and NASA launched OpenStack together to help developers everywhere "unlock-in" the cloud. We have more OpenStack developers on our team than any other company - and all work is open source, open community and open design. Come be a part of it!
In addition to official, supported open source projects, Rackers dedicate time during work and personal time to a wide range of open source project and standards, this is why we have /rackerlabs and /racker on Github!
Our open source and fully supported developer tools help you get started using any OpenStack cloud in minutes.
Cloud Servers powered by OpenStack® are virtual machines running Linux or Windows Server in the Rackspace Cloud. You can scale resources like CPU, memory, and storage up or down, depending on your needs. Best of all, you can pay as you go.
Mission-critical web-based applications and workloads require high availability. Load balancing distributes workloads across two or more servers, network links, and other resources to maximize throughput, minimize response time and avoid overload.
Cloud DNS automates and simplifies Domain Name System management. Using the Rackspace Cloud Control Panel and REST-based API, you can list, add, modify, and remove domains, subdomains, and records, as well as import and export domains and records.
Cloud Monitoring is an API-driven cloud service built for infrastructure monitoring. It offers a simple yet powerful feature set, allowing extreme flexibility in configuration and execution.
Cloud Files, powered by OpenStack®, provides an easy to use online storage for files and media which can be delivered globally at blazing speeds over Akamai's content delivery network (CDN).
Cloud Networks allows you to create isolated, multi-tiered networks on our Cloud Servers powered by OpenStack®, all with the click of a button. It greatly simplifies networking in the cloud.
Rackspace's implementation of the OpenStack Identity Service. Cloud Identity service performs authentication, generating a token in response to valid credentials, and identification, matching users and their roles.
Cloud Backup is a file-based backup solution that allows customization of files, folders, schedule, retention and notification. Restore your backed up data to any Cloud Server running Cloud Backup.
Cloud Block Storage, powered by OpenStack®, delivers consistent performance for your I/O-intensive applications. Choose the service that works best for you - Standard or SSD volumes.
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Cloud sites provides reliable, scalable, simple and versatile platform for hosting your websites and blogs.
Mailgun is a set of powerful APIs that allow you to send, receive and track email effortlessly.
Built from the ground up, ObjectRocket is the premium MongoDB database-as-a-service (DBaaS). The only solution that is by default highly available, automatically sharded and unbelievably fast.
Airbrake monitors and collects errors generated by your web or mobile applications, and provides critical data for rapid debugging.
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('Disclosure: I own stake in a variety of the assets related to Supernet projects.Soft launch of the Supernet v0 wallet was announced November 14th by James on the Slack channels with a cheerful:\xe2\x80\x9cSupernet v0 launched yesterday, took everyone by surprise. gotta love...


Dump extensive information about the contents of the input Field to an HTML file, and write a simple summary to a string output (user interface parameter.)
Input Ports
Name Type Description in Mesh+ Node_Data input field for print_field to dump contents Parameters
The following lists all of the parameters found in the parameter block PrintFieldParams, which are accessed by the module PrintField and the UI Macro PrintFieldUI.
Name Type Description UI Control filename string output HTML filename PrintField File Name gridout int toggle grid information in the output file Grid Data cellout int toggle cell information in the output file Cell Data xformout int toggle xform information in the output file Xform Data nodesout int toggle node information in the output file Nodes Data htmlout int toggle HTML formatting in the output file Print HTML File allout int toggle points array information in the output file Print All Point trigger int trigger the start of printing Click to Start Printing message string status message about printing Printing Status notes string summary information about field Node/Cell Data Attributes
PrintField accepts any type of Field for input and dumps extensive selectable information about the Grid, Cell, Node, Xform and Point data to a plain text or HTML formatted file. Additionally a summary of the information is written to a string parameter, which is displayed by the UI macro.
in
Reference to a field containing Mesh+Node_Data.
filename
Contains the filename and path of the HTML file to write the contents of the field to.
gridout
Toggle to select if the Grid information in the field should be included in the HTML file dump.
cellout
Toggle to select if the Cell information in the field should be included in the HTML file dump.
xformout
Toggle to select if the Xform information in the field should be included in the HTML file dump.
nodesout
Toggle to select if the Nodes information in the field should be included in the HTML file dump.
allout
Toggle to select if all the Point information in the field should be included in the HTML file dump.
htmlout
Toggle to select if output contains HTML formatting. If turned off, the file only contains a purely textual dump of the same information.
trigger
Used to trigger the actual printing (dumping) of the information to the selected file.
message
Used to hold status of the printing.
notes
When HTML file is dumped, this multi-line string is filled with a simple summary of the field contents.
The low-level PrintField module is used in this User Macro print_field. Note that there is no Functional Macro. Additionally, this User Macro also uses the PrintFieldParams parameter block group object, as does the PrintFieldUI UI Macro.
An example application PrintFieldEg is provided, which uses the AVS/Express sample data file hydrogen.fld, to display or dump the contents of that field.
xp_comm_proj/printfld/pfldmacs.v contains the print_field User Macro, the PrintFieldUI UI Macro, and the example application PrintFieldEg V definitions.
The Print_FieldMacs library inherits its process. As this library contains no procedural code, the process is not important. The low-level PrintField module, executes under the process specified in its library, not the process defined in the high-level library.
Note that if "Print All Point" is selected then the output file may become very large!
The filename specified as the output HTML file may only be written to once; any attempt to do so will result in an error message in the error dialog window.
Phil Chen Connie Farb
Paul G. Lever
PrintFieldParams, PrintFieldUI and PrintField.
print_field in AVS/Express version 3.4.


In analyzing response time data with two choices, researchers would usually examine average response times (RTs) and response proportions. Depending on the model a researcher wishes to presume, the response proportions can simply be correct and error rates, or, if using SDT model, hits, correct rejections, false alarms and misses. Here I used Pleskac, Cesario, and Johnson’s (2017) data in the first-person shooter task (FPST; Correll et al., 2002) to illustrate one method to calculate average RTs and response proportions across participants.
Firstly, I use fread function to load the data file, which is in csv format. The data set provides clear and good column names. That is, the column names have informed the coding method. I simply just followed column names to code the factor levels and later checked against the data in the paper. Of course, I had also checked against the figures of behaviour analyses in the paper to make sure I did correctly identify the dependent and independent variables, which are listed in the following.
- S: stimulus factor, gun vs. non-gun objects.
- BC: blurry or clear object
- CT: context, a safe or dangerous neighborhood
- RACE: race, a black or white target
- R: response factor, shoot or not to shoot
- RT: response times
- s: subject / participant nominal labels
library(data.table);
study3 <- fread("data/race/Study3/original/Study3TrialData.csv")
study3$S <- factor(ifelse(study3$Object0NG1G == 0, "non", "gun"))
study3$BC <- factor(ifelse(study3$Blurry0Clear1Blur == 0, "clear", "blur"))
study3$CT <- factor(ifelse(study3$Context1Safe2Danger == 0, "safe", "danger"))
study3$RACE <- factor(ifelse(study3$Race012B == 0, "white", "black"))
study3$R <- factor(ifelse(study3$Resp0NS1Sh == 0, "not", "shoot"))
study3$RT <- study3$RT / 1e3
study3$s <- factor(study3$Subject)
factor is a R function converting variables, numeric or character, to categorical (i.e., nominal) variable. After reorganizing the columns, I removed the replicated columns by assigning them as NULL. This is a data.table specific syntax.
study3[, c("Subject", "NewSubject", "conditionRaceDangerBlurbject",
"conditionRaceDangerBlur", "Object0NG1G", "Blurry0Clear1Blur",
"Context1Safe2Danger", "Race012B", "Resp0NS1Sh", "DiffusionRT") := NULL]
There are NaN response times in this data set. One method is to replace them with random RTs drawn from uniform distribution, with the range of valid RTs. This was achieved by using the data.table internal function .I. I firstly found the (row) index of these NaN RTs, and then replaced them.  Here I illustrated one way to do it via Heathcote’s rc, a collection of very useful R functions and my summarise, also a collection of useful R functions. First, I used R’s source function to load this large collection of R functions.
source("~/rc/data.analysis.R")
source("~/rc/utils.R")
source("~/functions/summarise.R")
## Scoring ------------
se3 <- score.rc(data.frame(d), S = "s", R = "R", RT = "RT", SC = "C",
F = c("BC", "CT", "RACE", "S"))
## Spreading 11851 of 12033 RTs that are ties given preceision 0.001 .
## 497 have ties out of 679 unique values
##
## Added the following manifest design
## S RACE CT BC R rcell
## 1 gun black danger blur not 1
## 2 gun black danger blur shoot 1
## 3 gun black danger clear not 2
## ...
## 30 non white safe blur shoot 15
## 31 non white safe clear not 16
## 32 non white safe clear shoot 16
score.rc function takes first argument data.frame, which is the data as seen previously. Because I stored it as data.table, I needed to convert it back to data.frame. Just a note. Although data.table may accommodate many functions operating in data.frame, there are some operations in rc functions, which cannot work in data.table.
Note the second argument, uppercase S, which takes the subject column, instead of the column of stimulus factor. The R and RT arguments take response column and the response time column. SC takes the column of score correctness, which is purely my guess. I cannot be sure why it is called SC. The last useful argument is F, which takes user-defined factors, including the stimulus factor.
score.rc detects the identical (ties) RTs and spread them into finer scale. For example, in this data set, there are 31 trials with 60y ms.
table(d$RT)
## 0.01 0.015 0.019 0.025
## 1 1 1 1
## 0.027 0.03 0.035 0.036
## 1 1 1 1
## ...
## 0.386 0.387 0.388 0.389
## 7 3 1 3
## 0.39 0.391 0.392 0.393
## 1 4 3 3
## 0.394 0.395 0.396 0.397
## 2 6 7 3
## ...
## 0.606 0.607 0.608 0.609
## 50 31 40 39
## 0.61 0.611 0.612 0.613
## 42 49 31 41
## 0.614 0.615 0.616 0.617
## 49 55 42 39
## ...
 In this example, it has 32 cell, so cell is from 1 to 32 and rcell is from 1 to 16, because this is a two-choice experiment, response, shoot and not to shoot in cell 1 and cell 2, belong to the same experimental design, but with different response types.
## 1 gun black danger blur not 1
## 2 gun black danger blur shoot 1
## 3 gun black danger clear not 2
## 4 gun black danger clear shoot 2
A usual practice is to take 3 times the standard deviation, respectively in each participants. This can be achieved via tapply function. If the data set is large, one can use data.table to achieve the same aim, which I will demonstrate in a later tutorial.
sd3 <- tapply(se3$RT, se3$s, mean) + tapply(se3$RT, se3$s, sd) * 3;
A second useful function in rc collection is the make.rc, which does the censoring work. It takes a first argument of the scored data set, from score.rc and a second argument, correct.name, indicating the character string for the correctness column, and the last two arguments, for the lower and upper bounds of the censoring.
me3 <- make.rc(se3, correct.name = "C", minrt = .2, maxrt = sd3)
How to average across trials
- mv: measurement / dependent variable
- gvs: grouping variables
- wvs: within variables
acc0 <- summarySE(d, mv = "error", gvs = c("s", "BC", "CT", "RACE", "S"))
mrt0 <- summarySE(d[C == TRUE], mv = "RT", gvs = c("s", "BC", "CT", "RACE", "S"))
## Within se average across subjects for pc and nt
figA <- summarySEwithin(acc0, wvs = c("BC","CT", "RACE", "S"), mv = "error")
figB <- summarySEwithin(mrt0, wvs = c("BC","CT", "RACE", "S"), mv = "RT")
names(figA) <- c("BC", "CT", "RACE", "S", "N", "y", "sd", "se", "ci")
names(figB) <- c("BC", "CT", "RACE", "S", "N", "y", "sd", "se", "ci")
Reference
Ratcliff, R. (1993). Methods for dealing with reaction time outliers. Psychological bulletin, 114(3), 510.


C++FILT(1) GNU Development Tools C++FILT(1) NAME c++filt - Demangle C++ and Java symbols. SYNOPSIS c++filt [-_|--strip-underscores] [-n|--no-strip-underscores] [-p|--no-params] [-t|--types] [-i|--no-verbose] [-s format|--format=format] [--help] [--version] [symbol...] DESCRIPTION The C++ and Java languages provide function overloading, which means that you can write many functions with the same name, providing that each function takes parameters of different types. In order to be able to distinguish these similarly named functions C++ and Java encode them into a low-level assembler name which uniquely identifies each different version. This process is known as mangling. The c++filt [1] program does the inverse mapping: it decodes (demangles) low-level names into user- level names so that they can be read. Every alphanumeric word (consisting of letters, digits, underscores, dollars, or periods) seen in the input is a potential mangled name. If the name decodes into a C++ name, the C++ name replaces the low-level name in the output, otherwise the original word is output. In this way you can pass an entire assembler source file, containing mangled names, through c++filt and see the same source file containing demangled names. You can also use c++filt to decipher individual symbols by passing them on the command line: c++filt <symbol> If no symbol arguments are given, c++filt reads symbol names from the standard input instead. All the results are printed on the standard output. The difference between reading names from the command line versus reading names from the standard input is that command line arguments are expected to be just mangled names and no checking is performed to separate them from surrounding text. Thus for example: c++filt -n _Z1fv will work and demangle the name to "f()" whereas: c++filt -n _Z1fv, will not work. (Note the extra comma at the end of the mangled name which makes it invalid). This command however will work: echo _Z1fv, | c++filt -n and will display "f(),", i.e., the demangled name followed by a trailing comma. This behaviour is because when the names are read from the standard input it is expected that they might be part of an assembler source file where there might be extra, extraneous characters trailing after a mangled name. For example: .type _Z1fv, @function OPTIONS -_ --strip-underscores On some systems, both the C and C++ compilers put an underscore in front of every name. For example, the C name "foo" gets the low- level name "_foo". This option removes the initial underscore. Whether c++filt removes the underscore by default is target dependent. -n --no-strip-underscores Do not remove the initial underscore. -p --no-params When demangling the name of a function, do not display the types of the function's parameters. -t --types Attempt to demangle types as well as function names. This is disabled by default since mangled types are normally only used internally in the compiler, and they can be confused with non-mangled names. For example, a function called "a" treated as a mangled type name would be demangled to "signed char". -i --no-verbose Do not include implementation details (if any) in the demangled output. -s format --format=format c++filt can decode various methods of mangling, used by different compilers. The argument to this option selects which method it uses: "auto" Automatic selection based on executable (the default method) "gnu" the one used by the GNU C++ compiler (g++) "lucid" the one used by the Lucid compiler (lcc) "arm" the one specified by the C++ Annotated Reference Manual "hp" the one used by the HP compiler (aCC) "edg" the one used by the EDG compiler "gnu-v3" the one used by the GNU C++ compiler (g++) with the V3 ABI. "java" the one used by the GNU Java compiler (gcj) "gnat" the one used by the GNU Ada compiler (GNAT). --help Print a summary of the options to c++filt and exit. --version Print the version number of c++filt and exit. @file Read command-line options from file. The options read are inserted in place of the original @file option. If file does not exist, or cannot be read, then the option will be treated literally, and not removed. Options in file are separated by whitespace. A whitespace character may be included in an option by surrounding the entire option in either single or double quotes. Any character (including a backslash) may be included by prefixing the character to be included with a backslash. The file may itself contain additional @file options; any such options will be processed recursively. FOOTNOTES 1. MS-DOS does not allow "+" characters in file names, so on MS-DOS this program is named CXXFILT. SEE ALSO the Info entries for binutils. COPYRIGHT Copyright (c) 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009 Free Software Foundation, Inc. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.3 or any later version published by the Free Software Foundation; with no Invariant Sections, with no Front-Cover Texts, and with no Back-Cover Texts. A copy of the license is included in the section entitled "GNU Free Documentation License". binutils-2.20 2009-10-16 C++FILT(1)


Welcome to my website!
When I grow up, I would like to be a computer programmer or a photographer. Or an author.
I live in Indianapolis IN and I am in fifth grade.
I have two sisters and a brother.
One of my favorite games is Magic: The Gathering. It is the best trading card game of all time!
In the future, I hope to own the world's most popular website. I would be on the news and my afternoon would be booked with cofreneces all over the world!
Something that I am looking forward to is the new Infinity War movie.
My favorite sport is baseball, but my favorite sport to watch is golf.
My favorite band is Steam Powered Giraffe.
A movie I saw recently is the Black Panther movie. I still think the comics are a bit better. All the good superheroes are cooler in the comics.
In class, we are making our own utopias.
My favorite sport to watch is golf. A lot of people think it is the most boring sport ever, but I think it is actually very exciting. It is fun to see the really close shots that Justin Thomas just barely makes, or Patrick Reed making snarky commentsafter missing a super close putt.
Another book that like is called Ready Player One.
My favorite characters are Parzival, Aech, and Art3mis.
My favorite TV show and superhero is The Flash.
I usually have a very busy week, but I always look forward to our web design class on fridays.
Mr. Leszcynski is the best computer teacher ever!
I play many games, but my favorite has to be Steamworld Heist or Scribblenauts Unlimited.
I hope to teach my dad how to code.
In the future, I hope to develop a software that will overpower Microsoft!
I hope to make monthly polls on this website.
My favorite podcast is The Bright Sessions
I also enjoy solving puzzles, like the Rubik's Cubes
In the future, I hope to be able to host an online chat on my website so that people all around the world would be able to communicate with each other all at the same time!
Feedback? Email me!


This is a demo on how to upload something with bsvup. This demo also shows how to use external signer(third-party wallet, hardware wallet, or in-browser wallet).
Instruction:
1. Type in a PrivateKey(or generate one), and charge.
2. Edit Page as you like.
3. Click commit to build TXs and broadcast
Note: Key saved in local storage is not safe and not recommanded, only for demostration.
Key & Signer
Identity Private Key:
Log:


Congratulations on reaching the final project of the Robotics Nanodegree!
Previously, you worked on the Semantic Segmentation lab where you built a deep learning network that locates a particular human target within an image. For this project, you will utilize what you implemented and learned from that lab and extend it to train a deep learning model that will allow a simulated quadcopter to follow around the person that it detects!
Most of the code below is similar to the lab with some minor modifications. You can start with your existing solution, and modify and improve upon it to train the best possible model for this task.
You can click on any of the following to quickly jump to that part of this notebook:
We have provided you with a starting dataset for this project. Download instructions can be found in the README for this project's repo. Alternatively, you can collect additional data of your own to improve your model. Check out the "Collecting Data" section in the Project Lesson in the Classroom for more details!
import os
import glob
import sys
import tensorflow as tf
from scipy import misc
import numpy as np
from tensorflow.contrib.keras.python import keras
from tensorflow.contrib.keras.python.keras import layers, models
from tensorflow.contrib.keras.python.keras.utils import plot_model
import pydot
from tensorflow import image
from utils import scoring_utils
from utils.separable_conv2d import SeparableConv2DKeras, BilinearUpSampling2D
from utils import data_iterator
from utils import plotting_tools
from utils import model_tools
In the Classroom, we discussed the different layers that constitute a fully convolutional network (FCN). The following code will introduce you to the functions that you need to build your semantic segmentation model.
The Encoder for your FCN will essentially require separable convolution layers, due to their advantages as explained in the classroom. The 1x1 convolution layer in the FCN, however, is a regular convolution. Implementations for both are provided below for your use. Each includes batch normalization with the ReLU activation function applied to the layers.
def separable_conv2d_batchnorm(input_layer, filters, strides=1):
output_layer = SeparableConv2DKeras(filters=filters,kernel_size=3, strides=strides,
padding='same', activation='relu')(input_layer)
output_layer = layers.BatchNormalization()(output_layer)
return output_layer
def conv2d_batchnorm(input_layer, filters, kernel_size=3, strides=1):
output_layer = layers.Conv2D(filters=filters, kernel_size=kernel_size, strides=strides,
padding='same', activation='relu')(input_layer)
output_layer = layers.BatchNormalization()(output_layer)
return output_layer
The following helper function implements the bilinear upsampling layer. Upsampling by a factor of 2 is generally recommended, but you can try out different factors as well. Upsampling is used in the decoder block of the FCN.
def bilinear_upsample(input_layer):
output_layer = BilinearUpSampling2D((2,2))(input_layer)
return output_layer
In the following cells, you will build an FCN to train a model to detect and locate the hero target within an image. The steps are:
encoder_block
decoder_block
Create an encoder block that includes a separable convolution layer using the separable_conv2d_batchnorm()
function. The filters
parameter defines the size or depth of the output layer. For example, 32 or 64.
def encoder_block(input_layer, filters, strides):
# TODO Create a separable convolution layer using the separable_conv2d_batchnorm() function.
output_layer = separable_conv2d_batchnorm(input_layer, filters, strides)
return output_layer
The decoder block is comprised of three parts:
def decoder_block(small_ip_layer, large_ip_layer, filters):
# TODO Upsample the small input layer using the bilinear_upsample() function.
upsampled = bilinear_upsample(small_ip_layer)
# TODO Concatenate the upsampled and large input layers using layers.concatenate
concated = layers.concatenate([upsampled, large_ip_layer])
# TODO Add some number of separable convolution layers
output1 = separable_conv2d_batchnorm(concated, filters)
output_layer = separable_conv2d_batchnorm(output1, filters)
return output_layer
Now that you have the encoder and decoder blocks ready, go ahead and build your FCN architecture!
There are three steps:
def fcn_model(inputs, num_classes):
# TODO Add Encoder Blocks.
# Remember that with each encoder layer, the depth of your model (the number of filters) increases.
#ORIG enclayer1 = encoder_block(inputs, 32, 2)
#ORIG enclayer2 = encoder_block(enclayer1, 64, 2)
enclayer1 = encoder_block(inputs, 32, 2)
enclayer2 = encoder_block(enclayer1, 64, 2)
enclayer3 = encoder_block(enclayer2, 128, 2)
# TODO Add 1x1 Convolution layer using conv2d_batchnorm().
#ORIG onebyonelayer = conv2d_batchnorm(enclayer2, 128, 1, 1)
onebyonelayer = conv2d_batchnorm(enclayer3, 100, 1, 1)
# TODO: Add the same number of Decoder Blocks as the number of Encoder Blocks
#ORIG declayer1 = decoder_block(onebyonelayer, enclayer1, 64)
#ORIG x = decoder_block(declayer1, inputs, 32)
declayer1 = decoder_block(onebyonelayer, enclayer2, 128)
declayer2 = decoder_block(declayer1, enclayer1, 64)
x = decoder_block(declayer2, inputs, 32)
# The function returns the output layer of your model. "x" is the final layer obtained from the last decoder_block()
return layers.Conv2D(num_classes, 1, activation='softmax', padding='same')(x)
The following cells will use the FCN you created and define an ouput layer based on the size of the processed image and the number of classes recognized. You will define the hyperparameters to compile and train your model.
Please Note: For this project, the helper code in data_iterator.py
will resize the copter images to 160x160x3 to speed up training.
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
image_hw = 160
image_shape = (image_hw, image_hw, 3)
inputs = layers.Input(image_shape)
num_classes = 3
# Call fcn_model()
output_layer = fcn_model(inputs, num_classes)
Define and tune your hyperparameters.
learning_rate = 0.001
batch_size = 16
num_epochs = 30
steps_per_epoch = 200
validation_steps = 50
workers = 2
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
# Define the Keras model and compile it for training
model = models.Model(inputs=inputs, outputs=output_layer)
model.compile(optimizer=keras.optimizers.Adam(learning_rate), loss='categorical_crossentropy')
# Generate model diagram
plot_model(model, to_file='model.png')
# Data iterators for loading the training and validation data
train_iter = data_iterator.BatchIteratorSimple(batch_size=batch_size,
data_folder=os.path.join('..', 'data', 'train'),
image_shape=image_shape,
shift_aug=True)
val_iter = data_iterator.BatchIteratorSimple(batch_size=batch_size,
data_folder=os.path.join('..', 'data', 'validation'),
image_shape=image_shape)
logger_cb = plotting_tools.LoggerPlotter()
callbacks = [logger_cb]
model.fit_generator(train_iter,
steps_per_epoch = steps_per_epoch, # the number of batches per epoch,
epochs = num_epochs, # the number of epochs to train for,
validation_data = val_iter, # validation iterator
validation_steps = validation_steps, # the number of batches to validate on
callbacks=callbacks,
workers = workers)
# Save your trained model weights
weight_file_name = 'model_weights'
model_tools.save_network(model, weight_file_name)
Now that you have your model trained and saved, you can make predictions on your validation dataset. These predictions can be compared to the mask images, which are the ground truth labels, to evaluate how well your model is doing under different conditions.
There are three different predictions available from the helper code provided:
# If you need to load a model which you previously trained you can uncomment the codeline that calls the function below.
# weight_file_name = 'model_weights'
# restored_model = model_tools.load_network(weight_file_name)
The following cell will write predictions to files and return paths to the appropriate directories.
The run_num
parameter is used to define or group all the data for a particular model run. You can change it for different runs. For example, 'run_1', 'run_2' etc.
run_num = 'run_1'
val_with_targ, pred_with_targ = model_tools.write_predictions_grade_set(model,
run_num,'patrol_with_targ', 'sample_evaluation_data')
val_no_targ, pred_no_targ = model_tools.write_predictions_grade_set(model,
run_num,'patrol_non_targ', 'sample_evaluation_data')
val_following, pred_following = model_tools.write_predictions_grade_set(model,
run_num,'following_images', 'sample_evaluation_data')
Now lets look at your predictions, and compare them to the ground truth labels and original images. Run each of the following cells to visualize some sample images from the predictions in the validation set.
# images while following the target
im_files = plotting_tools.get_im_file_sample('sample_evaluation_data','following_images', run_num)
for i in range(3):
im_tuple = plotting_tools.load_images(im_files[i])
plotting_tools.show_images(im_tuple)
# images while at patrol without target
im_files = plotting_tools.get_im_file_sample('sample_evaluation_data','patrol_non_targ', run_num)
for i in range(3):
im_tuple = plotting_tools.load_images(im_files[i])
plotting_tools.show_images(im_tuple)
# images while at patrol with target
im_files = plotting_tools.get_im_file_sample('sample_evaluation_data','patrol_with_targ', run_num)
for i in range(3):
im_tuple = plotting_tools.load_images(im_files[i])
plotting_tools.show_images(im_tuple)
Evaluate your model! The following cells include several different scores to help you evaluate your model under the different conditions discussed during the Prediction step.
# Scores for while the quad is following behind the target.
true_pos1, false_pos1, false_neg1, iou1 = scoring_utils.score_run_iou(val_following, pred_following)
# Scores for images while the quad is on patrol and the target is not visable
true_pos2, false_pos2, false_neg2, iou2 = scoring_utils.score_run_iou(val_no_targ, pred_no_targ)
# This score measures how well the neural network can detect the target from far away
true_pos3, false_pos3, false_neg3, iou3 = scoring_utils.score_run_iou(val_with_targ, pred_with_targ)
# Sum all the true positives, etc from the three datasets to get a weight for the score
true_pos = true_pos1 + true_pos2 + true_pos3
false_pos = false_pos1 + false_pos2 + false_pos3
false_neg = false_neg1 + false_neg2 + false_neg3
weight = true_pos/(true_pos+false_neg+false_pos)
print(weight)
# The IoU for the dataset that never includes the hero is excluded from grading
final_IoU = (iou1 + iou3)/2
print(final_IoU)
# And the final grade score is
final_score = final_IoU * weight
print(final_score)


Use a Message Splitter pattern to split a large XML message into smaller elements for processing by one or more targets.
You want to share information which has accumulated within an application over a period of time, with other applications. Typical examples might be sales data that has accumulated over a day, or information updates that are received from partners. The data is accumulated over a given time period and transmitted as a single large message. The end of the day processing at the target application, or target applications, requires that each element is processed individually.
Alternatively, an application might generate a message containing a number of records that must be distributed; for example, where one application maintains and distributes reference data.
Large messages which contain repetitive and processing structures can cause excessive memory use, which has a potential impact on other message processes and can result in broker failure. This pattern addresses the problem of routing the elements of a large message to different targets according to defined routing rules.
Use this pattern for applications that store information about a number of business transactions and transmit this information to one or more target applications in batches. It can be used to handle large messages, without excessive memory use.
This pattern can be used when either the source application or target applications cannot make or process near real-time service calls, or when batching of information is required for business reasons.
The solution is to implement a message flow that uses a mutable message tree, so that repeatable elements can be deleted as they are processed. Partial parsing enables single elements to be parsed as required and, because these elements are deleted on completion, the in-memory tree is prevented from growing linearly with the number of elements. As each element is extracted it is routed to the appropriate target application.
The input message and all output messages are processed in a single transaction; all output messages are persistent. A failure to read the XML input message causes the entire transaction to fail, but errors processing a single element can either add the failed element to a queue for later processing, or cause the entire transaction to fail.


Import and ExportImport
Help importing from:
Warning:
Importing data may cause duplication with existing contacts in your address book. You may wish to consider deleting all contacts from your address book.
ExportChoose the program you want to export to and click the corresponding button:
Help exporting to:
Warning:
Contacts added from Yahoo! Yellow Pages will not be exported.
|


Enter in format: val1,val2,val3,etc
Enter in format: val1,val2,val3,etc
After pushing the following button the chart will be shown.
Show graph


Introduction
This module configures the current settings of the selected devices. The settings can be configured to be either the device's saved power-up defaults or factory defaults. This feature is useful if you want to restore device settings without power cycling a device.


This model approximates the behaviour of an inductor with the
influence of saturation, i.e., the value of the inductance depends
on the current flowing through the inductor. The inductance
decreases as current increases.
The parameters are:
- Inom...nominal current
- Lnom...nominal inductance at nominal current
- Lzer...inductance near current = 0; Lzer has to be greater than
Lnom
- Linf...inductance at large currents; Linf has to be less than
Lnom
- Main Author:
- Anton Haumer
Technical Consulting & Electrical Engineering
D-93049 Regensburg
Germany
email<EMAIL_ADDRESS>- Release Notes:
- May 27, 2004: Implemented by Anton Haumer
Generated at 2019-01-25T16:44:03Z by OpenModelica 1.14.0~dev-130-gefa44fb


Adventure Time RPG Game!
Pick your Character
Hot Dog Princess
Choose Goose
Ancient Psychic Tandem
War Elephant
James I
Pick Your Opponent
Fight Controls
Attack!
Enemy
You attacked
for
damage.
attacked you for
damage.
You have been defeated ... Game Over!!!
Press Restart to play again.
You have defeated
, you can choose to fight another enemy.
No enemy here. Please select an enemy to fight.
You Won!! Game Over!!
Press Restart to play again.
Please select a character first!
Restart


{% extends "html/basic_page.html" %} {% load staticfiles %} {% block title %}Home - SDUST ACMer{% endblock %} {% block body %}
Welcome to SDUST ACMer. This website is designed to help you to get and analyse your information from other OJ.
You can also search other ACMers' information and follow them.


Amid row over Gandhis’ security cover, govt to table SPG amendment bill in Lok Sabha next week
Parliament will take up the proposed amendment to the Special Protection Group (SPG) Act next week, Union Minister Arjun Ram Meghwal has said.
HIGHLIGHTS
- The SPG Act (Amendment) Bill listed in the government's business for next week
- As per proposed amendments, family members of former PM will not be guarded by SPG
- Presently, PM Modi is the only person being protected by the SPG commandos
Amid controversy over the withdrawal of SPG cover to the Gandhis, the Lok Sabha is likely to take up a proposed amendment to the Special Protection Group (SPG) Act next week.
Sources have told India Today that the amendment will focus on restricting SPG protection to only the Prime Minister and not the VVIPs.
Union Minister Arjun Ram Meghwal on Friday said the proposed amendment to the SPG Act will be taken in the ongoing Winter Session next week, news agency PTI reported.
While listing out the government's business for next week in Lok Sabha, Meghwal said besides other bills, the SPG (Amendment) Bill will also be introduced.
Presently, elite SPG commandos protect the prime minister and his/her immediate family, former prime ministers and their immediate family for a certain period depending on the threat perception.
SPG row reaches Parliament
Recently, the Centre withdrew the SPG protection cover granted to Congress president Sonia Gandhi and Congress leaders Rahul Gandhi and Priyanka Gandhi Vadra.
The issue rocked the first four days of the ongoing Winter Session as the Congress upped its ante against the Narendra Modi government and alleged political vendetta.
In the Parliament, Congress demanded the restoration of the SPG cover to the Gandhi family and former prime minister Manmohan Singh and asked the government to "rise above partisan politics".
Gandhis without SPG cover after 28 years
The Gandhi family is now without SPG protection after 28 years. They were included in the VVIP security list following an amendment in September 1991 in the SPG Act of 1988.
The SPG Act was initially supposed to provide security to only the prime minister and former prime ministers of the country
SPG protectees, under the rules, are provided with guards, hi-tech vehicles, jammers and an ambulance in their cavalcade. Prime Minister Narendra Modi is now the only person being protected by the elite SPG commandos.


Common Layouts
Pure was crafted with the goal that it can be used in every web project. To showcase this, we've made some common layouts that leverage Pure. These layouts are responsive and don't use any JavaScript.
Pure was crafted with the goal that it can be used in every web project. To showcase this, we've made some common layouts that leverage Pure. These layouts are responsive and don't use any JavaScript.
This site is built with <3 using Pure v0.1.0
All code on this site is licensed under the BSD License unless stated otherwise.
© 2013 Yahoo! Inc. All rights reserved.


Hara showcases an edgy new look + netizens support her and cheer her on
by sl278Hara has shown off a new look while filming.
The star uploaded a picture to her Instagram page on November 7 showing off messy styled hair along with dark eye makeup, giving her an artistic but sad look. Many netizens have been commenting on her new look and supporting her, stating:
"You were born to be loved."
"It's really nice to see her trying and living hard."
"This is the first time I've seen her in that type of look."
What do you think of Hara's new look?


--- layout: post status: publish published: true title: AWS advent has started! day 4 author: display_name: neil login: neil email<EMAIL_ADDRESS>url: '' author_login: neil author_email<EMAIL_ADDRESS>wordpress_id: 212 wordpress_url: https://blog.mitese.co.uk/?p=212 date: '2016-12-04 17:38:20 +0000' date_gmt: '2016-12-04 17:38:20 +0000' categories: - Uncategorised tags: [] comments: [] ---
Today and a new article, this time about lambda with Python.
https://www.awsadvent.com/2016/12/04/exploring-concurrency-in-python-aws/


Man accused of sex offences 28 years ago loses legal fight against extradition to Republic
A man accused of sex offences against his niece 28 years ago has lost a legal fight against extradition to the Republic.
Senior judges in Belfast rejected his appeal against a decision that he should be returned over the border to face trial.
The man, referred to as AB due to reporting restrictions, is being sought on three charges of sexual assault of a child.
The alleged offences were committed at an undisclosed location in the Republic in July 1991.
Gardaí launched an investigation in 1993 - around the same time AB moved to Northern Ireland.
Authorities initially decided not to prosecute him due to a lack of corroborating evidence.
But a European Arrest Warrant was issued in September 2017 after a new determination was reached in the case.
AB has been locked in a battle against extradition since he was detained two months later.
In May 2019 the Recorder for Belfast ruled that he should be transferred to stand trial.
Lawyers for the wanted man challenged that decision at the High Court, arguing that the request was an abuse of process.
They also contended that he should not be extradited due to delay in the case, and claimed it would breach his Article 8 rights to private and family life under European law.
But judges dismissed all three grounds of challenge.
Madam Justice McBride said: "We do not consider this was a borderline case where the culpable delay would have tipped the balance against extradition."
AB, who is currently on bail, now has 14 days to consider mounting a further appeal to the Supreme Court.
If no such legal move is made he us expected to be handed over to authorities across the border.


Sylvanian Families Skidoo
From the box:
Father Nigel Snow-Warren and daughter Lisa are wall wrapped up against the cold and are out having lots of fun on their skidoo.
From the box:
Father Nigel Snow-Warren and daughter Lisa are wall wrapped up against the cold and are out having lots of fun on their skidoo.


Title: Why no markdown support?
Date: 29/01/2020
Author: 54wedge
Abstract: So that your don't have to worry about different markdown syntax
Tag:
- Markdown
- test
Category: Markdown
If you have tried some blog builder, you probably noticed that the supported markdown syntax is different in different blog builder. What if you don't like the supported syntax or find your favorite syntax is missing? Changing a blog builder is not as easy as changing a new markdown editor. Plus, most of people write markdown in a markdown editor and html export is one of the most basic function in markdown editor. In short, you don't need to worry about if the specific syntax is supported by the blog builder what your markdown editor can do is what my blog builder can present.


Install¶
pip¶
pip install skccm
Conda (Recommended)¶
To create a conda environment, you can use the following conda environment.yml file:
name: skccm_env
dependencies:
- python=3
- numpy
- scikit-learn
- scipy
- pip:
- skccm
Then you can simply create the environment with:
conda env create -f environment.yml
And activate it with:
source activate skccm_env
Contribute, Report Issues, Support¶
To contribute, we suggest making a pull request.
To report issues, we suggest opening an issue.
For support, email cortalen@uncw.edu.


SEARCH
ABOUT US
24 hours.
Jae Yang. Dhensel Dorji. Will Schultheis. Jake Rossi.


About Particle Lab
Particle Physics Laboratory.
Front-end for GAMOS 5.1.0.
Version: 0.1.0
This application has been developed by Alejandro Fernández (Centro Guadalinfo de Santistebab del Puerto).
This software is licensed under MIT LICENSE


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The iVolunteers style guide is a user interface component library designed to promote visual consistency, unify visual design and aid in any future development of the iVolunteers system. This working document should get updated, should any new components get added or the existing ones modified. The look and feel of each iVolunteers instance may be customized through the admin tool to reflect the client brand identity: logo, color scheme of select elements and footer navigation.
Color Pallet
Default Color Scheme
Neutral greys, used for text and backgrounds, blue, reserved for links, and accent green, used throughout the application for navigation, task performance and content highlight purposes, are part of the iVolunteers template color scheme.
#e4e4e4
light-grey
#ccc
med-grey
#999
dark-grey
#337ab7
link
#178552
accent
Customizable Color
The generic link color and the accent colors, such as those used in for header and page level navigation, buttons, thumbnail tags and user stats, may be overridden by those selected by the client.
Links
This is an example of a default iVolunteers link. Default links are of the same weight as the parent element and are underlined on mouse over.
Roboto & Roboto Slab are Google fonts that come is varying weights. Roboto is the main content fornt while Roboto Slab is used titles mainly. For more details on the font-family, please refer to Google Fonts API.
Client logos may be in .gif or .png files (transparency supported)
Max-width: 250 px
Max-height: 60 px
Size: ~ 150 x 60 px
151 x 60 px
170 x 50 px
120 x 50 px
170 x 60 px
UI Components
Main Header (+/-Subheader)
Every iVolunteers screen has a main header, and there is a number of ways to display one in the application. Subheader is optional and is present on the landing Call Center Screen and on the Petition related screens.
Character counts on Petition and Call Center screens:
Main Header: 72 characters max
Subheader: 80 characters max
H1 Header (Petition)
Main header, h1.petitions-header and subheader p.petitions-header, are placed on top of the layered header background images on Petitions screens and its corresponding Thank You screen sequence.
Main Header: 72 characters max
Subheader: 80 characters max
H1 Header (Call Center)
Main header, h1.hero-h1 and subheader p.lead, are nested in a .row on a Survey Call Center landing screens.
Main Header: 72 characters max
Subheader: 80 characters max
Legend as Page Header (Sign Up & Log In Screens)
Sign Up to Volunteer and Log Into your Account screens contain forms; thus, legend legend is used for page header. When the form is centered on the screen, so legend text is centered as well.
User Stats Block (Account Management Screens)
User Name, location, number of calls attempted, completed and petitions signed are used as page title on User Account Management screens. The display presentation varies depending on the viewport size.
Standard Display
Display at or below 768px
Secondary Header
Secondary Headers (not to consuse with Subheaders) are used on User Account Management Screens to separate content grouping. These are represented by legend. The corresponding icons are prepended to the legend via JavaScript: the class name assigned to the legend control what preassigned icons will display.
Side Panel
Side Panel is displayed on Sign Petition page sequences and on a Sign Up screen. It displays to the right of the form and collapses to an expandable "Read More" tab above the form when the viewport is at or below 768 px. Its display is controlled by the Bootstrap Grid for the breakpoint and visability and JavaScrip for slide in/out behavior. Reload screen capture below.
Thumbnail Card
Thumbnail cards are used to highlight Petitions/Surveys on the homepage and on the Petitions screen as "suggested" action items for the user to act upon.
Thumbnail title character count is is 72 characters max (same length as petition/survey title)
List of titles, stats, and thumbnails is used to show a list of surveys and petitions under user Account Management. The image type used for thumbnail list is the same one used in thumbnail card, with 2 by 1 ratio, with a .img-responsive Bootstrap class that allows images to size up/down depending on the view port size. The thumbnail does not dispaly on veiwport smaller than 768px, contlolled by .hidden-xs class assign to it's corresponding div.
Standard Display
Display at or below 768px
User Stats
Numerical data, such as user statistics on the number of Petitions Signed and Calls Attempted is highlighted in the color of choice as selected by the client. The example uses the color green that corresponds to the green derived from the logo of HEX value: #178552; that we used throughout the mock-ups,
100 Calls Attempted
Pagination
Please defer to i360 Portal pagination specification for pagination controls.
Social Bottons
Log In with Facebook (see comps and copy/paste)
Facebook likes & Twitter shareing (??) TBD by Dev. Team
iVolunteers navigation is sticky Bootstrap base navigation that remains put on top of the screen on page scroll.
The color and the background color of the header navigation links, including those of various link states, will be selected and set by each client through the administrative interface of iVolunteers.
Standart Display
Display at or below 768px
Page Level Navigation
Page level navigation is used on the landing page and serves as a "filter" for Petitions and/or Surveys display.
In addition to being used on the homepage, Page level navigation is used to navigation through the user account management options.
Footer Navigation
The content of the footer navigation is client specific and is to be entered in .html format through the iVolunteers admin. interface.
Form Elements
Basic Form
The following controls are part of a basic form. The width of the input elements is controlled by the Bootstrap .col-sm-x div class. The code specifics are provided below.
Form Legend
The form legend can be either generic form legend (Sign This Petion screen) or a page title (Log In screen).
When a page does not a background banner with an H2 title placed in it, a form Legend serves as a page Header
Buttons
iVolunteers button classes follow Bootstrap hierarchy useing two the most commonly used classes: .btn-primary and .btn-defaulty. The predefined iVolunteer botton display (below) can be overriten in accordance to the client's specs. The width of the botton is determined by the .col-.. class that has been assigned to the <div> that is houseing the item. More more information, please refere to the Grid specifications.
Grid System
Grid Overview
iVolunteers is build based on Bootstrap's responsive, mobile first fluid grid system that scales up to 12 columns on viewport size increase. The grid consists of predefined classes that allow for various layout options.
Rows are used to create horizontal groups of columns and are placed within a fixed-width .container or full-width .container-fluid for proper alignment and padding.
Content should be placed into columns .col-..-.., and columns can the be the only immediate children of rows.
Predefined grid classes, such as .row and .col-xs-4 are used for making grid layouts.
The spacing between the columns, or gutters, is controlled through bootstrap.css pre-defined padding
If more than 12 columns are placed within a single row, each group of extra columns will, as one unit, wrap onto a new line.
Grid classes apply to devices with screen widths greater than or equal to the breakpoint sizes, and override grid classes targeted at smaller devices: applying any .col-md-* class to an element will not only affect its styling on medium devices but also on large devices if a .col-lg-* class is not present.
Grid Options
Extra small devices Phones (<768px)
Small devices Tablets (≥768px)
Medium devices Desktops (≥992px)
Large devices Desktops (≥1200px)
Grid behavior
Horizontal at all times
Collapsed to start, horizontal above breakpoints
Container width
None (auto)
750px
970px
1170px
Class prefix
.col-xs-
.col-sm-
.col-md-
.col-lg-
# of columns
12
Column width
Auto
~62px
~81px
~97px
Gutter width
30px (15px on each side)
Column Ordering
Order of built-in grid columns can be changed by adding .col-md-push-* and .col-md-pull-* modifier classes.
1st: .col-md-9 .col-md-push-3
2nd: .col-md-3 .col-md-pull-9
Element Visibility
Page elements (not limited to columns) can be hidden by adding .hidden-* modifier classes. Similarly, hidden columns can be made visible by adding .visible-* modifier class.


Edit this index.html file to insert a google maps api developer key in two places (the script tag and form input where you see API_KEY_HERE. Usage rates will apply when pulling images.
Enter an origin and one or more destinations below.
Upon clicking go, the images along the provided route will be appended to the DOM, then you'll get a zip file.
This page is intended as a sample usage page to get started with.


Programming Java, HTML/CSS, Go, Javascript, C++ Development Android, Full Stack Web, Active Batch Scheduling Cloud Computing AWS Machine Learning Tensorflow, octave/Matlab Embedded Systems Microprocessors, Microcontrollers


Class: HTTY::Response
Overview
Encapsulates an HTTP(S) response.
Constant Summary
- COOKIES_HEADER_NAME =
'Set-Cookie'
- LOCATION_HEADER_NAME =
'Location'
Instance Attribute Summary (collapse)
-
- (Object) status
readonly
Returns the HTTP status associated with the response.
Instance Method Summary (collapse)
-
- (Object) cookies
Returns an array of the cookies belonging to the response.
-
- (Response) initialize(attributes = {})
constructor
Initializes a new HTTY::Response with attribute values specified in the attributes hash.
Methods inherited from Payload
Constructor Details
- (Response) initialize(attributes = {})
Initializes a new HTTY::Response with attribute values specified in the attributes hash.
Valid attributes keys include:
- :body
- :headers
- :status
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# File 'lib/htty/response.rb', line 23 def initialize(attributes={}) super attributes @status = attributes[:status] end |
Instance Attribute Details
- (Object) status (readonly)
Returns the HTTP status associated with the response.
13 14 15 |
# File 'lib/htty/response.rb', line 13 def status @status end |
Instance Method Details
- (Object) cookies
Returns an array of the cookies belonging to the response.
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# File 'lib/htty/response.rb', line 29 def HTTY::CookiesUtil. @headers[COOKIES_HEADER_NAME] end |