PM’s nosebleed ends press conference prematurely
by BY YISWAREE PALANSAMYKUALA LUMPUR, Nov 19 — Prime Minister Tun Dr Mahathir Mohamad’s nose bled midway through a press conference here, causing it to end early.
While taking questions from journalists at the MPOB International Palm Oil Congress and Exhibition (Pipoc) 2019, Dr Mahathir was seen wiping his nose with a white handkerchief.
Red stains were spotted on this later.
Dr Mahathir’s escorts then interrupted the event to usher 94-year-old prime minister away.
Sources told Malay Mail that the prime minister was escorted away for medical reasons.


Reports custom tags that do not exist.
You can create custom tags for your application using simple template files stored in the app/views/tags
directory.
The template's file name is used as the tag name.
For example, to create a hello
tag, just create the app/views/tags/hello.html
file.
Reference the tag directly: #{hello /}
.
This inspection will report the #{hello /}
reference if app/views/tags/hello.html
does not exist.


Synchronising - Optimize your gen-set usage or parallel different sizes and
brands of generators
PLC - Utilise our unique, and easy to use PLC drag-and-drop blocks for
configuration.
New design - Flexibility to change the application e.g. SPtM and MINT.
Built in AVRi - You no longer need a separate AVRi interface in your
installation.
Plug-in modules - Customize your controller with our plug-in extension and
communication
modules.
G/LTE - Connect to your gen-set via 4G/LTE for alerts or use NinjaOx's
WebSupervisor.
GPS - Always know the location of your gen-set using geofencing for theft
or movement alerts.
InteliConfig - Our new configuration and monitoring PC tool (based on new
LiteEdit) will offer
you multi gen-set control and monitoring as well as easy set-up.
Remote Display available
Low Temperature version available (order code IG3200XXBLA)
InteliGen 500
Synchronising - Optimize your gen-set usage or parallel different sizes and
brands of
generators
PLC - Utilise our unique, and easy to use PLC drag-and-drop blocks for
configuration.
New design - Flexibility to change the application e.g. SPtM and MINT.
Built in AVRi - You no longer need a separate AVRi interface in your
installation.
Plug-in modules - Customize your controller with our plug-in extension and
communication modules.
4G/LTE - Connect to your gen-set via 4G/LTE for alerts or use NinjaOx's
WebSupervisor.
GPS - Always know the location of your gen-set using geofencing for theft
or movement alerts.
InteliConfig - Our new configuration and monitoring PC tool (based on new
LiteEdit) will offer you multi gen-set control and monitoring as well as easy set-up.
InteliDrive BaseBox
The InteliDrive BaseBox is flexible controller mainly
for Gas compression market
Preconfigured functions for compression
applications
Provides control, monitoring and protection
gas/diesel engine and application side as well
Large built-in PLC to suit individual needs
Lock&hide with PLC functionality by 3 level password
UL Class1 Division 2 certification (Expected 2/2020)
Built-in Ethernet, RS485, 2x CAN communications
Availability of Air-Fuel Ratio for gas engines (ISAFC-PCM Dongle)
Predefined protections
16 BIN, 16 BOUT, 4 AIN, 2 AOUT on board (Speed governor configurable as LAI)
Load sharing functions
Virtual shared inputs / outputs via CAN
Cloud-based monitoring via NinjaOx WebSupervisor
InteliDrive FPC
Engine controller for land-based and marine Tier 4 applications
Control, monitoring and protection for both mechanical and electronic diesel/gas engines in
one unit
Automatic, manual or remote start/stop of the engine
Support of engines with Electronic Control Unit (ECU) - J1939 or Cummins Modbus
Analog oil pressure, water temperature, fuel level, battery voltage, engine speed (pick-up)
Smooth engine speed control by Up/Down controller buttons, binary inputs for predefined
speed options, Speed Up/Down binary inputs or one analog input
Selectable protections alarm/shutdown
3 levels of password protection
Ethernet, USB, GSM/GPRS, RS485 or RS232/Modem/Modbus communication
Automatic SMS on alarm
On-line control and monitoring over web pages (embedded web server)
2 languages (user changeable)
Real time clock and event history log
Push buttons for simple control, lamp test
Front panel sealed to IP65
EN, UL certifications
InteliDrive Lite
Engine controller for land-based and marine Tier4 applications
Control, monitoring and protection for both mechanical and electronic diesel/gas engines in
one unit
Automatic or manual start/stop of the engine
Programmable On/Off panel button
Support of engines with Electronic Control Unit (ECU) - J1939 or Cummins Modbus
Analog gauge (VDO, Datcon and others) outputs – operator friendly
Smooth engine speed control
7 configurable binary inputs
7 configurable binary outputs
9 configurable analog inputs (3 resistive, 4 voltage) + 2 not configurable voltage inputs
Selectable protections alarm/shutdown
3 level of password protection
USB, RS485/RS232/Modem/Modbus communication
Automatic SMS on alarm
On-line control and monitoring over web pages (embedded web server) via IB-Lite
Optional GSM/GPRS modem/wireless Internet via IL-NT GPRS
AirGate support
2 languages (user changeable)
Real time clock and event history log
PLC functions: PID loop, Comparators, Timers
Push buttons for simple control, lamp test
Front panel sealed to IP65
EN, UL certifications
InteliDrive Telecom
Engine controller for telecom tower applications
Control, monitoring and protection for both mechanical and electronic diesel/gas engines in
one unit
Automatic or manual start/stop of the engine
Programmable On/Off panel button
Support of engines with Electronic Control Unit (ECU) - J1939 or Cummins Modbus
Analog gauge (VDO, Datcon and others) outputs – operator friendly
Smooth engine speed control
7 configurable binary inputs
7 configurable binary outputs
7 configurable analog inputs (3 resistive, 4 voltage) + 2 not configurable voltage inputs
Selectable protections alarm/shutdown
3 level of password protection
USB, RS485/RS232/Modem/Modbus communication
Automatic SMS on alarm
On-line control and monitoring over web pages (embedded web server) via IB-Lite
Optional GSM/GPRS modem/wireless Internet via IL-NT GPRS
AirGate support
2 languages (user changeable)
Real time clock and event history log
PLC functions: PID loop, Comparators, Timers
Push buttons for simple control, lamp test
Front panel sealed to IP65
EN, UL certifications
InteliDrive WP
Engine controller for land-based and marine applications
Control, monitoring and protection for both mechanical and electronic diesel/gas engines in
one unit
Automatic or manual start/stop of the engine
Support of engines with Electronic Control Unit (ECU) - J1939
Analog gauge (VDO, Datcon and others) output – operator friendly
Smooth engine speed control
5 configurable binary inputs
5 configurable binary outputs (2 relays - up to 10A)
9 configurable analog inputs
1 configurable analog output 5 VDC
Selectable protections alarm/shutdown
3 level of password protection
USB port for programming
2 languages (user changeable)
Real time clock and event history log
Control functions: PID loop, Comparators, Timers
Dust and water protection IP 65 (front and rear panel)
Operating temperature: –20 to +70°C
EN certifications
InteliGen NTC
Gen-set controller for single or multiple generating sets operating in standby or parallel
modes
Support of engines with ECU (Electronic Control Unit)
Many communication options – easy remote supervising and servicing
To be used inde npendently or in conjunction with detachable / external colour displays
InteliVision 5, InteliVision 8, InteliVision 12Touch, or InteliVision 18Touch
Gen-set performance log for easy problem tracing
Automatic synchronizing and power control (via speed governor or ECU)
AMF function, Baseload, Import / Export, Peak shaving, Voltage and PF control (AVR)
Generator measurement, Mains measurement
Inputs and outputs configurable for various customer needs
Controller redundancy
Event-based history (up to 500 records) with customer-selectable list of stored values;
RTC;
statistic values
Integrated PLC programmable functions
Integrated fixed and configurable protections
Selectable measurement ranges for AC voltages and currents – 120 / 277 V, 0–1 / 0–5 A
Secondary isolated RS232 / RS485 interface
Support of external colour displays
USB 2.0 slave interface
With Solution of Application support - manage up to 992 Gen-sets in Extended Multiple
Gen-set Application (Bank Controller)
InteliVision 5 CAN
5,7” Colour TFT Display with resolution 320 × 240 pixels
Local and Remote display for single controller monitoring
Plug and Play operation (auto configuration based on controller application)
Direct connection to the controller (converters are not needed)
Simpler, faster and more comfortable control for the user
More information in less time
5,7” Colour TFT Display
Same cut out as standard NinjaOx controllers, e.g. InteliDrive DCU
Same language support as the master controller including graphic languages
Active buttons – fast access to important data
Galvanically separated CAN interface
Binary output for horn/buzzer control
Sealed to IP65
8-pins Harness connection on rear side
Drag & Drop customer screen configuration
Analog input to control display backlit intensity
InteliVision 8 Marine
Marine approved display unit for either InteliGen NT / InteliSys NT or InteliDrive
controllers
8" colour TFT display with resolution of 800 x 600 pixels
Controlled by active buttons
TRENDS monitoring screen as a standard feature
Developed from IS-Display
Windows CE operating system
Simpler, faster and more intuitive control
More information in less time
Same language support as InteliGen NT / InteliSys NT and InteliDrive controller
Screen configuration by customer - export to XML format with subsequent manual screen
modification and import back to the controller (as IS-Display)
Direct connection to the controller (converters are not needed)
The same dimensions as IS-Display / InteliSys NT (including cut out dimensions)
It is possible to mount InteliSys NT BaseBox to the rear side of InteliVision
Can be used as a replacement for (or an alternative to) IG-Display, IS-Display or I-RD-CAN
Plug and Play operation (auto configuration based on controller application)
Designed to be mounted in both monitoring and engine room
This display gives complete access to all control and monitoring functions when connected
to InteliGen NT / InteliSys NT / InteliDrive controllers
Universal solution - one type of display for both controller families - InteliGen NT /
InteliSys
NT and InteliDrive
Intended for connection to ONE controller (InteliGen NT / InteliSys NT or InteliDrive)
Connection to a controller via RS232/485 and CAN bus
Operating temperature from -20 to + 70°C
Front panel sealed to IP65
InteliVision 5.2
5" colour screen with resolution of 800 × 480 pixels
Plug & Play operation (auto configuration based on the controller application)
Easy screen customization using Screen Editor (part of InteliConfig)
5 configurable user buttons under the screen
Modern look and graphical user interface
Supported controllers : InteliGen 500
Fast and intuitive navigation
Multi-language support
Trends monitoring screen (up to 4 channels)
Communication with controller via Ethernet
Operating temperature: -30 °C to +70 °C (-40 °C to +70 °C if the device is powered on above
-30 °C).
Front face is sealed to IP65
1x Analog Input, 1x Binary Output
Easy-to-use mounting system
Same cut out as InteliGen 500
EMC, climatic and mechanical tests
CE and UL certification
InteliSys NTC Hybrid
InteliSys NTC Hybrid is a controller for applications that combine
gen-sets with a renewable
source of power. It continuously monitors data from all sources of energy including solar,
wind,
gen-sets and batteries.
The Dynamic Spinning Reserve function allows the controller to adjust the
output from the
gen-sets according to the output of the renewable source. So, if the power output from the
renewable source begins to drop, the controller automatically starts the gen-sets to ensure
no
drop in energy production from the system.
InteliSys NTC Hybrid communicates over CAN bus with NinjaOx gen-set
controllers which manage
outputs from gen-sets of different installed capacity and various manufacturers. Smart power
management automatically select the most efficient combination of gen-sets based on their
sizes
to maximize fuel savings.
InteliSys NTC Hybrid provides interface to photovoltaic inverters with
respect to the gen-sets
minimum loading level. If the gen-set output approaches the allowed minimum, the InteliSys
NTC
Hybrid regulates the inverter output to prevent the engine from running underloaded.
InteliVision 12T
12,1" colour, multi-touch screen, TFT display unit with resolution of 1280 x 800 pixels
Touch based Graphical User Interface, support for multi-touch gestures
Plug and Play operation (auto configuration based on controller application)


CHAPTER 18 [OLD]
ELECTION OFFENSES
REPEALED. L 1970, c 26, §3.
CHAPTER 18 [NEW]
DISTRICTING
REPEALED. L 1984, c 65, §1.


After 17 years teaching mathematics I founded Ardor Education. I now write code and do business stuff. While I no longer work in the classroom I believe that once a teacher always a teacher.
Here are some of the technologies I enjoy writing day-to-day:


--- layout: post title: Fun with 82 - Web service document cache categories: [] tags: cm-82 serviceapi dotnet-sdk - ".Net SDK" status: publish type: post published: false meta: {} ---
In this, the latest in my series on fun stuff in 82, I examine the new option for a larger web service document cache.
This new setting affects the document cache used by the ServiceAPI, WebClient and WebDrawer allowing them all to cache a larger number of documents locally. For the developers out there you can also take advantage of this simply by calling TrimApplication.SetAsWebService("c:\\SOME_PATH"); at the start of your web service.


Why use hashes?
They're hard and confusing
10/02/2015
When first learning learn to code and ruby specifically I found myself always asking. Why would I ever use a hash? Any program I tried to create would always fail when I used them. They were confusing and so hard to understand. But after learning more about ruby and more specifically about hashes, I think they are one of the most useful things you can use.
When you think about dictionaries and all the definitions it contains. You can think of it like a hash and what information those usually contain. In a dictionary(a hash) we look up a word(key in a hash) to find its definition(the value of that key). Hashes made a lot more sense to me when I was able to think of them in that sense. We can store all sorts of things in hashes. They also make more sense than arrays in situations where we want to store complex information. I could create a hash for myself and store inside of it, the name of my parents, my age, my location. In an array I would have to remember at what index I stored those answers and could not just look up the appropriate key(age, mom, dad, etc.)
From the above example you can see a situation where it would make more sense to use a hash than an array. If my information was just stored as an array, I would have to know which index my age was stored in to get 28.


Gluten.js is a responsive event binding library that lets you easily toggle bindings on an element within different breakpoints. On resize, Gluten will detach and attach events based on your defined breakpoints. Gluten is a standalone library and does not require any dependencies to work.
Gluten.js is hosted on github and is available under the MIT License.
Initializing
To start, initialize Gluten by calling Gluten.init() and pass it a config object. The config object should contain your breakpoint's name(key) and width(value). The breakpoint name can be set to any value you want. The breakpoint width should be a number and is measured in pixels. For example, Gluten could be initialized in the following manner:
Boolean - when true, will output debug info into to the console. Defaults to false
Gluten.settings.classPrefix
String - the classPrefix is a prefix prepended to the current breakpoint name. The breakpoint name with the prefix is added as a class to the body tag. Defaults to "screen-"
Binding Event Rules
Event bindings are set with the Gluten.rules() method. the rules method take an array with each rule as an object. The object needs to contain the following:
selector
String - The DOM element to be selected. required
event
String - The event that will be binded to the element. Events are namespaced and must be unique. The event must have the event type followed by the name (seperated by a period). required
live
String - Selects an element above the given selector. Optional, but needed if adding an event on a dynamic element (event bubbling).
sizes
Array - A listing of the breakpoints the event will fire on. Optional, when not present, the event will fire at any breakpoint.


This is an implementation for eWinery shopping carts for non-cms sites.
Version - 1.4
author - Nazario A. Ayala (niztech.com)
date - January 9, 2013
updated - March 5, 2013
updated - March 8, 2013 - Dale Geist
The eWinery Connector for Non-CMS Sites is intended for “hand coded,” none CMS websites. Its purpose is to display a buy button for each item being sold via eWinery and to generate a preview shopping cart.
This code has been rewritten not using the previous code by IQR. IQR’s code had some sever inefficiencies and performance issues. That is not to say the current implementation cannot be improved...
This was written in JavaScript, HTML, and CSS. Some functionality of this code is better suited for ASP, JSP, or PHP but since the eWinery Connector for Non-CMS sites needed to be platform agnostic, all code was written in JavaScript using jQuery as the primarily framework. Code and documentation exists as a github project https://github.com/jfedtc/ewinery-connector-for-non-cms-sites.
In order to use this code:

<link rel="stylesheet" href="/resources/ewinery/ewinery.css" type="text/css" media="screen"/>

(productid)
(productid)
Provides default styling for the cart.
This directory holds images used by the source. Some images at this point should be changed to HTML/CSS buttons.
Currently holds:
This is not used during production. This is a JSON valid file with dummy data. I kept it around because it is possible that an eWinery issue may arise where having known good datasource can aid in troubleshooting.
This is an overview of the most important variables used. Some of these need to be configured.
productid
productid
productid should be replaced with the ewinery product id when the page is built. The script will replace item 1) above with the buy button or appropriate message and 2) will be replaced with the price.


The current ZoomedElement.
The value calculated from Dimensions.scrollY when a ZoomedElement is first opened.
The Y value of a touch event when a ZoomedElement is first opened.
The Overlay.
Adds event listeners to the page to listen for element dismissal.
Closes _current.
An event listener that calls close if the event's key code was the ESCAPE_KEY_CODE.
The keyboard event.
Removes the event listeners that were listening for element dismissal.
An event listener that calls close if the difference between the _initialScrollY and Dimensions.scrollY is more than SCROLL_Y_DELTA.
An event listener that calls close and removes itself if the difference between _initialTouchY and the current touch Y position is more than TOUCH_Y_DELTA.
The touch movement event.
An event listener that records the event's _initialTouchY and then adds touchMoveListener.
The touch start event.
Zooms in on an element.
The click event that occurred when interacting with the element.
Executes a function when the DOM is fully loaded.
The function to execute.
Generated using TypeDoc
Entry point to the library.


Maruti Suzuki S-Presso vs Renault Kwid comparison video
by Renuka KirpalaniWhen the Renault Kwid was launched, it came in and shook up the budget hatch segment with its SUV-like attitude and feature-rich package. Can the Maruti Suzuki S-Presso outdo the Kwid? We do a quick comparison test to find out.
Also see:
Click here for Maruti Suzuki S-Presso prices, details, reviews, images and videos
Click here for Renault Kwid prices, details, reviews, images and videos
Maruti Suzuki S-Presso
Rs 4.11 lakh * On Road Price (New Delhi)


You walk over and start touching the panel. The lights in the building start getting brighter, then darker, then off, then back on, then off again.
Suddenly the library security guard is behind you. They ask what you are doing. You reply trying to pull up Netflix on this projector! Security lets you know that you cannot do that.
They then walk you to the front desk and highlights on a calendar the next time NE will be having a movie night.
They also kindly let you know you are banned until then.


BindGadgetEvent()
Syntax
BindGadgetEvent(#Gadget, @Callback() [, EventType])Description
Bind a gadget event to a callback. It's an additional way to handle events in PureBasic, which works without problem with the regulars WindowEvent() / WaitWindowEvent() commands. It also allows to have real-time event notifications as the callback can be invoked as soon as the event occurs (useful for ScrollBarGadget(), ScrollAreaGadget() etc.). A gadget event can be unbinded with UnbindGadgetEvent().
Parameters
#Gadget The gadget to bind the event to. @Callback() The callback procedure to call when the event occurs. It has to be declared like this: Procedure EventHandler() ; Code EndProcedureRegular functions like EventGadget(), EventWindow(), EventMenu(), EventType() and EventData() are available within the callback to get more information about the event.
Note: WindowEvent() and WaitWindowEvent() should never be called from inside the callback or the program can be locked or have wrong behavior.EventType (optional) The event type to bind the event to. For a full list of supported types, see EventType(). #PB_All can be used to bind the event to any type.
Return value
None.
Example
Procedure ButtonHandler() Debug "Button click event on gadget #" + EventGadget() EndProcedure OpenWindow(0, 100, 100, 200, 50, "Click test", #PB_Window_SystemMenu) ButtonGadget(0, 10, 10, 180, 30, "Click me") BindGadgetEvent(0, @ButtonHandler()) Repeat Event = WaitWindowEvent() Until Event = #PB_Event_CloseWindow
See Also
BindGadgetEvent(), BindMenuEvent(), UnbindEvent(), WindowEvent(), WaitWindowEvent()
Supported OS
All


Being a programmer, there is no shortage of things to learn. New languages, infrastructures, frameworks, and thought paradigms are emerging continuously. I strive to be constantly improving, and to understand more about the world every day. I'm self-taught, and I've held professional roles on each side of the web stack. My career focus has been to draw out the business value from well designed code, i.e. improving profitability using software as a tool.
Helped a small team build a platform for scaling live video training for management skills.
I served as a consultant or programmer on various software projects, with a specialty in modern, Javascript-heavy applications.
A platform that applies computing power to the task of improving gardening. The current version helps gardeners time their plantings better and set alerts for different garden tasks.
A service that helped people who were interested in giving to the most high-impact causes find the perfect cause. Started by a husband and wife team from Stanford Business School, it became one of the leading platforms for philanthropy, and was widely praised by media outlets like the New York Times. It unfortunately, was a concept that proved too ambitious, and we were unable to secure the funding we needed for the next level of growth.
Hotel and casino software for both guests and employees. Our customers were some of the largest hospitality brands in the world. Our product team used an Agile workflow, and a test driven methodology.
A platform that helps real estate investors find mortgages. I developed it while in university.


Steve Scalise: In the Soviet Union They Have a 'Behind Closed Doors Secret Impeachment Process' | Breitbart
by Trent BakerHouse Minority Whip Steve Scalise (R-LA) commented Friday on the impeachment inquiry and his remarks from the day before likening the House Democrats’ process to how it would work in the Soviet Union.
Scalise said the United States deserves better than a “one-sided” and “behind closed doors secret impeachment process,” adding the Democrats’ impeachment resolution is not fair because it will allow for House Intelligence Committee Chairman Rep. Adam Schiff (D-CA) to continue operating in secret.
“I equate it to Soviet-style justice because maybe in the Soviet Union they have a one-sided, closed doors, behind closed doors secret impeachment process,’ Scalise told “Fox and Friends.” “This is the United States of America. We deserve better than this. Our justice system works a lot different than this.”
Follow Trent Baker on Twitter @MagnifiTrent


Working with children and young people is an increasingly popular area for apprentices.
Learning support staff, such as apprentice teaching assistants, play a vital role in today’s classrooms by supporting pupils, parents and colleagues to make sure pupils get the most out of education. Apprenticeships can also involve particular specialisms in areas like music or working with pupils with special educational needs or challenging behaviour, helping them to overcome barriers to learning.


module_search_scaffolds module¶
Contains functions used in search_scaffolds.py.
-
module_search_scaffolds.
check_for_duplicate_ranges
(range_list)[source]¶ Return True if the given list of tuples contains duplicates.
-
module_search_scaffolds.
check_if_hsps_overlapping
(hsp_objs)[source]¶ Takes a list of HSP objects, and returns true if they overlap.
-
module_search_scaffolds.
check_if_two_hsp_ranges_overlap
(lists)[source]¶ Takes two lists which contain the start and stop positions of two HSPs, and returns True if they overlap.
-
module_search_scaffolds.
check_if_two_hsps_overlap
(hsp_obj1, hsp_obj2)[source]¶ Takes two HSP objects and returns True if their ranges overlap, and False if not.
-
module_search_scaffolds.
check_strands
(hsp_objs, fwd_strand)[source]¶ Return True if hsps in the list are not all on the same strand.
-
module_search_scaffolds.
clusters_overlap
(cluster1, cluster2)[source]¶ Take two lists of HSPs, and determine if they define overlapping ranges in the subject sequence.
-
module_search_scaffolds.
find_missed_hsp_ranges
(complete_ranges, final_proximate_ranges, leftover_proximate_ranges)[source]¶ Testable function to test for problems.
-
module_search_scaffolds.
get_best_proximate_hsps
(hsp_objs, top_hsp, max_gap)[source]¶ Return a list of HSP objects that are sufficiently proximate to the top HSP, and among which there is no overlap in the sequence ranges. And, also return a list of HSP objects that are also proximate to these HSPs, but were not included in the final list of HSPs because they overlap with the ones that were. Instead of simply selecting HSPs based on hit range, consider E-values, favoring HSPs with better E-values if HSPs overlap and some have to be excluded.
-
module_search_scaffolds.
get_blastp_hit_seq_obj_and_coord
(hit, max_gap)[source]¶ Takes a SearchIO hit object (containing one or more HSPs) and returns a fasta sequence object likely to correspond to a single gene or the portion of the subject sequence that is actually homologous to the query sequence.
-
module_search_scaffolds.
get_cluster_range
(hsp_list)[source]¶ Take a list of SearchIO HSP objects an return a range from the furthest 5’ hsp end to the furthest 3’ hsp end.
-
module_search_scaffolds.
get_hit_seq_obj
(hit, max_gap)[source]¶ Takes a SearchIO hit object (containing one or more HSPs) and returns a fasta sequence object likely to correspond to a single gene (the most similar gene to the query in the nucleotide subject sequence).
-
module_search_scaffolds.
get_hit_seq_record_and_coord
(hit, proximate_hsp_objs)[source]¶ Given a list of SearchIO HSP objects (arguably representing a single gene), return a Bio.Seq record composed of concatenated HSP sequences.
-
module_search_scaffolds.
get_hsp_clusters
(hit, max_gap)[source]¶ Take a SearchIO Hit object and return a list of lists, where the sublists are HSPs that cluster together on the subject sequence such that it is likely that they represent a distinct gene.
-
module_search_scaffolds.
get_proximate_hsp_objs
(hsp_objs, top_hsp, max_gap)[source]¶ Return a list of HSP objects that are sufficiently proximate to the top HSP, and among which there is no overlap in the sequence ranges. And, also return a list of HSP objects that are also proximate to these HSPs, but were not included in the final list of HSPs because they overlap with the ones that were.
-
module_search_scaffolds.
get_proximate_hsp_objs2
(hsp_objs, top_hsp, max_gap)[source]¶ Return a list of HSP objects that are sufficiently proximate to the top HSP, and among which there is no overlap in the sequence ranges. And, also return a list of HSP objects that are also proximate to these HSPs, but were not included in the final list of HSPs because they overlap with the ones that were. Instead of simply selecting HSPs based on hit range, consider E-values, favoring HSPs with better E-values if HSPs overlap and some have to be excluded.
-
module_search_scaffolds.
get_proximate_hsp_ranges
(range_list, top_hsp_range, max_gap)[source]¶ Takes a list of ranges and returns another list, but corresponding to the top HSP and proximate HSPs that do not overlap.
Potential problem: Does not take into account the E-value of overlapping HPSs, and may exclude some HSPs with better E-values because of their position. It would perhaps be better to take E-values into account?
-
module_search_scaffolds.
get_strand_word
(hsp_hit_frame)[source]¶ Take a frame (for a translation of a DNA sequence) and return ‘plus’ or ‘minus’ depending on which strand the frame corresponds to.
-
module_search_scaffolds.
get_tblastn_hit_seq_obj_and_coord
(hit, max_gap)[source]¶ Takes a SearchIO hit object (containing one or more HSPs) and returns a fasta sequence object likely to correspond to a single gene (the most similar gene to the query in the nucleotide subject sequence).
-
module_search_scaffolds.
parse_tblastn
(tblastn_out_path, seq_out_path, max_gap)[source]¶ Parse a given tblastn output file (XML) and write appropriate sequences corresponding to hits therein to a given sequence file path. Also, return relevant info for recording results.
-
module_search_scaffolds.
print_cluster
(clusterplus, hit_num, cluster_num, num_dots, startend=None)[source]¶ Print visualization of cluster.
startend is a tuple containing the start and end position of the subsequence of the subject sequence to be visualized.
-
module_search_scaffolds.
recursively_add_clusters
(hsp_list, hsp_clusters, max_gap)[source]¶ take a list of SearchIO HSP objects and a list of HSP clusters (representing distinct genes), and add clusters recursively.
-
module_search_scaffolds.
reduce_to_best_hsps
(sorted_hsps, max_gap, recursion_num, name, leftright)[source]¶ Recursively select HSPs upstream of the top HSP for inclusion in the cluster.
Note: This function may be inefficient.
-
module_search_scaffolds.
remove_hsps_with_redun_ranges
(range_list, hsp_objs)[source]¶ Remove HSPs with redundant ranges.
-
module_search_scaffolds.
search_scaffolds
(query_file, subject_db, max_gap)[source]¶ Uses functions to search a given nucleotide db with a given protein query.
-
module_search_scaffolds.
split_tblastn_hits_into_separate_genes
(query_res_obj, max_gap)[source]¶ Take a SearchIO QueryResult object and return a new object with hits split into groups of HSPs that represent distinct genes. This is important, because there may be multiple paralogous genes present in a single nucleotide subject sequence (such as a chromosome or scaffold).


Constant kernel:
Gabor kernel:
Linear kernel with ARD:
where is a diagnal matrix consist of length scale for each dimension .
Linear kernel:
Matern kernel:
with for , for and for .
where r is the distance and is a diagnal matrix consist of length scale for each dimension .
Independent noise kernel:
Periodic kernel:
Piecewise polynomial kernel:
with
where D is the dimension of input and is a diagnal matrix consist of length scale for each dimension and f is a function depending on v. See gpml matlab v3.4.
Polynomial kernel:
Squared exponential kernel:
Squared exponential kernel with ARD:
where is a diagnal matrix consist of length scale for each dimension .
Squared exponential kernel with unit magnitude:
Rational quadratic kernel:
Rational quadratic kernel with ARD:
where is a diagnal matrix consist of length scale for each dimension .
Constant kernel. hyp = [ log_sigma ]
| Parameters: | log_sigma – signal deviation. |
|---|
Covariance function to be used together with the FITC approximation. The function allows for more than one output argument and does not respect the interface of a proper covariance function. Instead of outputing the full covariance, it returns cross-covariances between the inputs x, z and the inducing inputs xu as needed by infFITC
Gabor covariance function with length scale ell and period p. The covariance function is parameterized as:
k(x,z) = h( ||x-z|| ) with h(t) = exp(-t^2/(2*ell^2))*cos(2*pi*t/p).
The hyperparameters are:
hyp = [log(ell), log(p)]
Note that SM covariance implements a weighted sum of Gabor covariance functions, but using an alternative (spectral) parameterization.
| Parameters: |
|
|---|
This is a base class of Kernel functions there is no computation in this class, it just defines rules about a kernel class should have each covariance function will inherit it and implement its own behaviour
Check validity of inputs for the method getCovMatrix()
| Parameters: |
|
|---|
Check validity of inputs for the method getDerMatrix()
| Parameters: |
|
|---|
Covariance function to be used together with the FITC approximation. Setting FITC gp model will implicitly call this method.
| Returns: | an instance of FITCOfKernel |
|---|
Return the specific covariance matrix according to input mode
| Parameters: |
|
|---|---|
| Returns: | the corresponding covariance matrix |
Compute derivatives wrt. hyperparameters according to input mode
| Parameters: |
|
|---|---|
| Returns: | the corresponding derivative matrix |
Linear covariance function with Automatic Relevance Detemination. hyp = log_ell_list
| Parameters: |
|
|---|
Linear kernel. hyp = [ log_sigma ].
| Parameters: | log_sigma – signal deviation. |
|---|
Matern covariance function with nu = d/2 and isotropic distance measure. For d=1 the function is also known as the exponential covariance function or the Ornstein-Uhlenbeck covariance in 1d. d will be rounded to 1, 3, 5 or 7 hyp = [ log_ell, log_sigma]
| Parameters: |
|
|---|
Independent covariance function, i.e “white noise”, with specified variance. Normally NOT used anymore since noise is now added in liklihood. hyp = [ log_sigma ]
| Parameters: | log_sigma – signal deviation. |
|---|
Stationary kernel for a smooth periodic function. hyp = [ log_ell, log_p, log_sigma]
| Parameters: |
|
|---|
Piecewise polynomial kernel with compact support. hyp = [log_ell, log_sigma]
| Parameters: |
|
|---|
Polynomial covariance function. hyp = [ log_c, log_sigma ]
| Parameters: |
|
|---|
Precomputed kernel matrix. No hyperparameters and thus nothing will be optimised.
| Parameters: |
|
|---|
Squared Exponential kernel with isotropic distance measure. hyp = [log_ell, log_sigma]
| Parameters: |
|
|---|
Squared Exponential kernel with Automatic Relevance Determination. hyp = log_ell_list + [log_sigma]
| Parameters: |
|
|---|
Squared Exponential kernel with isotropic distance measure with unit magnitude. i.e signal variance is always 1. hyp = [ log_ell ]
| Parameters: | log_ell – characteristic length scale. |
|---|
Rational Quadratic covariance function with isotropic distance measure. hyp = [ log_ell, log_sigma, log_alpha ]
| Parameters: |
|
|---|
Rational Quadratic covariance function with Automatic Relevance Detemination (ARD) distance measure. hyp = log_ell_list + [ log_sigma, log_alpha ]
| Parameters: |
|
|---|
Gaussian Spectral Mixture covariance function. The covariance function is parameterized as:
k(x^p,x^q) = w’*prod( exp(-2*pi^2*d^2*v)*cos(2*pi*d*m), 2), d = |x^p,x^q|
where m(DxQ), v(DxQ) are the means and variances of the spectral mixture components and w are the mixture weights. The hyperparameters are:
hyp = [ log(w), log(m(:)), log(sqrt(v(:))) ]
Copyright (c) by Andrew Gordon Wilson and Hannes Nickisch, 2013-10-09.
For more details, see 1) Gaussian Process Kernels for Pattern Discovery and Extrapolation, ICML, 2013, by Andrew Gordon Wilson and Ryan Prescott Adams. 2) GPatt: Fast Multidimensional Pattern Extrapolation with Gaussian Processes, arXiv 1310.5288, 2013, by Andrew Gordon Wilson, Elad Gilboa, Arye Nehorai and John P. Cunningham, and http://mlg.eng.cam.ac.uk/andrew/pattern
| Parameters: |
|
|---|
Constant mean function. hyp = [c]
| Parameters: | c – constant value for mean |
|---|
Linear mean function. self.hyp = alpha_list
| Parameters: | D – dimension of training data. Set if you want default alpha, which is 0.5 for each dimension. |
|---|---|
| Alpha_list: | scalar alpha for each dimension |
Error function or cumulative Gaussian likelihood function for binary classification or probit regression.
Exact inference for a GP with Gaussian likelihood. Compute a parametrization of the posterior, the negative log marginal likelihood and its derivatives w.r.t. the hyperparameters.
Expectation Propagation approximation to the posterior Gaussian Process.
FITC approximation to the posterior Gaussian process. The function is equivalent to infExact with the covariance function: Kt = Q + G; G = diag(g); g = diag(K-Q); Q = Ku’ * inv(Quu) * Ku; where Ku and Kuu are covariances w.r.t. to inducing inputs xu, snu2 = sn2/1e6 is the noise of the inducing inputs and Quu = Kuu + snu2*eye(nu).
FITC-EP approximation to the posterior Gaussian process. The function is equivalent to infEP with the covariance function: Kt = Q + G; G = diag(g); g = diag(K-Q); Q = Ku’ * inv(Kuu + snu2 * eye(nu)) * Ku; where Ku and Kuu are covariances w.r.t. to inducing inputs xu and snu2 = sn2/1e6 is the noise of the inducing inputs. We fixed the standard deviation of the inducing inputs snu to be a one per mil of the measurement noise’s standard deviation sn. In case of a likelihood without noise parameter sn2, we simply use snu2 = 1e-6. For details, see The Generalized FITC Approximation, Andrew Naish-Guzman and Sean Holden, NIPS, 2007.
FITC-Laplace approximation to the posterior Gaussian process. The function is equivalent to Laplace with the covariance function: Kt = Q + G; G = diag(g); g = diag(K-Q); Q = Ku’ * inv(Kuu + snu2 * eye(nu)) * Ku; where Ku and Kuu are covariances w.r.t. to inducing inputs xu and snu2 = sn2/1e6 is the noise of the inducing inputs. We fixed the standard deviation of the inducing inputs snu to be a one per mil of the measurement noise’s standard deviation sn. In case of a likelihood without noise parameter sn2, we simply use snu2 = 1e-6.


{% include head.html %}
{% include header.html %}Home infusion therapy works best when patients are well equipped to manage their care effectively in the absence of health practitioners. Infusion Connect puts patients in a much better position to troubleshoot problems with their infusion, keep track of infusion pump information and stay on top of their therapy all together.
Patients are able to reach their assigned nurse via text, phone call or live video chat, as long as the nurse is online.


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PrimeIcons use the pi pi-{icon} syntax such as pi pi-check. A standalone icon can be displayed using an element such as i or span

Size of the icons can easily be changed using font-size property.

Special pi-spin class applies infinite rotate to an icon.

Here is the current list of PrimeIcons, more icons will be added periodically. You may also request new icons at the issue tracker.


Contributing to Passpie¶
Whether reporting bugs, discussing improvements and new ideas or writing extensions: Contributions to Passpie are welcome! Here’s how to get started:
- Check for open issues or open a fresh issue to start a discussion around a feature idea or a bug
- Fork the repository clone your fork and start making your changes
- Write a test which shows that the bug was fixed or that the feature works as expected
- Send a pull request and bug the maintainer until it gets merged and published ☺
Documentation¶
Passpie documentation is always open to improvements, to help passpie with better documentation. Passpie documentation is hosted on https://readthedocs.org and documentation files are written in RestructuredText for Sphinx
Coding¶
git clone https://github.com/marcwebbie/passpie.git
cd passpie
virtualenv .venv && source .venv/bin/activate
make develop
make
Importers¶
Importers should be independent pluggable code. To add a new importer. Create python module into importers package. If you were writting an importer to a site called superpassword
.
touch passpie/importers/superpassword.py
This importer should inherit from passpie.importers.BaseImporter
and override two methods:
match
: This method should check file passed as filepath and see if it is parseable and can be read by importerhandle
: Return parsed credentials from file. The return must be a list of credentials with plain text passwords
from passpie.importers import BaseImporter
class DefaultImporter(BaseImporter):
def match(self, filepath):
return True
def handle(self, filepath):
credentials = []
return credentials


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There is no transformation from the dimensions of the input image to something that can be processed by the cross entropy function, yielding an invalid model.
The weights were initialized randomly, and have not been adjusted or trained to classify this image in particular, so I you wouldn't expect great performance from this network in particular. until it's been trained. You'd expect it to be right maybe one in ten times, since there are ten classes.
I observe about 90% or higher accuracy with an outlier here or there. The system performs 1550 inferences per second, and trains 1080 examples per second. With Fashion MNI
I see accuracies anywhere from 5%-90%, a very broad and often wrong range.
The weights for the new layer are probably initialized randomly. Since the sequence of layers is linear, nothing new happens. It's as if we were just multiplying by another random matrix. Also presumably the weights during backpropagation become very small effectively making the points disappear, which is why we get Nan percentages.
The network performs okay at first with a wide range of accuracies from 27% - 80% ish. With 100 neurons I notice a much faster increase in training accuracy and much better performance with a smaller amount of training examples.
It seems that one layer did the trick, since this was a fairly simple dataset and the weights were able to be trained quickly and effectively. You can definitely see some overfitting in the larger layers, since their performance for the same amount of training wasn't so great on other examples.
The first layer being wider seemed to be more effective on average. I'd assume this has something to do with the backpropagation process and a drawback of the nonlinear activation function we're using
With both datasets I got much worse results across the board. These images are much more complex, and it seems that this network structure is definitely not enough for it.
I tested out various batch sizes (20, 50, 100) on a certain number of batches (50), and then the same batch sizes on a larger number of batches (150). Interestingly, I found that of course increasing the number of batches greatly reduced the loss (1.9ish -> 1.3ish), but I also got better results on average with a smaller batch size (20); my initial suspicion is that it might be overfitting.
These results in particular were intersting to me because it seemed clear (relative of other data samples) what the numbers were but, for some reason it seemed that the model really struggled to identify 8.
Ultimately, after playing around with both spectra of values, I found that what got me the highest training accuracy was increasing the number of batches (1000) and keeping that batch size smmall, around 20-40 images .
I tested a model with a first layer of size 20, a leaky activation layer, and a second layer of size 10, with which I was just able to get about 90% accuracy. That's still only 9/10 right, which isn't great. So I tried a three layer scheme of 20->15->10 for not much improvement, as it turns out.


ID: {{locations.id}}
Start: {{locations.sdate}} {{locations.stime}} UTC{{locations.sutc}}
Finish: {{locations.edate}} {{locations.etime}} UTC{{locations.eutc}}
Period: {{locations.period}}
Total Traffic: {{locations.total_traffic}}
Return Rate: {{locations.return_rate}}
Average Dwell Time: {{locations.avg_dwell_time}}
Average Return Interval: {{locations.avg_return_interval}}
Avg hours mode: {{locations.avg_hours_mode}}
Avg days mode: {{locations.avg_days_mode}}
Percent qualified visits: {{locations.percent_qualified_visits}}
Percent engaged visits: {{locations.percent_engaged_visits}}
Percent visits potential: {{locations.percent_visits_potential}}


This webpage is provided 'as is' without warranty of any kind, either expressed or implied and such software is to be used at your own risk.
This has only been tested on Windows 10, using Google Chrome 88.0.4324.190. View the 'Console' in 'Developer Tools' to catch and troubleshoot any errors.
Prepare XRP Transaction For Claiming Sparks on the Flare Network
NOTE: For security reasons, this transaction will expire approximately 30 minutes after being prepared. Expired transactions will receive the 'Ledger sequence too high' error upon submission.
Thanks to Wietse Wind for explaining the requirements. See here: https://coil.com/p/wietse/Prepare-for-claiming-your-Spark-token-Flare-Networks-a-tool-for-XUMM-XRPToolkit/NkXJQUqpi


The Treasure Chest is a fictional store in Utah that aims to create and recreate the magic and treasure of childhood through new and vintage toys, games, and magic tricks. For over 30 years the store has sold its highly-curated inventory and they are looking to bring their store online. Based on the project brief and stakeholder interviews with the business leaders, I set out to meet these goals:
Increase engagement with current customers. Increase customer acquisition locally and globally. Translate the in-store experience into a digital experience.My research focused on understanding the landscape for online toy stores, the mental models of people shopping for toys, and the design patterns for "social proof" and "product discovery" experiences. I dug deep into the goals of Jason who shops for his daughter:
User Research The client provided 3 well-researched personas: Jason (the single father), Jenny (the grandmother), and Daniel (the collector). I narrowed in on Jason as the primary persona.
Competitive and Comparative Analysis Jason's goals guided an analysis of other online toy stores, places where people discover unique products, and places that provide strong social proof for their products.
User Card Sorting I used a list of the store’s top 100 items to conduct an open and closed card sort to better understand mental models for organizing toys (beyond gender and age categories.)
These research insights evolved into 4 key experiences in Jason's shopping journey: Find a toy. Jason finds a toy through easy search or curated collections. Make sure it is "cool." Jason feels confident his daughter will like the toy because the reviews show another child with her similar interests liked it. Buy and give the toy. Purchase to delivery to gift-giving is transparent and worry-free. Spend time together playing (and re-engage with the website). Jason can revisit the website to get additional ideas for how to play with the toy with his daughter.
Storyboard, Scenarios, and User Flows I developed storyboards, scenarios, and flows of Jason's shopping journey from finding a toy to spending time with his daughter playing.
Site Mapping and Navigation The site map and navigation was informed by an understanding of the user flows and the insights from card-sorting research.
Layout and UI I sketched many possibe interfaces and conducted user walkthroughs to iterate and narrow in on the key experiences.
The Treasure Chest online experience helps customers find the right toy for their child through detailed reviews and curated collections, and helps them create memories through getting the most out of their toy with ideas for play.
Next steps:
Find a toy and make sure it is cool. By shopping at Treasure Chest, Jason can be guided to the right toy for his daughter and, after accessing reviews, can feel confident about his choice.
Buy and give the toy.
Spend time together playing. With access to many ideas for how to play, he can spend time with his daughter creating memories and growing their relationship. And the increased engagement with Treasure Chest online ensures the social shopping experience that is so important to Jason by building a relationship and bringing users back to the website to review their purchases.
Icons from the Noun Project.


Definition of an individual author
| Field | Type | Label | Description |
| last_name | string | Last name of the author |
|
| first_name | string | First name of the author |
|
| initials | string | Any initials of the author |
|
| rank | int64 | Ranking of the author |
Defines fields for manipulating output of Publication collection
| Field | Type | Label | Description |
| cursor | int64 | A unique pointer to the next set of result in the list |
|
| limit | int64 | Maximum number of records that can be fetch per request |
|
| filter | string | The `filter` field restricts the data return by the collection. To use it, supply one or multiple allowed fields to filter followed by a filter expression. It uses the following syntax... field_name operator expression The following fields of `PublicationAttributes` definition are allowed to be used for filtering * journal - Journal where the publication was published (string) * year - Year publication was published (string) * pub_date - Date of publication (string) * pub_type - Type of publication (string) * source - Source of the publication (string) * issue - Issue of the publication (string) * status - Status of the publication (string) * author - Authors of the publication (string) field_name - Any one of the allowed field_name of the `PublicationAttributes` definition. operator - Defines the type of filter match to use. It could be any of the following four and all of them should be URL-encoded for http request. Operators for strings =~ Contains substring !~ Not contains substring === Equals !== Not equals Operators for number == Equals > Greater than < Less than =< Less than equal to >= Greater than equal to expression - The value that will be included or excluded from the result. URL-reserved characters must be URL-encoded for http request. filter: "pub_type===journal_article" filter: "source===pubmed" Filter can be combined using OR or AND boolean logic. * The OR is represented using a comma(,). * The AND is represented using a semi-colon(;). * AND and OR operators can be combined and AND takes precedence over OR. filter: "year==2008;journal===Genesis" Can also accept multiple authors. filter: "author===Vandelay;author===VanNostrand" The sort field allow to sort the data return by the collection based on fields of `PublicationAttributes. To use it, supply a comma separated one or more allowed field from the definition of `PublicationAttributes`. |
Metadata definition for traversing the collection
| Field | Type | Label | Description |
| next_cursor | int64 | A unique pointer to the next set of result in the collection. Set the cursor value parameter to the value of next_cursor to retrieve the next set of collection using the same method |
|
| limit | int64 | Maximum number of records that can be fetch per request |
|
| total | int64 | Total number of records in the collection. |
Definition for creating a new publication
| Field | Type | Label | Description |
| data | NewPublication.Data |
|
| Field | Validations |
| data |
|
| Field | Type | Label | Description |
| type | string | Resource name, by default should be publication |
|
| attributes | PublicationAttributes |
|
Definition of an individual publication
| Field | Type | Label | Description |
| data | Publication.Data |
|
| Field | Validations |
| data |
|
| Field | Type | Label | Description |
| type | string | Resource name, by default should be publication |
|
| id | string | Unique identifier for the publication |
|
| attributes | PublicationAttributes |
|
Definition of various publication attributes
| Field | Type | Label | Description |
| doi | string | Digital object identifier for publication |
|
| title | string | Title of publication |
|
| abstract | string | Abstract of publication |
|
| journal | string | Journal where the publication was published |
|
| pub_date | google.protobuf.Timestamp | Date publication was published |
|
| volume | string | Volume of the publication |
|
| pages | string | Pages containing the publication |
|
| issn | string | International Standard Serial Number of publication |
|
| pub_type | string | Type of publication (i.e. "journal_article") |
|
| source | string | Source of the publication (i.e. "pubmed") |
|
| issue | string | Issue of the publication |
|
| status | string | Status of the publication |
|
| authors | Author | repeated | List of authors of the publication |
List of publications
| Field | Type | Label | Description |
| data | PublicationCollection.Data | repeated |
|
| meta | Meta |
|
| Field | Validations |
| data |
|
| meta |
|
| Field | Type | Label | Description |
| type | string | Resource name, by default should be publication |
|
| id | string | Unique identifier for the publication |
|
| attributes | PublicationAttributes |
|
| Field | Type | Label | Description |
| id | string | Unique identifier for the publication |
| Field | Validations |
| id |
|
Definition for updating an existing publication
| Field | Type | Label | Description |
| data | PublicationUpdate.Data |
|
| Field | Validations |
| data |
|
| Field | Type | Label | Description |
| type | string | Resource name, by default should be publication |
|
| id | string | Unique identifier for the publication |
|
| attributes | PublicationUpdateAttributes |
|
| Field | Validations |
| id |
|
Defines attributes for updating an existing publication
| Field | Type | Label | Description |
| pub_date | google.protobuf.Timestamp | Date of publication |
|
| volume | string | Volume of the publication |
|
| pages | string | Pages containing the publication |
|
| pub_type | string | Type of publication (i.e. "journal_article") |
|
| source | string | Source of the publication (i.e. "pubmed") |
|
| status | string | Status of the publication |
|
| authors | Author | repeated | List of authors of the publication |
The publication service specification
| Method Name | Request Type | Response Type | Description |
| GetPublication | PublicationId | Publication | Retrieves publication by ID |
| CreatePublication | NewPublication | Publication | Create new publication |
| UpdatePublication | PublicationUpdate | Publication | Update an existing publication |
| DeletePublication | PublicationId | .google.protobuf.Empty | Delete an existing publication |
| ListPublications | ListPublicationParameters | PublicationCollection | List all publications |
| .proto Type | Notes | C++ Type | Java Type | Python Type |
| double | double | double | float | |
| float | float | float | float | |
| int32 | Uses variable-length encoding. Inefficient for encoding negative numbers – if your field is likely to have negative values, use sint32 instead. | int32 | int | int |
| int64 | Uses variable-length encoding. Inefficient for encoding negative numbers – if your field is likely to have negative values, use sint64 instead. | int64 | long | int/long |
| uint32 | Uses variable-length encoding. | uint32 | int | int/long |
| uint64 | Uses variable-length encoding. | uint64 | long | int/long |
| sint32 | Uses variable-length encoding. Signed int value. These more efficiently encode negative numbers than regular int32s. | int32 | int | int |
| sint64 | Uses variable-length encoding. Signed int value. These more efficiently encode negative numbers than regular int64s. | int64 | long | int/long |
| fixed32 | Always four bytes. More efficient than uint32 if values are often greater than 2^28. | uint32 | int | int |
| fixed64 | Always eight bytes. More efficient than uint64 if values are often greater than 2^56. | uint64 | long | int/long |
| sfixed32 | Always four bytes. | int32 | int | int |
| sfixed64 | Always eight bytes. | int64 | long | int/long |
| bool | bool | boolean | boolean | |
| string | A string must always contain UTF-8 encoded or 7-bit ASCII text. | string | String | str/unicode |
| bytes | May contain any arbitrary sequence of bytes. | string | ByteString | str |


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| 5 |
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loop-vectorize |
vectorized loop (vectorization width: 4, interleaved count: 2) |
TestH |
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res[i] = (p[i] == 0) ? res[i] : res[i] + d[i]; |
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loop-unroll |
completely unrolled loop with 16 iterations |
TestH |
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res[i] = (p[i] == 0) ? res[i] : res[i] + d[i]; |
| 11 |
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| 13 |
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inline |
foo can be inlined into TestH with cost=30 (threshold=412) |
TestH |
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inline |
foo inlined into TestH |
TestH |
| 14 |
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| 15 |
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inline |
bar will not be inlined into TestH because its definition is unavailable |
TestH |
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inline |
foo can be inlined into TestH with cost=30 (threshold=412) |
TestH |
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inline |
foo inlined into TestH |
TestH |
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inline |
foo can be inlined into TestH with cost=30 (threshold=412) |
TestH |
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inline |
foo inlined into TestH |
TestH |
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I have been practising a form of mindfulness meditation called Samatha (meaning calm) for almost 2 years now, and have found it to be a very beneficial thing to do.
To give you some background; I am a 24 year old male, living in England. I have suffered from depression on and off since I was 19, and fortunately I have had years at a time of being completely okay and quite happy. Unfortunately the depression has returned on two occasions since I was first ill, and i have sought various ways to deal with it.
I started smoking cannabis (mainly high THC skunk) when I was 20. I've found it to be a great way to relieve stress as it stops me worrying about anything. This is great if you are worrying about silly things that make you depressed, but the side effect of cannabis is that it makes me really lazy. I've also tried DXM (a few times), Mushrooms (only once) and Nutmeg (never again!)
Anyway, in the Autumn of 2001 I decided to try meditation. I had started reading about Buddhism in a book my housemate had lent me. It seemed to be really interesting, fitting in with what had until then been my personal philosophy, and explaining the causes of my suffering (depression). I joined a class to learn meditation at my local University and began to practise at home.
Meditation in a Buddhist setting is about learning how to observe your mind, and hence have more control over your thoughts and feelings, by recognising that they are not an integral and inseparable part of your self. Letting go of thoughts that hurt or upset me is THE WAY to be happy. Cannabis can help me to do this, but makes me really lazy.
Most meditation works by focusing on an object of concentration; the breath entering and leaving the body in this case. We generally meditate for 30 mins at a time. It is very hard to concentrate just on one thing, and when I first began to meditate I found my mind constantly bringing up other things for me to think about.
Slowly, over a period of months of practice, one can learn to let the distractions of the mind drift away, enabling one to concentrate more on what one is doing. I have made some progress, and now am able to meditate for about 10-20 seconds without any distractions. After a year or so of meditation, I now start to feel really happy when I meditate. I find it is easier to concentrate on things in my everyday life, as meditation seems to be practise to deal with distractions, worries and thoughts that are painful, or unwanted.
I have gained a lot of other things from meditation, such as greater feelings of happiness, kindness towards others, etc... but I feel that these things are also partly because I have been trying to be a good buddhist for the last 2 years.
Finally, I have a theory on the trippy and spiritual side of meditation and psychoactives: If my brain has different channels of thought (like sound mixing channels on a mixing deck) then drugs turn UP the volume on the weird ones (like dreams, sometimes useless, sometimes fun, sometimes insightful, sometimes scary). Meditation seems to turn the volume DOWN on every day normal thoughts, and worries (I need to tidy up my room, get some lunch, go out and meet some girls, pay the bills, learn to drive). This allows me to hear the drug-like thoughts more easily. In this way, the two paths have a similar effect, and could possibly be combined.
I think meditation is a guided, mapped way of exploring my consciousness. Many others have been this way before, and will be there to help in hard times, or if I go off track.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


About Me
Hello! I’m Khadijah Sanusi. I am passionate about Web Design and UI/UX design. I am a skilled Front-end Developer and master of tools such as Photoshop and Adobe Illustrator.
Age
25
Email
<EMAIL_ADDRESS>Phone
0810-586-5118
Address
9, Iwo Road, Ibadan, Nigeria
Professional Skills
HTML
CSS
JavaScript
Adobe Photoshop
Sketch
Adobe XD
Work Experience
Frontend Developer Intershipat ZURI TEAM
August, 2021 - Present
Leverage agile frameworks to provide a robust synopsis for high level overviews. Iterative approaches to corporate strategy foster collaborative thinking to further the overall value proposition.
UI/UX Designer Internshipat KODECAMP
August, 2021 - Present
Override the digital divide with additional clickthroughs from DevOps. Nanotechnology immersion along the information highway will close the loop on focusing solely on the bottom line.
Support/Technical Officer at PROWEB e-solutions
March, 2021 - Present
Offering support and training to ProwebPos Users. Ensuring all users of the software have a smooth experience with the software by handling all clients complaints.
Education
Computer Science from Federal Polytechnic
2018 - 2020
Override the digital divide with additional clickthroughs from DevOps. Nanotechnology immersion along the information highway will close the loop on focusing solely on the bottom line.
Science Class from Al-Hikmah High School
1999 - 2012
Basic Education helps a lot especially as things get tough and as problem becomes bigger, with a Sound Background, it becomes solvable
Contact
Address
9, Iwo Road, Ibadan, Nigeria
Phone
0810-586-5118
<EMAIL_ADDRESS>https://twitter.com/babygirlajoke
https://www.facebook.com/hormowumy.damilola
https://www.instagram.com/i_am_khadiijah/
https://github.com/Rubee-k1


{% if include.person %} {% if include.person.education %}
{{school.school}} {{school.study}} {{school.start}} - {{school.end}}


Potassium is one of the three basic elements that are necessary for plant growth along with nitrogen and phosphorus.


Ex_rep, found in the examples/c/ex_rep
subdirectory of the Berkeley DB distribution, is a simple but
complete demonstration of a replicated application. The
application is a mock stock ticker. The master accepts a stock
symbol and a numerical value as input, and stores this
information into a replicated database; either master or
clients can display the contents of the database, given an
empty input line.
There are two versions of the application: ex_rep_mgr uses Replication Manager, while ex_rep_base uses the replication Base API. This is intended to demonstrate that, while the basic function of the application is the same in either case, the replication support infrastructure differs markedly.
The communication infrastructure demonstrated with ex_rep_base has the same dependencies on system networking and threading support as does the Replication Manager (see the Replication introduction). The Makefile created by the standard UNIX configuration will build the ex_rep examples on most platforms. Enter "make ex_rep_mgr" and/or "make ex_rep_base" to build them.
The synopsis for both programs is as follows:
ex_rep_xxx -h home
-l host:port [-MC] [-r
host:port] [-R
host:port] [-a
all|quorum] [-b]
[-n
sites] [-p
priority] [-v]
where "ex_rep_xxx" is either "ex_rep_mgr" or "ex_rep_base". The only difference is that:
specifying -M or -C is optional for ex_rep_mgr, but one of these options must be specified for ex_rep_base.
The -n option is not supported supported by ex_rep_mgr. That option specifies the number of nodes in the replication group. When you use the Replication Manager, this number is automatically determined for you.
The options apply to either version of the program except where noted. They are as follows:
A typical ex_rep_mgr session begins with a command such as the following, to start a master:
ex_rep_mgr -M -p 100 -h DIR1 -l localhost:30100
and several clients:
ex_rep_mgr -C -p 50 -h DIR2 -l localhost:30101 -r localhost:30100 ex_rep_mgr -C -p 10 -h DIR3 -l localhost:30102 -r localhost:30100 ex_rep_mgr -C -p 0 -h DIR4 -l localhost:30103 -r localhost:30100
In this example, the client with home directory DIR4 can never become a master (its priority is 0). Both of the other clients can become masters, but the one with home directory DIR2 is preferred. Priorities are assigned by the application and should reflect the desirability of having particular clients take over as master in the case that the master fails.


Definition of ECU-TEST package output parameter consisting of a unique name.
Defined package output parameters are written to related environment variables after test execution has finished.
The environment variable has the following syntax: TT_TEST_RETVAL_<VARIABLE NAME>_<INDEX>


If, for some reasons, your prefer to use Gson rather than Jackson as a way of converting Java Objects into JSON and back, this new plugin is for you.
The Spincast Gson plugin is a complete replacement for the default Spincast Jackson Json plugin that is provided in the default Spincast artifact.
Some custom serializers and deserializers are provided. Also, the GsonBuilder
is easy to access so you can add your own configurations to help build the final Gson
instance.


Name
Likes
Dislikes
Like/Dislike
{{ technology.name }}
{{ technology.likes }}
{{ technology.dislikes }}


|
OIS
1.5
Object-oriented Input System
|
#include <OISMouse.h>
Public Member Functions | |
| MouseState () | |
| bool | buttonDown (MouseButtonID button) const |
| Button down test. More... | |
| void | clear () |
| Clear all the values. More... | |
Public Attributes | |
| int | width |
| int | height |
| Axis | X |
| X Axis component. More... | |
| Axis | Y |
| Y Axis Component. More... | |
| Axis | Z |
| Z Axis Component. More... | |
| int | buttons |
| represents all buttons - bit position indicates button down More... | |
Represents the state of the mouse All members are valid for both buffered and non buffered mode
|
inline |
|
inline |
Button down test.
|
inline |
Clear all the values.
| int OIS::MouseState::buttons |
represents all buttons - bit position indicates button down
|
mutable |
|
mutable |
Represents the height/width of your display area.. used if mouse clipping or mouse grabbed in case of X11 - defaults to 50.. Make sure to set this and change when your size changes..


2018
-
October 22-14, 2018: Outreachy Linux Kernel Internship report (Moderator) at OSS, Europe (Upcoming)
-
September 26-28, 2018: From Knowing the definition of Linux Kernel to becoming a kernel hacker at Kernel Recipes (Upcoming)
-
August 30, 2018: So You want to be a Kernel hacker at GLS University in Ahmedabad (Slides)
-
August 4-5, 2018: Kernel BoF at Devconf.in (Slides, Blogpost)
-
March 17, 2018: Building a career in open source at Avishkar Techfest, NIT Durgapur (Slides)
2017
-
December 9, 2017: Introduction to Coccinelle(hands on) at Kernel Meetup, Bangalore (Slides)
-
November 4-5, 2017: Panelist at Women in open source panel and moderator for BoF on Rails Girls Summer of Code at PyCon India, 2017
-
October 23-26, 2017: Why Should We Care About Kernelnewbies? at Open Source Summit Europe, 2017 (Slides)
-
October 23-26, 2017: Bringing more women into Tech - One Summer At A Time (RGSoC) at Open Source Summit Europe, 2017 (Slides)
-
August 19-20, 2017: What’s up in the land of Linux kernel security? at FrOSCon, 2017(Slides, video)
-
July 8, 2017: Linux kernel workshop at Christ University, Bangalore (Slides)
-
June 9-10, 2017: Building a career in open source at Hong Kong Open source conference, 2017(Slides)
-
March 17-19, 2017: Coccinelle: Finding bugs in the Linux kernel at FOSSASIA Summit, 2017 (Slides, video)
-
February 25, 2017: Introduction to Linux Kernel hacking[talk] at FAD UVCE, 2017 (Slides)
-
January 21, 2017: Linux Kernel workshop at CodeUtsava - NIT, Raipur (Slides)
2016
-
December 3, 2016: Introduction to Linux Kernel Hacking (Hands-on session) at Bangalore Kernel Hackathon, 2016 (Slides)
-
October 4-6, 2016: Using static checkers to find c language security vulnerabilities in the Linux Kernel at LinuxCon Europe, 2016 (Slides)
-
July 13-15, 2016: Introduction to Coccinelle and SmPL (Tutorial) at LinuxCon Japan, 2016 (Slides)
-
April, 2016: Getting started with contributing to open source at NIE University, Mysore (Slides)
-
February 26-28, 2016: Contributing to Linux kernel (workshop) at FossMeet, 2016 (Slides)
-
January 16, 2016: Contributing to Linux kernel at Bangalore kernel meetup (Slides)


2.2. RV: Random Variables¶
The RV tab allows the user to specify the probabilistic distribution for the random problem at hand. The following probabilistic distributions for the random variables are currently supported:
2.2.1. Probability distribution¶
Note
To add a new random variable the user presses the Add button. To remove a random variable, the user must first select it by checking the small circle before the random variable, and then pressing the Remove button.
<IP_ADDRESS>. Dakota Engine¶
The following six distribution classes are supported for Dakota engine.
-
User provides the mean (\(\mu\)) and standard deviation (\(\sigma\)) of the normal distribution. The density function of the normal distribution, as a function of \(\mu\) and \(\sigma\) is:
()¶\[f(x) = \frac{1}{\sqrt{2 \pi} \sigma} \exp \left( -{\frac{1}{2} \left( \frac{x - \mu}{\sigma} \right)^2} \right)\] -
User provides the mean (\(\mu\)) and standard deviation (\(\sigma\)) of the lognormal distribution. The density function of the lognormal distribution, as a function of \(\mu\) and \(\sigma\) is:
()¶\[f(x) = \frac{1}{\sqrt{2 \pi} \zeta x} \exp \left( -{\frac{1}{2} \left( \frac{\ln x - \lambda}{\zeta} \right)^2} \right)\]
where \(\zeta^2 = \ln \left( \frac{\sigma^2}{\mu^2} + 1 \right)\) and \(\lambda = \ln(\mu) - \frac{\zeta^2}{2}\)
-
User provides \(\alpha\), \(\beta\), lower bound (\(L_B\)), and an upper bound (\(U_B\)) for the beta distribution. The density function of the normal distribution, as a function of these quantities is:
()¶\[f(x) = \frac{\Gamma(\alpha + \beta)}{\Gamma(\alpha)\Gamma(\beta)} \frac{(x - L_B)^{\alpha-1}(U_B-x)^{\beta-1}}{(U_B - L_B)^{\alpha + \beta - 1}}\]where \(\Gamma(\alpha)\) is the Gamma function.
-
User provides the lower bound (\(L_B\)), and an upper bound (\(U_B\)) for the uniform distribution. The density function of the normal distribution, as a function of these quantities is:
()¶\[f(x) = \frac{1.0}{(U_B - L_B)}\]The mean of the distribution is \(\frac{(U_B + L_B)}{2.0}\)
-
User provides shape parameter (\(k\)) and scale parameter (\(\lambda\)) for the Weibull distribution. The density function of the Weibull distribution, as a function of these quantities is:
()¶\[f(x) = \frac{k}{\lambda}\left(\frac{x}{\lambda}\right)^{k-1} \exp \left( -(x/\lambda)^{k} \right)\]where \(k,\lambda > 0\) and \(x \geq 0\). For \(x<0\), \(f(x) = 0\).
-
User provides \(\alpha\) and \(\beta\) for the Gumbel distribution, where \(\beta\) is known as the location parameter and \(\frac{1}{\alpha}\) the scale parameter. The density function of the Gumbel distribution, as a function of these quantities is:
()¶\[f(x) = \alpha e^{-\alpha(x-\beta)} \exp(-e^{-\alpha(x-\beta)})\]
For each random variable, the user must enter a name and select from the pull down menu the distribution associated with the random variable. For the distribution selected, the user must then provide the input arguments, which are as described above. figRV
shows the panel for a problem with four Random Variables with all random input following Gaussian distributions.
Note
To add a new random variable the user presses the Add button. To remove a random variable, the user must first select it by checking the small circle before the random variable, and then pressing the Remove button.
Warning
Removing a random variable may have unintended consequences and cause the UQ engine to fail.
<IP_ADDRESS>. SimCeterUQ Engine¶
Five additional distributions are supported in the SimCenter UQ engine. The users can define distributions either by Parameters, Moments or Dataset. (Note: Nataf transform module developed by [ERA19] is adopted)
Exponential
User provides the parameter (\(\lambda\)) of the exponential distribution. The density function of the exponential distribution, as a function of \(\lambda\) is:
()¶\[f(x) = \lambda \exp(-\lambda x)\]where \(x>0\) and \(\lambda>0\). User can alternatively provide the mean (\(m\)) of the exponential distribution.
()¶\[m = \frac{1}{\lambda}\]Discrete
User provides the \(N\) discrete values (\(x_i\)) and their weights (probability \(p_i\)) for a multinomial distribution. The probability mass function of the discrete distribution is:
()¶\[\begin{split}p(x)=\begin{cases} p_i, & \text{if $x=x_i$}\\ 0, & \text{otherwise} \end{cases}\end{split}\]where \(p_i>0\). The weights (\(p_i\)) will be automatically normalized if they do not sum up to one. The option to define by moments is not supported for the discrete distribution.
Gamma
User provides the shape parameter (\(k\)) and scale parameter (\(\lambda\)) of the Gamma distribution. The density function of the Gamma distribution, as a function of \(k\) and \(\lambda\) is:
()¶\[f(x) = \frac{\lambda^kx^{k-1}\exp(-\lambda x)}{\Gamma(k)}\]where \(\lambda>0\) and \(k>0\). User can alternatively provide the mean (\(m\)) and standard deviation (\(\sigma\)) .
()¶\[\begin{split}m &= \frac{k}{\lambda} \\ \sigma &= \sqrt{\frac{k}{\lambda^2}}\end{split}\]Chi-squared
User provides the parameter \(k\) of the Chi-squared distribution. The density function of the Chi-squared distribution, as a function of \(k\) is
()¶\[f(x) = \frac{1}{2^{\frac{k}{2}}\Gamma\left(\frac{k}{2}\right)}x^{\left(\frac{k}{2}-1\right)} \exp\left(-\frac{x}{2}\right)\]where \(x>0\) and \(k\) is a natural number. User can alternatively select the moment option where the mean (\(m\)) is
()¶\[m = k\]Truncated exponential
User provides the parameter \(k\) and bounds \(L_B\) and \(U_B\) for the truncated exponential distribution. The density function of the truncated exponential distribution, as a function is
()¶\[f(x) = \frac{\lambda}{c} \exp(-\lambda x), \text{ where $L_B<x<U_B$}\]where \(c\) is a normalization constant, i.e.
()¶\[c = \int_{L_B}^{U_B} \lambda\exp(-\lambda x) dx\]where \(x>0\) and \(\lambda>0\). User can alternatively provide the mean of the distribution along with the truncated bounds.
User can also define the random variables by providing sample realization data set as shown in the below figure, by selecting
Dataset
input type. The data will be fitted to the specified probability distribution model. Note that for some of the bounded distributions, such as beta and truncated exponential, the bounds should additionally be provided.
Note
Clicking the
Show PDF
orShow PMF
button will display probability distribution (or mass) function of each random variable with the specified parameters/moments. If the PDF or PMF is not displayed, we recommend the users to double-check the parameters/moments if they are in a valid range. The plotting button is not activated for theDataset
input type.
2.2.2. Correlation matrix¶
Correlation coefficients between each variable can be defined by clicking the Correlation Matrix
button. Default correlations between variables are set to be zero. The diagonal element of the matrix is fixed as one, and symmetricity of the correlation matrix is enforced once the entries of the lower triangular part of the matrix are modified.
Once the OK
button is clicked after setting all required entries, the program will automatically check the validity of the matrix before closing the correlation matrix window. If the matrix is not positively defined, an error message will be displayed and the window will not be closed. In such case, user should adjust the correlation coefficients to be positive definite.
Note
When
constant
variable is introduced instead of probability distributions, the correlation coefficient corresponding to those variables will be ignored.When more than one random variable is provided as
Dataset
, correlations between the data pairs will not be incorporated automatically. If correlations exist, user can define them manually at the correlation matrix window.
- ERA19
Engineering Risk Analysis Group, Technische Universität München: https://www.bgu.tum.de/era/software/eradist/ (Matlab/python programs and documentations)


Mask allows you to display keyboard shortcuts. Mask recognizes some keyboard names and will replace them with their appropriate characters.


MQTT in
Slide 1
Slide 2
Terminal
## Page title A paragraph with some text and a [link](http://hakim.se).


About Typesafe
Our products (free, open source):
Scala, Akka (Actors on the JVM), Play (Web framework),
sbt (Build tool), Scala IDE, Slick (RDMBS persistence)
For these, we offer
- Developer support
- Production support
- Consulting
- Training
About me
- Java since ~1997
- Over time, had a variety of job titles, including consultant, architect, it project manager, it manager, scrum master.
- After dealing mostly with the development process for some years, my “discovery” of Scala (ca. 2011) revived interest in actual coding
- Developing with Scala since 2012 (primarily REST back-ends for mobile applications)
- Since April 2014 trainer and consultant for Typesafe
Front-end / JavaScript experiences
- Backoffice application for travel agency with GWT
- Migrating large TV/set-top box application from Flash to JavaScript/jQuery
- PhoneGap application with jqMobi, require.js, underscore.js, backbone.js


Applying to Your First Co-op Job
Hey world! It’s been a long time since I last posted. I apologize for this delay, but I’ve never been so busy in my life. I can’t believe that first year is going to be over in one month from now. Time flies.
Today, I’ll be going over my experience applying to my first co-op job and providing tips to maximize your chances. This blog post will be tailored towards engineering and math students, though students of all faculties can benefit from reading this post.
Step 1: Gather relevant experiences and projects.
What I mean by this step is to actually do stuff and have content to put on your resume. Getting a job in a field related to your major would be awesome to include on your resume. However, this isn’t realistic for the majority of people, so another approach is to get involved in extracurriculars and student design teams. I would recommend checking out the Federation of Students clubs and the engineering student design teams.
For example, Bridges to Prosperity provides civil engineering students with a relevant and enriching experience related to their major. The UW Robotics Team is an awesome opportunity to get involved with robotics. The UW Designed Nanoscale Assembly Team focuses on designing applicable biomedical and nanotechnology innovations for applications such as medicine. Joining clubs not only improve your resume, but they’re also incredibly fun!
Step 2: Craft the best resume ever.
Now that you’ve gathered all the content to stuff your resume with, you can focus on the most important step. I won’t go over how to write a resume, since there are numerous guides online. From what I’ve noticed, the best resumes have the following characteristics: one pager, clean, concise and visually appealing.
Having a great resume is critical in landing that first co-op job, since it’s the only thing employers can evaluate you by. The first draft of your resume probably will be quite different from the final copy you submit on Jobmine. It is absolutely crucial to receive feedback on your resume and get it critiqued by others. In September, I thought my resume was great, but clearly that was not the case after the upper-years destroyed my resume with red ink. Also, only write a cover letter if you can write a great non-generic one. Remember that crafting the perfect resume is an ongoing process and improvements can always be made.
Step 3: Apply to jobs on Jobmine.
Depending on your co-op stream, the job application process begins in early September (stream 4), January (stream 8), or May (sequence 4 math students). On the second Saturday of the academic term, job postings open up on Jobmine. You upload your resume on Jobmine and then apply to jobs on the portal. Applying to jobs is as simple as uploading your resume, and submitting your application.
For most programs, there are usually hundreds of job listings on Jobmine. In the first round, you have 50 job applications. This might seem like a lot, but it’s actually not. The first round of application closes the following Tuesday night. The second round of job postings opens up the next week and closes a few days later. My personal advice is to use 40-45 apps during the first round and the remaining apps for the second round because the jobs in the second round tend to be not as great.
Remember that you’re not limited to jobs on Jobmine. If you find a job that fascinates you outside of Jobmine, go ahead and apply to them! Several of my friends did this and have had success. For programs where there is a low supply of jobs, I would strongly recommend considering this route.
Step 4: Nail your interviews.
Interviews may start as early as the fourth Monday of the term and end on the eighth Thursday of the term. They may either be in person, Skype, or on the phone. All interviews will take place at the Tatham centre, unless otherwise specified. For the majority of the jobs, employers will only conduct one interview. Some employers may send you a coding challenge to do online or in person to determine if you’re fit for another interview. For the U.S. jobs, you can pretty much expect a multistage interview process. In general, interviews are typically around 30 minutes long, though they can range anywhere from 15 minutes to an hour. For technical interview preparation, I would recommend ‘Cracking the Coding Interview’ by Gayle McDowell. Another helpful resource is HackerRank
From my personal experience with interviews, I’ve noticed a general structure. In the first 5 minutes, you usually introduce yourself and tell them a little about yourself, your passions and the technologies you’ve worked with. Then, the employer will talk about their own company for the next 5-10 minutes. Next up is the technical interview questions, spanning roughly 15-20 minutes. This could be a straight up coding or a “how would you solve this” question. Or, it could be a review of your resume, and asking you specific questions about your experiences and projects.
For non-coding interviews, the 15-20 minute portion of the interview is mainly behavioural questions. A sample question could be “tell me about your experience and proficiency working with AutoCAD”. The employer usually asks type of questions to determine if you’re a good problem solver and to assess if you’re a right fit for the team. Finally, the interview concludes with you asking questions to the employer. Always come up with one or two good questions.
A key tip for interviews is be confident and don’t think of the interview as “OMG will they accept me?” Instead, think of the interview as a more formal conversation with your future coworkers. They selected you for an interview for a reason, don’t undersell yourself. Interviewers aren’t trying to make your life miserable by giving you hard questions, they’re just looking for someone they can work with.
Step 5: Rankings.
Hopefully, by this point, you’ve had a couple of interviews and walked out of them happy. On the eighth Friday of the term, rankings open up. The three possibilities are: offer, ranked, or not ranked. If it’s an offer, congratulations! If it’s ‘not ranked’, that means the employer did not select you.
You have until the following Monday morning to submit your rankings. You can rank the employers anywhere between 1 to 9, with ranking 1 meaning you want them. To secure a job offer, simply rank ‘1’ to the employer that gave you an offer. Give the other employers ranks that are not a ‘1’. If you weren’t ranked for any jobs, then you’re out of luck and will have to go to the continuous round. If you were ranked for jobs, then you’ll have to decide and strategize how to rank them. Note that it is possible to get matched with a job even if you ranked them a 9, so apply to jobs carefully. You do not want to be matched up with a job that you don’t want (it creates a lot of problems).
Step 6: Job matching.
Jobmine will apply an algorithm to determine how the rankings will be dealt with. To read more about the specific details, please read this post by CECA. You will find out on the Monday afternoon which job you were matched to. If you got matched with a job, then this is the end of the guide for you. Congratulations, I hope you enjoy your first co-op experience. If you still don’t have a job, then proceed to Step 7.
Step 7: Continuous round.
The continuous round begins the ninth Wednesday of the term and goes on until you have successfully found a job. Don’t be shocked or saddened if you have to go to the continuous round. In fact, only about one third of the students in Engineering and Mathematics are employed after the first round.
The continuous round functions pretty much the same way as the first round. You apply to jobs and do the typical interviews. The only twist is that rankings come out every Tuesday and Thursday night, rather than just once during the first round. Matches come out Wednesday and Friday mornings, so hopefully you have been matched up with a job by now. If not, don’t give up and keep on persevering.
If you happen to be without a job after the first month of the co-op term, it’s not the end of the world. You still have 4 more weeks to find a job. Even if you don’t, you can use the time to focus on self improvement and gather relevant experience and projects.
My personal experience and thoughts.
My experience with Jobmine has been exactly what I expected from the start: a lot of fun and stress. I worked on personal projects and did most of the things I mentioned in Step 1. I developed the first draft of my resume in September 2014, and continued improving it, until I was finally satisfied. I submitted ‘BoPengResumeV6.pdf’ to Jobmine on Saturday January 17th, 2015 and proceeded to Step 3. I had a very ambitious mindset, applying to several jobs in California and most of senior level.
In the first round, I received 3 interviews. The first was for a large company in Seattle, though admittedly I got wrecked in the phone interview. The second was an established company based in Waterloo. The competition for this job was tough and I was not as prepared for the interviews as I should have been. My last interview was for another senior level job in Waterloo, where I was competing with upper-year students with multiple co-op experiences under their belt.
I was not ranked by all three employers which made me feel a bit sad. My upper-year friends advised me that the continuous round would an amazing opportunity for me, so I was quite optimistic about the future. I revamped to a new, cleaner, more elegant design. Then, I uploaded ‘BoPengResumeV7.pdf’ on Jobmine on March 4th, 2015 and applied to about 130 more jobs. The next thing you know, I get several interviews during the first week alone. I am pleased to announce that I will be working at Bidvine as a Software Developer for the Spring 2015 term.
To conclude, getting that first co-op job is not an easy task. As I’m writing this post, most programs currently have employment rates of less than 50%. However, the SE class last year had a final employment rate of 99.3%. What this means is that most students will find a job in the next few weeks. The entire co-op application process is not as pretty as advertised and many students struggle to find suitable jobs. If you follow my advice, then you can optimize your chances of getting that first co-op job.


Introduction to the use of computers for digital media creations, this course covers
the fundamentals of how computers work in the creation of digital media for commercial
entertainment and fine art purposes; including hardware components, operating principles,
software basics, memory management, storage, programming basics for vector and bitmap
functions, compression schemes and basic scripting.
The projects in this course often called for students to present information about a
program or technology that would be considered useful within our field. The final project
was a presentation in which each group would lead the class in teaching peer students how to
do a certain process. With our experience from multiple different courses, we taught our class
the basic foundation of how to use Drupal CMS, install and how to use the available modules.


rm
OS/161 Reference Manual
Name
rm - remove (unlink) files
Synopsis
/bin/rm file...
Description
rm deletes the files specified on its command line. (If one of the
filenames specified is one of several hard links to the same file, the
actual file is only erased when all links are removed.)
Using rm on directories produces an error. Use
rmdir to remove
directories.
Requirements
rm uses the following system calls:
rm should function properly once the file system assignment is
completed.
Restrictions
emufs does not support rm. (This is
intentional.)
See Also
rmdir


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java.lang.Object synoptic.invariants.fsmcheck.FsmModelChecker
public class FsmModelChecker
Implements two different finite-state-machine based synoptic.model checkers. The first is implemented using bitsets, and therefore can evaluate many synoptic.invariants at once in one relatively efficient pass. Following this pass, a less efficient synoptic.model is invoked, which keeps track of the path required to end up in the failing state.
| Constructor Summary | |
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FsmModelChecker()
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| Method Summary | ||
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protected static
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bitFilter(java.util.BitSet set,
java.util.List<E> list,
java.util.List<E> results)
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static
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getCounterExample(BinaryInvariant invariant,
IGraph<T> graph)
Runs invariant-checking finite state machines over the synoptic.model graph, while keeping history paths which justify any particular state. |
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static
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runBitSetChecker(java.lang.Iterable<BinaryInvariant> invariants,
IGraph<T> graph)
Use the BitSet checker to evaluate, and return which synoptic.invariants failed. |
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static
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runChecker(IStateSet<T,S> initial,
IGraph<T> graph,
boolean earlyExit)
Given an initial StateSet, and graph to check, this yields the fixpoint states eventually reached. |
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protected static
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whichFail(FsmStateSet<T> initial,
IGraph<T> graph)
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| Methods inherited from class java.lang.Object |
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clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
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public FsmModelChecker()
| Method Detail |
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public static <T extends INode<T>,S extends IStateSet<T,S>> java.util.Map<T,S> runChecker(IStateSet<T,S> initial, IGraph<T> graph, boolean earlyExit)
S
- The type of StateSet we are propagating.initial
- The initial state of each node.graph
- The graph to analyze.
protected static <T extends INode<T>> java.util.BitSet whichFail(FsmStateSet<T> initial, IGraph<T> graph)
protected static <E> void bitFilter(java.util.BitSet set, java.util.List<E> list, java.util.List<E> results)
public static <T extends INode<T>> java.util.List<BinaryInvariant> runBitSetChecker(java.lang.Iterable<BinaryInvariant> invariants, IGraph<T> graph)
public static <T extends INode<T>> RelationPath<T> getCounterExample(BinaryInvariant invariant, IGraph<T> graph)
invariant
- The invariant to test.
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In the following experiment, you will be asked to control a robot moving in the 2d gridworld on the right.
Please take a moment to test moving the robot.
Note: Running out of battery or touching a yellow recharge tile will reset the world.