Unfair Grounds
A magazine spread about the problems with Fair Trade coffee. I set up a photoshoot and shot all the photographs. I also crafted the title out of coffee grounds and photographed it.
A magazine spread about the problems with Fair Trade coffee. I set up a photoshoot and shot all the photographs. I also crafted the title out of coffee grounds and photographed it.


Set 3 years after the events of Uncharted 3, Nathan Drake has apparently left the world of fortune hunting behind. However, it doesn't take long for adventure to come calling when Drake's brother, Sam, re-emerges asking for his help to save his own life and offering an adventure Drake cannot resist. On the hunt for Captain Henry Avery's long-lost treasure, Sam and Drake embark on a journey to find Libertalia, the pirate utopia deep in the forests of Madagascar. Uncharted 4: A Thief's End takes players around the globe, through jungle isles, urban cities and snow-capped peaks on the search for Avery's fortune.
2.Overwatch
Overwatch is a highly stylized team-based shooter set on earth in the near future. Every match is an intense multiplayer showdown pitting a diverse cast of soldiers, mercenaries, scientists, adventurers, and oddities against each other in an epic, globe-spanning conflict.
3.Deus Ex: Mankind Divided
The action of Deus Ex: Mankind Divided takes place in 2029, a couple of years after the events of Human Revolution and the infamous 'Aug Incident' in Panchaea that resulted in the deaths of millions at the hands of those who had installed augmentations. This event has caused a massive divide between those who have augmentations and those who don't. Amidst this emotional turmoil are various factions aiming to manipulate the public by twisting public opinion of augmentation to advance their own agenda and hide the truth of what really occurred.
4.Horizon Zero Dawn
Horizon Zero Dawn is an action role playing game exclusively for the PlayStation 4. As Horizon Zero Dawn's main protagonist Aloy, a skilled hunter, explore a vibrant and lush world inhabited by mysterious mechanized creatures. Embark on a compelling, emotional journey and unravel mysteries of tribal societies, ancient artifacts and advanced technologies that will determine the fate of this planet, and of life itself.
5.Street Fighter V
Street Fighter V upholds the series' legacy, offering a diverse cast of 16 characters that bring its great fighting engine to life. Combatants new and old alike vie for your attention with infectious personalities and unique fighting styles.One of the most noticeable differences between Street Fighter V and its predecessor is the removal of the Focus Attack.
6.Doom
There is no taking cover or stopping to regenerate health in campaign mode as you beat back Hell's raging demon hordes. Combine your arsenal of futuristic and iconic guns, upgrades, movement and an advanced melee system to knock-down, slash, stomp, crush, and blow apart demons in creative and violent ways. In multiplayer, dominate your opponents in DOOM's signature, fast-paced arena-style combat. In both classic and all-new game modes, annihilate your enemies utilizing your personal blend of skill, powerful weapons, vertical movement, and unique power-ups that allow you to play as a demon. DOOM SnapMap is an easy-to-use game and level editor that allows for limitless gameplay experiences on every platform. Anyone can snap together and customize maps, add pre-defined or custom gameplay, and edit game logic to create new modes.
7.HITMAN - Episode 3: Marrakesh
Hitman Episode 3: Marrakesh presents players with a range of new in-game Challenges and Opportunities that keep them busy along with an entirely new setting for Escalation Contracts and Elusive Targets.
8.Mirror's Edge Catalyst
Through Mirror's Edge Catalyst, experience the emergence of Faith, a brave Runner who uncovers a hidden truth inside the walls of the city of Glass. Through her journey, Faith explores every corner of the city, from the highest, most pristine skyscrapers, to the dark and dingy tunnels that underlie the city. Players must master the environment, using their momentum and timing their combat to fight the oppressiveness of the ruling elite, uncovering the dark secrets behind the city's beautiful facade. Mirror's Edge Catalyst offers players the freedom to explore the expanse of the city, discovering a variety of new activities, like 'Dash', races from point A to B, environmental puzzles and story missions to complete at
9.Paragon
Players entrenched in the game's access have been clamouring for features that come as standard in League of Legends and DOTA 2, but which have taken a back seat to the important task of game balancing.One of the most important items on this wishlist is draft mode, which Epic is finally introducing in an upcoming patch.
10.LEGO Star Wars: The Force Awakens
LEGO Star Wars: The Force Awakens beautifully captures both the authenticity and excitement of the film. In addition to its cute and clever representations of people and places from The Force Awakens, the new LEGO Star Wars tests even the most obsessed fan's knowledge and attention to detail.
Most Anticipated
Watch Dogs 2
Release Date : Novermber 15, 2016
Injustice 2
Release Date : 2017
Confirmed characters so far include Batman, Superman, Supergirl, Aquaman, as well as newcomers Red Lantern Atrocitus and Gorilla Grodd. Likewise, on the location front, Metropolis, Gotham City, and Atlantis have also been confirmed as battle arenas.No release date has been mentioned, but you can expect Injustice 2 at some point in 2017 for PlayStation 4 and Xbox One.
Titanfall 2
Release Date : December 2016
Tom Clancy's Ghost Recon Wildlands
Release Date : 2017
Facing an almighty enemy in a massive and hostile environment, the Ghosts will need to make critical moral choices and engage in tough battles to complete their mission - their grittiest and most dangerous operation to date.


Enter the IP address printed from your microcontroller's Serial readout here, press Submit/Connect, and start working.
Local IP: Submit/Connect
CSV Name:


Post #7: Values
Affirmation & Stereotype Threat
July 9, 2015
The values I chose include the following, in no particular order:
- close relationships
- challenge
- helping society
- accountability (or reliability)
- introspection
- competition
- confidence
- personal development
- meaningful work
- faith
- intellectual status
I try to live up to all of these values because they are my values. I am certainly more and less successful at following through on these at various points in life, but they remain my values.
I will elaborate on "personal development" as one of my values. I think this actually encompasses the rest of the values. At one point on staff at the high school I worked at, we took time to think about our personal missions. A large part of my mission is to be ever-improving. To live with purpose and have pride in what I do. I see this in personal development as I want to never settle but always work to improve as a person, which includes as a family member, a friend, an employee, a citizen, a human, and a person of faith.
The most frequent advice I have given has been to my girls at the school where I worked. We have an advisory program where teachers are assigned a group of same-sex students to help mentor in life and school. By the end of year three, they solicited my advice pretty frequently. I would say this relates to personal development, challenge, meaningful work, and close relationships. If their questions related to school, I would always push them to challenge themselves for personal growth and know that their hard work would pay off in the future and that it mattered. If it was more personal, then it may have been related to maintaining and navigating important relationships with family or friends. I would probably give advice that supports what I find valuable in life.
I feel proud when I think about my values. I like my values. Obviously I feel most proud when I am living them well. I don't know if I see how my values will help address my sensing of stereotype threat. I can see how stereotype threat could stifle one's realization of their values, so that is extra motivation to be self-aware. Perhaps introspection, confidence, and some others could relate. As far as other struggles I might encounter while working at DBC, I think it will be most important for me to establish good relationships with people in my cohort, to take time for myself and to keep being introspective, and to continue to remember the satisfaction that comes with facing a challenging, persisting, and finding success.


Class: life
http://purl.org/biotop/btl2.owl#Life
Annotations (2)
-
definition "The whole process which is characterized by the existence of a physical or information entity. E.g. the life process of an organism perdures as long as this entity exists.
" ()
-
label "life" ()
Superclasses (3)
Usage (8)


My lifelong friend Adam and I were camping in the Michigan UP. Our campsite was right on Lake Superior. Just beyond our tent, long slabs of rock jutted out into the lake. We had been drinking whiskey and laying in the sun all day, smoking hash and cannabis, having a splendid day.
The sun started to set and I was getting the fire ready when Adam said he had a surprise for me, and produced two brown capsules. He told me to trust him, and in a gesture of recklessness I washed the capsule down with whiskey and proceeded to ask what it was. 'Two see tee two.' He said. 'Huh,' I said, and went about my business splitting wood and getting us ready for a long night's fire. I later found out that the dose was 20 mg.
The sun is down now, the sky purpled, and it's been about 45 minutes after we swallowed the pills, Adam gets up and starts behaving strangely. He gets down on his stomach and stares at the grass with his flashlight. Consequently I start acting like the adult of the group, tending the fire and trying to talk my wasted friend back to sanity.
He keeps asking how I'm feeling and I say fine. I don't feel anything. Then, about 90 minutes after ingenstion, I start giggling. It feels warm like a good dose of vicoden. Adam points the flashlight at the grass near my feet and says, 'Look at the grass.' I looked for a second and didn't see anything. Then I noticed a subtle movement and sparkling.
Up until this point, I have smoked plenty of cannabis in my life, eaten mushrooms once with little effect, and taken the usual painkillers for kicks. That's it. No raves, no sweaty jungle dance orgies on Exstacy, nothing.
I felt like doors in my mind were being blown down. Everything moved, breathed, the fire I was tending wobbled and rose into the air. The tents of our fellow campers looked like they were rising into the air. When I looked at my hand with a flashlight, it seemed to turn into an old man's hand and sprout white hairs rapidly. 'This is some Fear and Loathing shit,' I heard myself saying. We laughed and had the most open and honest and glad-to-be-alive conversation that I've ever experienced. The stars were out in full and they exploded in colors indescribable. We both kept saying that it was the perfect evening, that the planets were aligned, that we both had a better idea of what God was. We layed out on the rocks for what seemed like hours, but when we walked back to the fire we managed to figure out that it had only been about 15 minutes.
The coals were the most interesting. There were lots of faces, kaleidescopes absolutely full of faces, lots of them close-up and indistinguishable. Adam and I both had this in common. 'Do you see the faces?' He had said. I immediately replied, 'Yes, I see the faces.' Eyes, mouths - it was like a strange mosaic of people staring at us. A few faces came in conherently and I recognized them as people I knew in passing who I thought I'd forgotton about. Animations also abounded, and I was eventually able to get control over them and will myself to see certain things. After about five hours we started smoking hash and weed, and we both had a beer. The trip went on all night. We crawled into the tent around dawn, so it must have been around eight or nine hours total. We both fell asleep to mild visuals.
Overall, having never experimented with drugs of this nature, this experience changed my life. I experienced no nausea, no discomfort, no panic. Tranquility and lucidity ran strong throughout this experience. I'm sure the setting had a lot to do with this.
Someone thinking about taking this drug should not assume that they'll have a good time or come out of it with 'expanded consciousness.' As reality started bending around me and I began to realize the seriousness of the drug, I had an overwhelming feeling that I was ready for this. I wasn't a kid anymore, I wasn't rebelling against authority.  That, I think, gave very positive vibes to a trip that certainly could have been negative.
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.


Zarni Wang
Project Portfolio
Zarni Wang is a an Under Graduate student majoring in Computer Science at UCSD.
Projects
Digital Vision Screening
Android Google Chat
Habit Tracker


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RILEY SCOTT
05.30.19
Ohio was left out of the NCAA Tournament for the fourth consecutive year last season. The Bobcats felt they deserved a ticket to the dance after bringing a resume that included a 27-5 record, a Mid-American Conference East Division championship and a second-place finish in the MAC Tournament.
But it wasn’t enough. The selection committee favored the likes of other teams, and Ohio settled for the WNIT and fell to Northwestern in the Round of 8.
Ohio finished its historic year with the best record in program history. The Bobcats want more next season, and they’ll probably get it.
Here’s five reasons why Ohio will make the 2020 NCAA Tournament.
Coach Bob Boldon’s relaxed personality was a great mask to cover his disappointment when Ohio was left out of the 2018 NCAA Tournament. But Boldon is a man who knows what could have been. Last year, he often talked about how the 2017-2018 season hurt because the Bobcats were two wins away from the WNIT. Maybe the Bobcats missed out on the NCAA Tournament this season because it had one too many losses. Who knows? Boldon probably lost sleep over it. He’s built the Bobcats for a prolonged period of success, and that success still showed last season. The six-year head coach believes in his system but isn’t afraid to tweak to ensure the Bobcats have the best shot at winning.
At the end of a great season there’s always a dejected feeling because a team will never be the same. Seniors graduate and the top of a roster can take some heavy blows. But the damage to the Bobcats next season is minimal — they lost only three seniors. The loss that hurts the most is forward •Dominique Doseck, who was Ohio’s best 3-point shooter, smartest player and had strong leadership role in the locker room. The Bobcats also lost •forwards Olivia Bower and Kendall Jessing. At 6-foot-1, Bower provided Ohio with size and 3-point shooting, while Jessing produced rebounds and was one of the Bobcats most-respected voices in the locker room. Ohio will have to address those absences if it hopes to take another step, but the Bobcats are still young and talented. The future is still bright.
Kelsey Boeing | Photo Editor
Ohio University's Gabby Burris (#41) sits wide open on the three point line against Western Kentucky during the home game on Thursday, March 28, 2019.
While the Bobcats will miss Doseck, Bower and Jessing, the amount of talent coming back is enough to make fans blush. Cece Hooks, MAC Defensive Player of the Year, will return. Erica Johnson, the first player in MAC history to ever win Freshman of the Year and Sixth Player of the Year is also back. Amani Burke, a 3-point sharpshooter, will round out the trio of top talent. Ohio will also have healthy versions of Gabby Burris and Katie Barker, who Boldon calls the best shooter on the team. The team will also be deep with Deesh Beck, Kaylee Bambule, Abby Gardner and transfer guard Caitlyn Kroll off the bench. Ohio proved it can win with a young roster last season. With those new players transitioning to veterans, the Bobcats are one of the most lethal teams in the MAC.
To go along with Ohio’s returning talent is some fresh faces. Peyton Guice and Ella Pope hope to continue the string of great Ohio freshmen that Boldon has produced. They won’t be expected to produce much early, but expect at least one to find themselves some minutes.
Ohio finished second in the MAC… twice. Once in the regular season to Central Michigan, and again in the conference tournament to Buffalo. Both of those teams lost their best weapons, though. Central Michigan lost Reyna Frost, who won MAC Player of the Year, and one of the best point guards in the conference in Presley Hudson. Buffalo lost one of the greatest scorers the conference has ever seen in Cierra Dillard. While the conference will still be tough, the top two teams just lost their best talents. Ohio’s best players are all coming back and should only improve in talent.
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Hi ! Thanks for Dropping by , this is my own website. Developed ,managed ,and updated by me ,I am a Data Scientist/Deep learning Engineer at Deloitte Consulting and also author of a book on Deep Learning with TensorFlow! (Not in Python though).
To know more about me visit the ABOUT section of my website
I also an Active open source contributor to Google Datalab and creator of TensorFlow JS IDE plugin
I created my first website when I was in 6th Class (year 2006) ! , the website is still live and can be found HERE It basically shows details of my childishness :P What more can you expect from a 11 year old?
Below you can find Few blog posts that I write:


find out info about the communities in Chicago by typing there name in the search bar.(all caps please, ex:PORTAGE PARK, NORTH LAWNDALE, AUSTIN.) Check Stats


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CREDICOR
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INVENTRIX
LAKEFRONT RESORTS
MANUTECH
MINING GUILD
MONS INSURANCE
MORNING STAR
PHARMACY UNION
PHILARES
PHOBOLOG
POINT LUNA
POLYPHEMOS
POSEIDON
PRISTAR
RECYCLON
ROBINSON INDUSTRIES
SATURN SYSTEMS
SEPTEM TRIBUS
SPLICE
STORMCRAFT
TERACTOR
TERALABS
THARSIS REPUBLIC
THORGATE
U.N.M.I.
UTOPIA
VALLEY TRUST
VIRON
VITOR
---
BEGINNER
KUIPER BELT COOP.
APHRODITE
ARCADIAN COMMUNITIES
ARIDOR
ARKLIGHT
ASTRODRILL
CELESTIC
CHEUNG SHING MARS
CREDICOR
ECOLINE
FACTORUM
HELION
INTERPLAN. CINEMATICS
INVENTRIX
LAKEFRONT RESORTS
MANUTECH
MINING GUILD
MONS INSURANCE
MORNING STAR
PHARMACY UNION
PHILARES
PHOBOLOG
POINT LUNA
POLYPHEMOS
POSEIDON
PRISTAR
RECYCLON
ROBINSON INDUSTRIES
SATURN SYSTEMS
SEPTEM TRIBUS
SPLICE
STORMCRAFT
TERACTOR
TERALABS
THARSIS REPUBLIC
THORGATE
U.N.M.I.
UTOPIA
VALLEY TRUST
VIRON
VITOR
---
BEGINNER
KUIPER BELT COOP.
APHRODITE
ARCADIAN COMMUNITIES
ARIDOR
ARKLIGHT
ASTRODRILL
CELESTIC
CHEUNG SHING MARS
CREDICOR
ECOLINE
FACTORUM
HELION
INTERPLAN. CINEMATICS
INVENTRIX
LAKEFRONT RESORTS
MANUTECH
MINING GUILD
MONS INSURANCE
MORNING STAR
PHARMACY UNION
PHILARES
PHOBOLOG
POINT LUNA
POLYPHEMOS
POSEIDON
PRISTAR
RECYCLON
ROBINSON INDUSTRIES
SATURN SYSTEMS
SEPTEM TRIBUS
SPLICE
STORMCRAFT
TERACTOR
TERALABS
THARSIS REPUBLIC
THORGATE
U.N.M.I.
UTOPIA
VALLEY TRUST
VIRON
VITOR
---
BEGINNER
KUIPER BELT COOP.
APHRODITE
ARCADIAN COMMUNITIES
ARIDOR
ARKLIGHT
ASTRODRILL
CELESTIC
CHEUNG SHING MARS
CREDICOR
ECOLINE
FACTORUM
HELION
INTERPLAN. CINEMATICS
INVENTRIX
LAKEFRONT RESORTS
MANUTECH
MINING GUILD
MONS INSURANCE
MORNING STAR
PHARMACY UNION
PHILARES
PHOBOLOG
POINT LUNA
POLYPHEMOS
POSEIDON
PRISTAR
RECYCLON
ROBINSON INDUSTRIES
SATURN SYSTEMS
SEPTEM TRIBUS
SPLICE
STORMCRAFT
TERACTOR
TERALABS
THARSIS REPUBLIC
THORGATE
U.N.M.I.
UTOPIA
VALLEY TRUST
VIRON
VITOR
---
BEGINNER
KUIPER BELT COOP.


library(process)
This package contains utilities for process creation.
A process is represented by a process reference, a ground
compound term. Both SICStus and the operating system maintain a
state for each such process reference and they must therefore be
released, either explicitly with process_release/1
or
implicitly by process_wait/[2,3]
. Process references are
created with process_create/[2,3]
if explicitly requested with
the process/1
option. Process references are required in
order to obtain the exit status of a process. Many of the
predicates can accept a numeric operating system process id
(“PID”) but since process ids are subject to re-use by the OS
this is less reliable and does not work if the process has already
exited.
Run ls on a home directory in a subshell under UNIX:
| ?- absolute_file_name('$SHELL', Shell), absolute_file_name('~/', Dir), process_create(Shell, ['-c', [ ls, ' ', file(Dir) ]]).
Run notepad.exe on a file C:/foo.txt under Windows:
| ?- absolute_file_name('$SYSTEMROOT/notepad.exe', Prog), process_create(Prog, [file('C:/foo.txt')]).
Exported predicates:
process_create(
+File,
+Args)
process_create(
+File,
+Args,
:Options)
process_wait/[2,3]
.
File, is expanded as if by absolute_file_name/2
(with argument access(execute)
) and
is used to locate the file to execute.
The predefined file search path path/1
(see ref-fdi)
is especially useful here since it makes it easy to look up the
names of an executable in the directories mentioned by the
PATH
environment variable. To run the Windows command shell
cmd you would simply specify path('cmd.exe')
, to
start the UNIX Bash shell you would specify path(bash)
.
Args is a list of argument specifications. Each argument specification is either a simple argument specification, see below, or a non-empty list of simple argument specifications. The expanded value of each element of Args is concatenated to produce a single argument to the new process. A simple argument specification can be one of:
file(
File)
file(
File)
is not subject to syntactic rewriting, the
argument specification file/1
only adjusts for differences
in file name syntax and character
encoding between SICStus and the operating system. You
must explicitly call
absolute_file_name/[2,3]
if you want to expand file search
paths etc.
stdin(
Spec)
stdout(
Spec)
stderr(
Spec)
std
pipe/1
spec, see below, and explicitly read (write) data
from (to) the process.
null
pipe(
Stream)
close/[1,2]
, it is not
closed automatically when the new process exits.
process(
Proc)
process_wait/[2,3]
etc.. This process reference
must be released, either explicitly with process_release/1
or
implicitly by process_wait/[2,3]
.
detached(
Bool)
true
or false
. Specifies
whether the new process should be “detached”, i.e. whether it
should be notified of terminal events such as ^C
interrupts. By default a new process is created detached if none
of the standard streams are specified, explicitly or implicitly,
as std
.
cwd(
CWD)
absolute_file_name/2
and
is used as the working directory for the new process.
By default, the working directory is the same as the Prolog
working directory.
window(
Bool)
true
or
false
(the default). Specifies whether the process should
open in its own window.
Specifying window(true)
may give unexpected results if the
standard stream options stdin/1
, stdout/1
and
stderr/1
are specified with anything but their default
value std
.
Currently only implemented on Windows.
process_wait(
+Process,
-ExitStatus)
process_wait(
+Process,
-ExitStatus,
+Options)
Process is either a process reference obtained from
process_create/3
or an OS process identifier. Specifying a
process identifier is not reliable. The process identifier may
have been re-used by the operating system. Under Windows, it is not
possible to obtain the exit status using a process identifier if
the process has already exited.
ExitStatus is one of:
exit(
ExitCode)
killed(
SignalNumber)
SignalNumber
(a
positive integer).
timeout
timeout/1
option was specified and the process did not
exit within the specified interval. In this case the process
reference is not released, even if the release/1
option is
specified.
timeout(
Seconds)
infinite
(the default) to specify
infinite wait. If the specified timeout interval passes before the
process exits, process_wait/3
exits with ExitStatus
set to timeout
and the process reference is not released.
Currently the UNIX implementation supports only timeout values
0 (zero) and infinite
.
release(
Bool)
true
(the default) or
false
. Specifies whether the process reference should be
released when process_wait/3
exits successfully.
process_id(
-PID)
process_id(
+Process,
+PID)
is_process(
+Thing)
process_release(
+Process)
process_create/3
. This ensures that Prolog
and the operating system can reclaim any resources associated with
the process reference.
Usually you would not call this. Either do not request the process
reference when calling process_create/3
or let
process_wait/[2,3]
reclaim the process reference when the
process terminates.
process_kill(
+Process)
process_kill(
+Process,
+SignalSpec)
The following signal names are accepted under UNIX if the platform
defines them: SIGABRT
, SIGALRM
, SIGBUS
,
SIGCHLD
, SIGCONT
, SIGFPE
, SIGHUP
,
SIGILL
, SIGINT
, SIGKILL
(the default),
SIGPIPE
, SIGPOLL
, SIGPROF
, SIGQUIT
,
SIGSEGV
, SIGSTOP
, SIGSYS
, SIGTERM
,
SIGTRAP
, SIGTSTP
, SIGTTIN
, SIGTTOU
,
SIGURG
, SIGUSR1
, SIGUSR2
, SIGVTALRM
,
SIGXCPU
and SIGXFSZ
. However, many of these do not
make sense to send as signals.
Under Windows, which does not have the signal
concept, the signal name SIGKILL
(the default) is treated
specially and terminates the process with
TerminateProcess(Process, -1)
.


Continuity timeframe
Thought we'd share the following email that we sent out on the developers mailing list (urbit-dev@googlegroups.org):
Welcome to Urbit! You've been added to this mailing list because you all are now proud owners of one or more destroyers (let us know if you're not and we'll remedy that). We'll be using this list to update all of you with the latest news from the offworld colonies. Presently your subscription settings are at one digest email daily (we have a powerful hatred of listserv-spam). Feel free to change these settings on Google Groups to whatever you like.
Our target date for network continuity is Friday October 4th. At which point the data structures we create will (if we succeed) live forever. We'll almost certainly do at least one reboot sometime before then, so you'll have to recreate your destroyers from your keys when we do.
Also, we plan on putting up some actual Hoon doc by then, so you all can, you know, actually learn Hoon.
I'll leave you with some Borges: "Stranger and more pure than any hron is, at times, the ur: the object produced through suggestion, educed by hope."
Since this blog post is not limited to 1000 words (seriously Google) and because I love that quote so damn much, I'll post the whole thing:
"In the most ancient regions of Tlön, the duplication of lost objects is not infrequent. Two persons look for a pencil; the first finds it and says nothing; the second finds a second pencil, no less real, but closer to his expectations. These secondary objects are called hronir and are, though awkward in form, somewhat longer. Until recently, the hronir were the accidental products of distraction and forgetfulness. It seems unbelievable that their methodical production dates back scarcely a hundred years, but this is what the Eleventh Volume tells us.
[...]
The methodical fabrication of hronir (says the Eleventh Volume) has performed prodigious services for archaeologists. It has made possible the interrogation and even the modification of the past, which is now no less plastic and docile than the future. Curiously, the hronir of second and third degree - the hronir derived from another hron, those derived from the hron of a hron - exaggerate the aberrations of the initial one; those of fifth degree are almost uniform; those of ninth degree become confused with those of the second; in those of the eleventh there is a purity of line not found in the original. The process is cyclical: the hron of the twelfth degree begins to fall off in quality. Stranger and more pure than any hron is, at times, the ur: the object produced through suggestion, educed by hope."


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|
C++ Actor Framework 0.19
|
Accepts incoming clients with an Acceptor and handles them via a connection factory. More...
#include <accept_handler.hpp>
Public Types | |
| using | socket_type = net::socket |
| using | transport_type = typename Acceptor::transport_type |
| using | connection_handle = typename transport_type::connection_handle |
| using | factory_type = connection_factory< connection_handle > |
| using | factory_ptr = detail::connection_factory_ptr< connection_handle > |
Public Member Functions | |
| accept_handler (Acceptor acc, factory_ptr fptr, size_t max_connections, std::vector< strong_actor_ptr > monitored_actors={}) | |
| error | start (net::socket_manager *owner) override |
| Starts processing on this layer. | |
| net::socket | handle () const override |
| Returns the handle for the managed socket. | |
| void | handle_read_event () override |
| Handles a read event on the managed socket. | |
| void | handle_write_event () override |
| Handles a write event on the managed socket. | |
| void | abort (const error &reason) override |
| Called on socket errors or when the manager gets disposed. | |
| void | self_ref (disposable ref) |
| Public Member Functions inherited from caf::net::socket_event_layer | |
| virtual error | start (socket_manager *owner)=0 |
| Starts processing on this layer. | |
| virtual socket | handle () const =0 |
| Returns the handle for the managed socket. | |
| virtual void | handle_read_event ()=0 |
| Handles a read event on the managed socket. | |
| virtual void | handle_write_event ()=0 |
| Handles a write event on the managed socket. | |
| virtual bool | do_handover (std::unique_ptr< socket_event_layer > &next) |
Called after returning handover from a read or write handler. | |
| virtual void | abort (const error &reason)=0 |
| Called on socket errors or when the manager gets disposed. | |
| virtual bool | finalized () const noexcept |
| Queries whether the object can be safely discarded after calling abort on it, e.g., that pending data has been written. | |
Static Public Member Functions | |
| static std::unique_ptr< accept_handler > | make (Acceptor acc, factory_ptr fptr, size_t max_connections, std::vector< strong_actor_ptr > monitored_actors={}) |
Accepts incoming clients with an Acceptor and handles them via a connection factory.
|
overridevirtual |
Called on socket errors or when the manager gets disposed.
Implements caf::net::socket_event_layer.
|
overridevirtual |
Returns the handle for the managed socket.
Implements caf::net::socket_event_layer.
|
overridevirtual |
Handles a read event on the managed socket.
Implements caf::net::socket_event_layer.
|
overridevirtual |
Handles a write event on the managed socket.
Implements caf::net::socket_event_layer.
|
overridevirtual |
Starts processing on this layer.
Implements caf::net::socket_event_layer.


influenza samples genome reads Nanopore sequencing virus using clinical OpenUrl sample Hazara dilution viral Influenza generated pooled Fig cDNA diagnostic titre RNA sequence titres data positive sensitivity sequences series throat total CrossRef control individual laboratory metagenomic respiratory time <;sup>;<;/sup>; (A) (B) approach derived mapping pools segments background detection samples, Abstract influenza-negative negative protocol samples. value values Download FREE Illumina Samples Text acid figure human infection methods min new nucleic polymerase pool proportion series. Data Figure Health Open Oxford PCR bacterial data-hide-link-title=""> data-icon-position="" days different following generate high method potential produced range shown spiked tab test used amplification collected coverage described diluted higher million present processing testing ;<;div Coverage Research S). SISPA [Internet]. complete consensus consistent contain copies/ml. ferret number optimisation previously protein, subunit, swabs, tested vaccine xmlns=";http://www.w.org//xhtml";>;<;span A). Clin Clinical GeneXpert Human Microbiol. Sample Sequencing Total Viral analysis compared concentration copies followed highly incubation inform material min. prior reduced required selected sequencing, study swab swabs tests transmission triplicate v.. .μl Available: Correlation Detection Dilution Lewandowski Public RPM Reads Sequenase Viruses. according addition assay assess bioRxiv. cell classified comparable copies/ml copies/ml), copies/ml). demonstrated detect directly existing experiment flow gene genomes health host identify improve increased influenza-positive insights kit large length manufacturer’s mapped material, mean nasal offers p-value performed post provide reads. real-time reference research respectively. results reverse scale) sequenced set standard substantially synthesis undertaken virus. viruses viruses.


Welcome to part 2 of this series. In the previous post we setup our problem which is to speed up the SliceMap
family of types for sparse data. We created benchmarks and measured the performance of the current implementation. I gave a brief overview of a new approach I had come up with and showed how it failed miserably.
We were in a depressing place at the end of the last post but hope burns eternal! I have already been researching approaches for this problem on and off for a year, so I didn’t expect the problem to be slain in a day. Rather than giving up, I went searching for answers.
Enter Data-Oriented Design
Recently I have been researching Data-Oriented Design. My first introduction to it was a great talk by Mike Acton as CppCon. I regularly watch talks on other languages and paradigms to grow my understanding of the field and this talk in particular struck a chord. While some may find Mike’s delivery a little brusque, I found it refreshing. The talk is littered with great lines, but the following is one of my favorite.
Reality is not a hack you’re forced to deal with to solve your abstract, theoretical problem. Reality is the actual problem.
Mike Acton
Overall, Data-Oriented Design emphasizes data and its transformation as the key thing to design around. It generally eschews Object Orientation as a means of decomposing problems and instead looks at what data layouts and access patterns allow us to extract the maximum performance. This talk sent me deep down a rabbit hole. Eventually I found my way to Jonathan Blow who has given many great talks online. I decided to pick up the book “Data-Oriented Design” by Richard Fabian.
I’m still struggling with how I could use Data-Oriented Design to solve my slicing problem when I came to chapter 6 which discusses Searching. On page 114 of the paperback Richard describes how we can have data structures for looking up data that keep track of the query patterns being used. Once a threshold is met, the data could be re-ordered to better suit how the data is being accessed.
This was the moment of insight for me. “Wait!” I said to myself. “In real world use cases, you are often slicing across 1 dimension of the data many times in a row. Then you may start slicing across another dimension many times in a tight loop. Why not have the SliceMap re-order it’s data to be optimal for the types of lookups that are being performed!”
Idea 3: Reorganizing Internals
I went back to the drawing board and reworked how data was being stored in the SliceMap
types. The internal fields of the 1 dimensional SliceMap
remained simple. We give the SliceMap
a comparer for comparing the keys when performing the Hadamard Product. keys
is just a chunk of memory that is sorted. values
is contiguous memory where the position is what determines the key it goes with.
type SliceMap<'k, 'v when 'k : comparison>
(comparer: IComparer<'k>,
keys: ReadOnlyMemory<'k>,
values: ReadOnlyMemory<'v>) =
let comparer = comparer
let keys = keys
let values = values
SliceMap2D
gets a little more interesting. We need to remember that a SliceMap2D
can be thought of as a table in a database where the primary key is made up of two fields: Key1 and Key2. Here is what some example data could look like.
| Key1 | Key2 | Value |
|---|---|---|
| 1 | “A” | 2.0 |
| 1 | “B” | 8.0 |
| 1 | “C” | 3.0 |
| 2 | “B” | 1.7 |
| 2 | “C” | 1.7 |
| 3 | “A” | 9.4 |
| 3 | “B” | 4.6 |
| … | … |
Since we are trying to optimize the speed of slicing the data, we are willing to do some work up front to organize the data. When we initially create the SliceMap2D
, we will sort the data by Key1 then Key2. This will allow us to use Run Length Encoding on the outer keys, Key1 in this case. We will store the length of the runs of the outer key in an IndexRange
type.
[<Struct>]
type IndexRange = {
Start : int
Length : int
}
We will use two arrays for storing Key1 data. One array for the values of Key1, another for the IndexRange
that corresponds to the key. We will call these fields OuterKeyValues
and OuterKeyRanges
respectively. Key2 and Values will be stored in a ReadOnlyMemory
of their respective types. Key2 and Values have a 1 to 1 matching based on their location in their containers. We can now define SliceMap2DInternals
for storing this information.
[<Struct>]
type SliceMap2DInternals<'k1, 'k2, 'v when 'k1 : comparison and 'k2 : comparison> = {
OuterComparer : IComparer<'k1>
InnerComparer : IComparer<'k2>
OuterKeyValues : 'k1[]
OuterKeyRanges : IndexRange[]
InnerKeyValues : ReadOnlyMemory<'k2>
Values : ReadOnlyMemory<'v>
}
Now, you may notice that I was talking about Key1 and Key2 but then switched to talking about OuterKey and InnerKey. This is where things may get confusing but trust me, we’ll get there! We need SliceMap2D
to be able to restructure itself in order to provide fast slicing across Key1 or Key2. If Key1 data is stored in the OuterKey fields, then it is much faster to slice along Key1 because all we need to do it find the range of values it applies to and simply just slice the memory for InnerKeyValues
and Values
to create a SliceMap
.
If Key2 is stored in the InnerKeyValues
field, it is difficult to slice because a particular value of Key2 could occur in multiple places in InnerKeyValues
. But what if we were able to flip which key was stored in the OuterKeyValues
and OuterKeyRanges
fields and which one was stored in InnerKeyValues
? Well, then we could slice along the Key2 dimension quickly since all its values would be contiguous after flipping.
The “problem” is that F# is statically typed and doesn’t like you changing the type of fields. Fortunately, every problem in F# is solved with another type. Enter the SliceMap2DState
.
type SliceMap2DState<'k1, 'k2, 'v when 'k1 : comparison and 'k2 : comparison> =
| Key1Key2 of SliceMap2DInternals<'k1, 'k2, 'v>
| Key2Key1 of SliceMap2DInternals<'k2, 'k1, 'v>
What this Discriminated Union is doing is containing the information for how the keys are stored in the SliceMap2DInternals
. It tells us if Key1 is in the outer fields or if Key2 is. Now we can define SliceMap2D
.
type SliceMap2D<'k1, 'k2, 'v when 'k1 : comparison and 'k2 : comparison>
(internalState: SliceMap2DState<_, _, _>) =
let mutable internalState = internalState
Notice, SliceMap2D
is storing its state in a mutable field so it can change it when it wants. When you go to slice along a dimension, it will check how the data is laid out. If the data is not laid out for efficient slicing, it will swap the keys around. Here is what the slicing method looks like.
member _.Item
// Ignoring `f` at this time
with get (x: 'k1, f: Filter) =
// Get the internals
let internals =
// Check which state the internals are in
match internalState with
// This is the ideal state for serving up this slice so do nothing
| SliceMap2DState.Key1Key2 i -> i
// If the internals are in this state, we will swap the keys so
// that slicing can be fast
| SliceMap2DState.Key2Key1 i ->
let reOrdered = SliceMap2DInternals.swapKeys i
internalState <- SliceMap2DState.Key1Key2 reOrdered
reOrdered
let mutable intervalIdx = 0
let mutable keepSearching = true
// Perform a scan to find the matching key. We'll come back to this 😉
while keepSearching && intervalIdx < internals.OuterKeyValues.Length - 1 do
if internals.OuterComparer.Compare (internals.OuterKeyValues[intervalIdx], x) = 0 then
keepSearching <- false
else
intervalIdx <- intervalIdx + 1
if not keepSearching then
// The key was found, and we can slice the contiguous data
let interval = internals.OuterKeyRanges[intervalIdx]
// We have ensured that the pertinent data is contiguous so we can use
// slicing to get the necessary data
let newKeys = internals.InnerKeyValues.Slice (interval.Start, interval.Length)
let newValues = internals.Values.Slice (interval.Start, interval.Length)
// Return a SliceMap with the data slice
SliceMap (internals.InnerComparer, newKeys, newValues)
else
// The key was not found, return an empty SliceMap
SliceMap (internals.InnerComparer, ReadOnlyMemory Array.empty, ReadOnlyMemory Array.empty)
The reason this is a valid optimization is that SliceMap
was never intended as a general-purpose data structure. It was built to make composing Mathematical Planning problems clean and simple. When creating constraints, the dominant usage pattern is to perform the same slice many times for different values. Honestly, I put too much functionality into the original SliceMap
. I lost focus on what the real problem was. You can see our solution up to this point at this repo and branch.
Did We Get Faster?
In the previous post we ran our benchmarks against the current implementation, and we got the following timings.
| Method | Mean | Error | StdDev |
|---|---|---|---|
| DenseData | 7.993 s | 0.0748 s | 0.0700 s |
| MediumSparsity | 2.154 s | 0.0176 s | 0.0156 s |
| HighSparsity | 1.209 s | 0.0134 s | 0.0126 s |
These are the timings we get for our new version of SliceMap2D
with self-adjusting internals.
| Method | Mean | Error | StdDev |
|---|---|---|---|
| DenseData | 379.05 ms | 5.281 ms | 4.940 ms |
| MediumSparsity | 113.99 ms | 0.647 ms | 0.574 ms |
| HighSparsity | 71.89 ms | 0.636 ms | 0.595 ms |
It looks a little better when we plot the performance against each other.
So, it got a little faster 😊. I almost cried when I saw this. The fact that this problem has been tormenting me for over a year problem had something to do with it. We have even more gains on the horizon! There are several other things we can do to speed this up. Feel free to check out this repo and branch to see what all of the code looks like and run the benchmarks for yourself! I welcome feedback and ideas!
Please send me an email at<EMAIL_ADDRESS>if you have any questions and subscribe so you can stay on top new posts and products I am offering.


Ticker-iQ lets users screen stocks, view financial history, key metrics, and manage portfolio assets and holdings all in real time.
Github Repo
# You will need to have Java version 8 install (SDK 1.8)
$ git clone https://github.com/ianramzy/ticker-iq.git
# Import the project into your IDE of choice and setup the SDK as 1.8. Depending on your installation you may need to install JavaFX separately. (Note: it may work with other versions of Java, but is untested)
# Run the program from the main.java class and from there you should be up and running! Be sure to also be connected to the internet so you can download new stock data.
I saw websites like tradingview.com and other similar projects, and I wanted a stock screener that I could run on my desktop while I traded. That was how Ticker-iQ was born. A desktop application that could process all of the basic information needed for swing trading as well as help me manage my portfolio.
Ticker-iQ presents various stock data in a visually pleasing way, it allows users to see daily trading numbers, price history, browse current news with the in-built browser, and setup their portfolio automatically calculating things like profit/loss per share and in total.
This program is created solely in JavaFX and all data is downloaded in realtime from nasdaq.com and then graphed visually with JavaFX. The portfolio manager saves information locally to a text file when a user inputs various data for computation.
The main challenge I ran into was where to get the data. Many sources would either require too many steps to get the data making automation next to impossible. Many sources are also are paid, making them not viable for my student budget. And lastly, many websites had scripts in place to protect against automated requests.
In the future I would like port this project to Android for mobile use. As well in the future I will start using the data I'm collecting to add technical indicators to graphs as an option.
Another project by Ian Ramzy


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JaCoP/Scala
JaCoPinScala is a Scala wrapper for JaCoP. Download from Sourceforge
Here are some of my JaCoP/Scala programs.
Also see My Constraint Programming Blog, and
* Common constraint programming problems
* MiniZinc page
* My Zinc page
* My Choco page
* My Gecode/R page
* My Comet page
* My Gecode page
* My ECLiPSe page
* My Tailor/Essence' page
* My SICStus Prolog page
* My Google CP Solver page
* My OscaR page
* My JSR-331 page
* My Numberjack page
* My AIMMS+CP page
* My B-Prolog page
* My Choco3 page
* My Picat page
Back to my JaCoP page
Created by Hakan Kjellerstrand hakank@gmail.com


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Learning text representations and text classifiers may rely on the same simple and efficient approach. 'fastText' is an open-source, free, lightweight library that allows users to perform both tasks. It transforms text into continuous vectors that can later be used on many language related task. It works on standard, generic hardware (no 'GPU' required). It also includes model size reduction feature. 'fastText' original source code is available at <https://github.com/facebookresearch/fastText>.
| Version: | 0.2.5 |
| Depends: | R (≥ 3.3) |
| Imports: | methods, Rcpp (≥ 0.12.12), assertthat |
| LinkingTo: | Rcpp |
| Suggests: | knitr, testthat |
| Published: | 2018-01-04 |
| Author: | Michaël Benesty [aut, cre, cph], Facebook, Inc [cph] |
| Maintainer: | Michaël Benesty <michael at benesty.fr> |
| BugReports: | https://github.com/pommedeterresautee/fastrtext/issues |
| License: | MIT + file LICENSE |
| URL: | https://github.com/pommedeterresautee/fastrtext, https://pommedeterresautee.github.io/fastrtext/ |
| NeedsCompilation: | yes |
| Materials: | NEWS |
| CRAN checks: | fastrtext results |
| Reference manual: | fastrtext.pdf |
| Vignettes: |
List of commands Supervised learning Unsupervised learning |
| Package source: | fastrtext_0.2.5.tar.gz |
| Windows binaries: | r-devel: fastrtext_0.2.5.zip, r-release: fastrtext_0.2.5.zip, r-oldrel: fastrtext_0.2.5.zip |
| OS X binaries: | r-release: fastrtext_0.2.5.tgz, r-oldrel: fastrtext_0.2.5.tgz |
| Old sources: | fastrtext archive |
| Reverse suggests: | projector |
Please use the canonical form https://CRAN.R-project.org/package=fastrtext to link to this page.


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public interface RunStore
| Modifier and Type | Method and Description |
|---|---|
TavernaRun |
getRun(String uuid)
Obtain the named workflow run.
|
TavernaRun |
getRun(UsernamePrincipal user,
Policy p,
String uuid)
Obtain the workflow run for a given user and name.
|
Map<String,TavernaRun> |
listRuns(UsernamePrincipal user,
Policy p)
List the runs that a particular user may access.
|
String |
registerRun(TavernaRun run)
Adds a workflow instance run to the store.
|
void |
unregisterRun(String uuid)
Removes a run from the store.
|
TavernaRun getRun(UsernamePrincipal user, Policy p, String uuid) throws UnknownRunException
user
- Who wants to do the lookup.p
- The general policy system context.uuid
- The handle for the run.UnknownRunException
- If the lookup fails (either because it does not exist or
because it is not permitted for the user by the policy).TavernaRun getRun(String uuid) throws UnknownRunException
uuid
- The handle for the run.UnknownRunException
- If the lookup fails (either because it does not exist or
because it is not permitted for the user by the policy).Map<String,TavernaRun> listRuns(UsernamePrincipal user, Policy p)
user
- Who wants to do the lookup, or null
if it is
being done "by the system" when the full mapping should be
returned.p
- The general policy system context.String registerRun(TavernaRun run)
run
- The run itself.void unregisterRun(String uuid)
uuid
- The name of the run.Copyright © 2015–2018 The Apache Software Foundation. All rights reserved.


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import os.path as op
import numpy as np
import mne
In MNE, epochs refers to a collection of single trials or short segments of time locked raw data. If you haven’t already, you might want to check out The Epochs data structure: epoched data. In this tutorial we take a deeper look into construction of epochs and averaging the epoch data to evoked instances. First let’s read in the raw sample data.
data_path = mne.datasets.sample.data_path()
fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(fname)
Out:
Opening raw data file /home/ubuntu/mne_data/MNE-sample-data/MEG/sample/sample_audvis_raw.fif...
Read a total of 3 projection items:
PCA-v1 (1 x 102) idle
PCA-v2 (1 x 102) idle
PCA-v3 (1 x 102) idle
Current compensation grade : 0
Range : 25800 ... 192599 = 42.956 ... 320.670 secs
Ready.
Adding average EEG reference projection.
1 projection items deactivated
To create time locked epochs, we first need a set of events that contain the information about the times. In this tutorial we use the stimulus channel to define the events. Let’s look at the raw data.
order = np.arange(raw.info['nchan'])
order[9] = 312 # We exchange the plotting order of two channels
order[312] = 9 # to show the trigger channel as the 10th channel.
raw.plot(n_channels=10, order=order, block=True)
Notice channel STI 014
at the bottom. It is the trigger channel that
was used for combining all the events to a single channel. We can see that it
has several pulses of different amplitude throughout the recording. These
pulses correspond to different stimuli presented to the subject during the
acquisition. The pulses have values of 1, 2, 3, 4, 5 and 32. These are the
events we are going to align the epochs to. To create an event list from raw
data, we simply call a function dedicated just for that. Since the event list
is simply a numpy array, you can also manually create one. If you create one
from an outside source (like a separate file of events), pay special
attention in aligning the events correctly with the raw data.
events = mne.find_events(raw)
print(events)
# Plot the events to get an idea of the paradigm
# Specify colors and an event_id dictionary for the legend.
event_id = {'Auditory/Left': 1, 'Auditory/Right': 2,
'Visual/Left': 3, 'Visual/Right': 4,
'smiley': 5, 'button': 32}
color = {1: 'green', 2: 'yellow', 3: 'red', 4: 'c', 5: 'black', 32: 'blue'}
mne.viz.plot_events(events, raw.info['sfreq'], raw.first_samp, color=color,
event_id=event_id)
Out:
320 events found
Events id: [ 1 2 3 4 5 32]
[[ 27977 0 2]
[ 28345 0 3]
[ 28771 0 1]
[ 29219 0 4]
[ 29652 0 2]
[ 30025 0 3]
[ 30450 0 1]
[ 30839 0 4]
[ 31240 0 2]
[ 31665 0 3]
[ 32101 0 1]
[ 32519 0 4]
[ 32935 0 2]
[ 33325 0 3]
[ 33712 0 1]
[ 34089 0 5]
[ 34532 0 2]
[ 34649 0 32]
[ 34956 0 3]
[ 35428 0 1]
[ 35850 0 4]
[ 36211 0 2]
[ 36576 0 3]
[ 37007 0 1]
[ 37460 0 4]
[ 37910 0 2]
[ 38326 0 3]
[ 38711 0 1]
[ 39130 0 4]
[ 39563 0 2]
[ 39926 0 3]
[ 40405 0 1]
[ 40880 0 4]
[ 41260 0 2]
[ 41646 0 3]
[ 42126 0 1]
[ 42598 0 5]
[ 42938 0 32]
[ 42960 0 2]
[ 43346 0 3]
[ 43781 0 1]
[ 44170 0 4]
[ 44649 0 2]
[ 45115 0 3]
[ 45486 0 1]
[ 45860 0 4]
[ 46240 0 2]
[ 46686 0 3]
[ 47095 0 1]
[ 47530 0 4]
[ 47895 0 2]
[ 48287 0 3]
[ 48722 0 1]
[ 49101 0 4]
[ 49469 0 2]
[ 49897 0 3]
[ 50269 0 1]
[ 50682 0 4]
[ 51046 0 2]
[ 51437 0 5]
[ 51803 0 32]
[ 51881 0 1]
[ 52252 0 4]
[ 52675 0 2]
[ 53118 0 3]
[ 53494 0 1]
[ 53912 0 4]
[ 54320 0 2]
[ 54699 0 3]
[ 55100 0 1]
[ 55463 0 4]
[ 55878 0 2]
[ 56269 0 3]
[ 56717 0 1]
[ 57183 0 4]
[ 57612 0 2]
[ 58079 0 3]
[ 58448 0 1]
[ 58913 0 4]
[ 59320 0 5]
[ 59677 0 32]
[ 59739 0 3]
[ 60129 0 1]
[ 60563 0 4]
[ 61035 0 2]
[ 61458 0 3]
[ 61868 0 1]
[ 62283 0 4]
[ 62682 0 2]
[ 63069 0 3]
[ 63532 0 1]
[ 63923 0 4]
[ 64387 0 2]
[ 64769 0 3]
[ 65181 0 1]
[ 65643 0 4]
[ 66071 0 2]
[ 66538 0 3]
[ 66964 0 1]
[ 67401 0 5]
[ 67782 0 32]
[ 67811 0 2]
[ 68198 0 3]
[ 68666 0 1]
[ 69063 0 4]
[ 69458 0 2]
[ 69859 0 3]
[ 70259 0 1]
[ 70663 0 4]
[ 71128 0 2]
[ 71608 0 3]
[ 71991 0 1]
[ 72442 0 4]
[ 72824 0 2]
[ 73258 0 3]
[ 73718 0 1]
[ 74172 0 4]
[ 74540 0 2]
[ 74919 0 3]
[ 75305 0 5]
[ 75624 0 32]
[ 75724 0 4]
[ 76086 0 2]
[ 76479 0 3]
[ 76894 0 1]
[ 77304 0 4]
[ 77682 0 2]
[ 78130 0 3]
[ 78539 0 1]
[ 78934 0 4]
[ 79361 0 2]
[ 79819 0 3]
[ 80209 0 1]
[ 80624 0 4]
[ 81017 0 2]
[ 81440 0 3]
[ 81911 0 1]
[ 82334 0 4]
[ 82715 0 2]
[ 83150 0 3]
[ 83568 0 1]
[ 83984 0 4]
[ 84446 0 2]
[ 84898 0 5]
[ 85111 0 32]
[ 85299 0 1]
[ 85664 0 4]
[ 86107 0 2]
[ 86520 0 3]
[ 86971 0 1]
[ 87354 0 4]
[ 87724 0 2]
[ 88150 0 3]
[ 88593 0 1]
[ 89024 0 4]
[ 89496 0 2]
[ 89949 0 3]
[ 90419 0 1]
[ 90813 0 4]
[ 91194 0 2]
[ 91600 0 3]
[ 92038 0 1]
[ 92414 0 4]
[ 92801 0 5]
[ 93018 0 32]
[ 93270 0 3]
[ 93669 0 1]
[ 94094 0 4]
[ 94541 0 2]
[ 94960 0 3]
[ 95435 0 1]
[ 95883 0 4]
[ 96329 0 2]
[ 96789 0 3]
[ 97234 0 1]
[ 97703 0 4]
[ 98113 0 2]
[ 98499 0 3]
[ 98903 0 1]
[ 99363 0 4]
[ 99795 0 2]
[100159 0 3]
[100620 0 1]
[101091 0 5]
[101468 0 2]
[101540 0 32]
[101908 0 3]
[102288 0 1]
[102713 0 4]
[103192 0 2]
[103588 0 3]
[104033 0 1]
[104433 0 4]
[104909 0 2]
[105348 0 3]
[105760 0 1]
[106172 0 4]
[106620 0 2]
[106988 0 3]
[107355 0 1]
[107743 0 4]
[108170 0 2]
[108619 0 3]
[108975 0 5]
[109324 0 32]
[109364 0 4]
[109741 0 2]
[110209 0 3]
[110574 0 1]
[111004 0 4]
[111440 0 2]
[111839 0 3]
[112292 0 1]
[112743 0 4]
[113140 0 2]
[113589 0 3]
[113952 0 1]
[114324 0 4]
[114740 0 2]
[115160 0 3]
[115619 0 1]
[116083 0 4]
[116527 0 2]
[116929 0 3]
[117304 0 1]
[117714 0 4]
[118101 0 5]
[118369 0 32]
[118480 0 3]
[118879 0 1]
[119235 0 4]
[119696 0 2]
[120110 0 3]
[120539 0 1]
[120984 0 4]
[121392 0 2]
[121800 0 3]
[122218 0 1]
[122684 0 4]
[123065 0 2]
[123451 0 3]
[123931 0 1]
[124404 0 4]
[124830 0 2]
[125300 0 3]
[125709 0 1]
[126123 0 5]
[126470 0 32]
[126546 0 2]
[126950 0 3]
[127331 0 1]
[127705 0 4]
[128075 0 2]
[128511 0 3]
[128925 0 1]
[129285 0 4]
[129732 0 2]
[130111 0 3]
[130495 0 1]
[130876 0 4]
[131253 0 2]
[131672 0 3]
[132081 0 1]
[132556 0 4]
[132974 0 2]
[133451 0 3]
[133927 0 5]
[134287 0 32]
[134316 0 4]
[134729 0 2]
[135101 0 3]
[135533 0 1]
[135916 0 4]
[136285 0 2]
[136722 0 3]
[137133 0 1]
[137576 0 4]
[137993 0 2]
[138412 0 3]
[138836 0 1]
[139306 0 4]
[139737 0 2]
[140191 0 3]
[140612 0 1]
[141085 0 4]
[141562 0 2]
[141981 0 3]
[142446 0 5]
[142815 0 32]
[142826 0 4]
[143218 0 2]
[143671 0 3]
[144071 0 1]
[144545 0 4]
[144916 0 2]
[145291 0 3]
[145666 0 1]
[146085 0 4]
[146545 0 2]
[147011 0 3]
[147458 0 1]
[147855 0 4]
[148304 0 2]
[148721 0 3]
[149162 0 1]
[149555 0 4]
[149989 0 2]
[150430 0 3]
[150872 0 1]
[151265 0 4]
[151626 0 2]
[152030 0 5]
[152336 0 32]
[152426 0 1]
[152825 0 4]
[153211 0 2]
[153681 0 3]
[154080 0 1]
[154486 0 4]
[168672 0 32]]
The event list contains three columns. The first column corresponds to sample number. To convert this to seconds, you should divide the sample number by the used sampling frequency. The second column is reserved for the old value of the trigger channel at the time of transition, but is currently not in use. The third column is the trigger id (amplitude of the pulse).
You might wonder why the samples don’t seem to align with the plotted data.
For instance, the first event has a sample number of 27977 which should
translate to roughly 46.6 seconds (27977 / 600). However looking at
the pulses we see the first pulse at 3.6 seconds. This is because Neuromag
recordings have an attribute first_samp
which refers to the offset
between the system start and the start of the recording. Our data has a
first_samp
equal to 25800. This means that the first sample you see with
raw.plot
is the sample number 25800. Generally you don’t need to worry
about this offset as it is taken into account with MNE functions, but it is
good to be aware of. Just to confirm, let’s plot the events together with the
raw data. Notice how the vertical lines (events) align nicely with the pulses
on STI 014.
raw.plot(events=events, n_channels=10, order=order)
In this tutorial we are only interested in triggers 1, 2, 3 and 4. These
triggers correspond to auditory and visual stimuli. The event_id
here
can be an int, a list of ints or a dict. With dicts it is possible to assign
these ids to distinct categories. When using ints or lists this information
is lost. First we shall define some parameters to feed to the
mne.Epochs
constructor. The values tmin
and tmax
refer to
offsets in relation to the events. Here we make epochs that collect the data
from 200 ms before to 500 ms after the event.
tmin, tmax = -0.2, 0.5
event_id = {'Auditory/Left': 1, 'Auditory/Right': 2,
'Visual/Left': 3, 'Visual/Right': 4}
# Only pick MEG and EOG channels.
picks = mne.pick_types(raw.info, meg=True, eeg=False, eog=True)
Now we have everything we need to construct the epochs. To get some
meaningful results, we also want to baseline the epochs. Baselining computes
the mean over the baseline period and adjusts the data accordingly. The
epochs constructor uses a baseline period from tmin
to 0.0 seconds by
default, but it is wise to be explicit. That way you are less likely to end
up with surprises along the way. None
as the first element of the tuple
refers to the start of the time window (-200 ms in this case).
See mne.Epochs
for more.
We also define rejection thresholds to get rid of noisy epochs. The rejection thresholds are defined as peak-to-peak values within the epoch time window. They are defined as T/m for gradiometers, T for magnetometers and V for EEG and EOG electrodes.
Note
In this tutorial, we don’t preprocess the data. This is not something you would normally do. See our Tutorials on preprocessing for more.
baseline = (None, 0.0)
reject = {'mag': 4e-12, 'eog': 200e-6}
epochs = mne.Epochs(raw, events=events, event_id=event_id, tmin=tmin,
tmax=tmax, reject=reject, picks=picks)
Out:
289 matching events found
Applying baseline correction (mode: mean)
Created an SSP operator (subspace dimension = 3)
4 projection items activated
Let’s plot the epochs to see the results. The number at the top refers to the id number. We can see that 128 good epochs out of total of 145 events got through the rejection process. Visual inspection also reveals that some epochs containing saccades or blinks got through. You can also reject epochs by hand by clicking on the epoch in the browser window. The selected epochs get rejected when you close the epochs browser. How you should reject the epochs and which thresholds to use is not a trivial question and this tutorial takes no stand on that matter.
To see all the interactive features of the epochs browser, click ‘Help’ in the lower left corner of the browser window.
epochs.plot(block=True)
Out:
Loading data for 289 events and 421 original time points ...
Rejecting epoch based on EOG : [u'EOG 061']
Rejecting epoch based on EOG : [u'EOG 061']
Rejecting epoch based on EOG : [u'EOG 061']
Rejecting epoch based on EOG : [u'EOG 061']
Rejecting epoch based on EOG : [u'EOG 061']
Rejecting epoch based on EOG : [u'EOG 061']
Rejecting epoch based on EOG : [u'EOG 061']
Rejecting epoch based on EOG : [u'EOG 061']
Rejecting epoch based on MAG : [u'MEG 1711']
Rejecting epoch based on EOG : [u'EOG 061']
Rejecting epoch based on EOG : [u'EOG 061']
Rejecting epoch based on EOG : [u'EOG 061']
Rejecting epoch based on MAG : [u'MEG 1711']
Rejecting epoch based on EOG : [u'EOG 061']
Rejecting epoch based on EOG : [u'EOG 061']
Rejecting epoch based on EOG : [u'EOG 061']
Rejecting epoch based on EOG : [u'EOG 061']
Rejecting epoch based on EOG : [u'EOG 061']
Rejecting epoch based on EOG : [u'EOG 061']
Rejecting epoch based on EOG : [u'EOG 061']
Rejecting epoch based on EOG : [u'EOG 061']
Rejecting epoch based on EOG : [u'EOG 061']
Rejecting epoch based on EOG : [u'EOG 061']
Rejecting epoch based on EOG : [u'EOG 061']
Rejecting epoch based on EOG : [u'EOG 061']
Rejecting epoch based on EOG : [u'EOG 061']
Rejecting epoch based on EOG : [u'EOG 061']
Rejecting epoch based on EOG : [u'EOG 061']
28 bad epochs dropped
Loading data for 20 events and 421 original time points ...
To see why the epochs were rejected, we can plot the drop log.
epochs.plot_drop_log()
To get the evoked response you can simply do epochs.average()
. It
includes only the data channels by default. For the sake of example, we use
picks to include the EOG channels as well. Notice that we cannot use the
same picks as before as the indices are different. ‘Why are they different?’
you might ask. They’re different because picks
is simply a list of
channel indices and as the epochs were constructed, also a new info structure
is created where the channel indices run from 0 to epochs.info['nchan']
.
See The Info data structure for more information.
picks = mne.pick_types(epochs.info, meg=True, eog=True)
evoked_left = epochs['Auditory/Left'].average(picks=picks)
evoked_right = epochs['Auditory/Right'].average(picks=picks)
Notice we have used forward slashes (‘/’) to separate the factors of the conditions of the experiment. We can use these ‘tags’ to select for example all left trials (both visual left and auditory right) ...
epochs_left = epochs['Left']
# ... or to select a very specific subset. This is the same as above:
evoked_left = epochs['Auditory', 'Left'].average(picks=picks)
Finally, let’s plot the evoked responses.
evoked_left.plot()
evoked_right.plot()
Total running time of the script: (0 minutes 16.831 seconds)
plot_epoching_and_averaging.py
plot_epoching_and_averaging.ipynb


Supply-side Economics in One Lesson: What the Critics Don’t Tell You” by Gary M. Galles, The Freeman, July 25, 2013
Raymond J. Keating, "A Walk on the Supply-Side," The Freeman, June 1995


横
- 3: sitting; stability
- 5: just (e.g. "just wait here"); come now; now, now; tolerably; passably; moderately; reasonably; fairly; rather; somewhat; well...; I think...; it would seem...; you might say...; Hmmm, I guess so...; oh!; oh dear!; oh, my!; wow!; goodness gracious!; good heavens!
| 縦
- 1: fusuma; Japanese sliding screen
- 2: style; manner; way; after (period of time) again (e.g. meeting again after a year); for the first time in (period of time)
- 4: wow!; oh!; eek!; gee!; yeah!; alright!; hurray!; waah!; boohoo!
|


REWIND: A trip down the memory lane...
Are you a frequent GPS user?
Yes
No
How much do you travel?
I travel quite a lot. Many times within a year.
More than a couple of times per year.
About once or a couple of times per year.
I don't travel much.
Start


Basic menu with different display types and icons
Menu items variations
Menu with multiselection
Menu item with checkbox
Second item
Third menu item
Fourth item
First from second group
Another menu item
Menu item with checkbox
Browse all properties...
Destructive action


Assignment: complete your final project, tracking your progress:
project_development/index.htmlTasks completed: 3D modelling of the layers, laser cutting of mechanical components, writing of Arduino sketch, designing of the FabKit, testing output device (a servomotor).
Tasks remaining: Assembling mechanical components, testing electronic components and Arduino sketch with the final prototype, testing input device (light sensor).
For the first prototype, I had chosen to use a toothed belt to move pulleys. However, I realized that using gears could reduce friction and could make the movement smoother. So, for the final project I chose to use gears. I had chosen a Wiper motor to move the mechanism, but it was too massive and too powerful. Therefore I chose a servomotor, which is perfect for my needs. I found a servomotors with electronics damaged, but with motor and gears fully functional.
I did not have available black plexiglass, so for the gears I had to use Multiexel. This material, however, is too porous and creates a lot of friction. It makes the panel very noisy and “halting”.
I have to choose whether to use a photoresistor or a phototransistor. A photoresistor is simply a resistor whose resistance depends on the amount of light incident upon it. Its resistance varies according to the intensity of light, it has no gain and it does not need power to operate. Instead, phototransistor has gain, It is more sensitive than photoresistor, but it requires power to operate. It all depends on the speed of the signal you are using.
Now, I have to put together mechanical and electronic conponents, and I have to test input and output devices. But above all, I have to choose between photoresistor and phototransistor. My goal is to have one single module assembled and working at the end of the course.
It was really hard but very funny. I learned a lot about electronics, programming and mechanics. I figured out how to apply all this stuff to my interests: architecture and design. It is exciting make an interactive object from scratch. I am not really well prepared, but I will continue to train myself further more in electronics and programming after I have completed the academy.
You can find all documentation in my final project pages
My goal was to have a functioning panel at the end of the course. I wanted my panel opening and closing, so I focused on the mechanical and electronic components. Actually, I wanted to include a larger number of sensors, but it would take a lot of time. So, I finally preferred to focus only on the light sensor, in order to make the panel simpler. On the other hand, the panel could be used especially in museums to protect works of art, and light is one of the most important variables in this field. In any case, later I will add other sensors to the board.


|
My Project
|
EK_TM4C1294XL Board Specific APIs. Ayrıntılar...
Bu dosyanın kaynak koduna git.
Makrolar | |
| #define | EK_TM4C1294XL_LED_OFF (0) |
| #define | EK_TM4C1294XL_LED_ON (1) |
Typedef'ler | |
| typedef enum EK_TM4C1294XL_EMACName | EK_TM4C1294XL_EMACName |
| typedef enum EK_TM4C1294XL_GPIOName | EK_TM4C1294XL_GPIOName |
| typedef enum EK_TM4C1294XL_I2CName | EK_TM4C1294XL_I2CName |
| typedef enum EK_TM4C1294XL_PWMName | EK_TM4C1294XL_PWMName |
| typedef enum EK_TM4C1294XL_SDSPIName | EK_TM4C1294XL_SDSPIName |
| typedef enum EK_TM4C1294XL_SPIName | EK_TM4C1294XL_SPIName |
| typedef enum EK_TM4C1294XL_UARTName | EK_TM4C1294XL_UARTName |
| typedef enum EK_TM4C1294XL_USBMode | EK_TM4C1294XL_USBMode |
| typedef enum EK_TM4C1294XL_USBMSCHFatFsName | EK_TM4C1294XL_USBMSCHFatFsName |
| typedef enum EK_TM4C1294XL_WatchdogName | EK_TM4C1294XL_WatchdogName |
| typedef enum EK_TM4C1294XL_WiFiName | EK_TM4C1294XL_WiFiName |
Fonksiyonlar | |
| void | EK_TM4C1294XL_initGeneral (void) |
| Initialize the general board specific settings. Ayrıntılar... | |
| void | EK_TM4C1294XL_initEMAC (void) |
| Initialize board specific EMAC settings. Ayrıntılar... | |
| void | EK_TM4C1294XL_initGPIO (void) |
| Initialize board specific GPIO settings. Ayrıntılar... | |
| void | EK_TM4C1294XL_initI2C (void) |
| Initialize board specific I2C settings. Ayrıntılar... | |
| void | EK_TM4C1294XL_initPWM (void) |
| Initialize board specific PWM settings. Ayrıntılar... | |
| void | EK_TM4C1294XL_initSDSPI (void) |
| Initialize board specific SDSPI settings. Ayrıntılar... | |
| void | EK_TM4C1294XL_initSPI (void) |
| Initialize board specific SPI settings. Ayrıntılar... | |
| void | EK_TM4C1294XL_initUART (void) |
| Initialize board specific UART settings. Ayrıntılar... | |
| void | EK_TM4C1294XL_initUSB (EK_TM4C1294XL_USBMode usbMode) |
| Initialize board specific USB settings. Ayrıntılar... | |
| void | EK_TM4C1294XL_initUSBMSCHFatFs (void) |
| Initialize board specific USBMSCHFatFs settings. Ayrıntılar... | |
| void | EK_TM4C1294XL_initWatchdog (void) |
| Initialize board specific Watchdog settings. Ayrıntılar... | |
| void | EK_TM4C1294XL_initWiFi (void) |
| Initialize board specific WiFi settings. Ayrıntılar... | |
EK_TM4C1294XL Board Specific APIs.
============================================================================
The EK_TM4C1294XL header file should be included in an application as follows:
| #define EK_TM4C1294XL_LED_OFF (0) |
| #define EK_TM4C1294XL_LED_ON (1) |
| typedef enum EK_TM4C1294XL_EMACName EK_TM4C1294XL_EMACName |
| typedef enum EK_TM4C1294XL_GPIOName EK_TM4C1294XL_GPIOName |
| typedef enum EK_TM4C1294XL_I2CName EK_TM4C1294XL_I2CName |
| typedef enum EK_TM4C1294XL_PWMName EK_TM4C1294XL_PWMName |
| typedef enum EK_TM4C1294XL_SDSPIName EK_TM4C1294XL_SDSPIName |
| typedef enum EK_TM4C1294XL_SPIName EK_TM4C1294XL_SPIName |
| typedef enum EK_TM4C1294XL_UARTName EK_TM4C1294XL_UARTName |
| typedef enum EK_TM4C1294XL_USBMode EK_TM4C1294XL_USBMode |
| typedef enum EK_TM4C1294XL_WatchdogName EK_TM4C1294XL_WatchdogName |
| typedef enum EK_TM4C1294XL_WiFiName EK_TM4C1294XL_WiFiName |
| void EK_TM4C1294XL_initEMAC | ( | void | ) |
Initialize board specific EMAC settings.
This function initializes the board specific EMAC settings and then calls the EMAC_init API to initialize the EMAC module.
The EMAC address is programmed as part of this call.
| void EK_TM4C1294XL_initGeneral | ( | void | ) |
Initialize the general board specific settings.
This function initializes the general board specific settings. This includes:
| void EK_TM4C1294XL_initGPIO | ( | void | ) |
Initialize board specific GPIO settings.
This function initializes the board specific GPIO settings and then calls the GPIO_init API to initialize the GPIO module.
The GPIOs controlled by the GPIO module are determined by the GPIO_PinConfig variable.
| void EK_TM4C1294XL_initI2C | ( | void | ) |
Initialize board specific I2C settings.
This function initializes the board specific I2C settings and then calls the I2C_init API to initialize the I2C module.
The I2C peripherals controlled by the I2C module are determined by the I2C_config variable.
| void EK_TM4C1294XL_initPWM | ( | void | ) |
Initialize board specific PWM settings.
This function initializes the board specific PWM settings and then calls the PWM_init API to initialize the PWM module.
The PWM peripherals controlled by the PWM module are determined by the PWM_config variable.
| void EK_TM4C1294XL_initSDSPI | ( | void | ) |
Initialize board specific SDSPI settings.
This function initializes the board specific SDSPI settings and then calls the SDSPI_init API to initialize the SDSPI module.
The SDSPI peripherals controlled by the SDSPI module are determined by the SDSPI_config variable.
| void EK_TM4C1294XL_initSPI | ( | void | ) |
Initialize board specific SPI settings.
This function initializes the board specific SPI settings and then calls the SPI_init API to initialize the SPI module.
The SPI peripherals controlled by the SPI module are determined by the SPI_config variable.
| void EK_TM4C1294XL_initUART | ( | void | ) |
Initialize board specific UART settings.
This function initializes the board specific UART settings and then calls the UART_init API to initialize the UART module.
The UART peripherals controlled by the UART module are determined by the UART_config variable.
| void EK_TM4C1294XL_initUSB | ( | EK_TM4C1294XL_USBMode | usbMode | ) |
Initialize board specific USB settings.
This function initializes the board specific USB settings and pins based on the USB mode of operation.
| usbMode | USB mode of operation |
| void EK_TM4C1294XL_initUSBMSCHFatFs | ( | void | ) |
Initialize board specific USBMSCHFatFs settings.
This function initializes the board specific USBMSCHFatFs settings and then calls the USBMSCHFatFs_init API to initialize the USBMSCHFatFs module.
The USBMSCHFatFs peripherals controlled by the USBMSCHFatFs module are determined by the USBMSCHFatFs_config variable.
| void EK_TM4C1294XL_initWatchdog | ( | void | ) |
Initialize board specific Watchdog settings.
This function initializes the board specific Watchdog settings and then calls the Watchdog_init API to initialize the Watchdog module.
The Watchdog peripherals controlled by the Watchdog module are determined by the Watchdog_config variable.
| void EK_TM4C1294XL_initWiFi | ( | void | ) |
Initialize board specific WiFi settings.
This function initializes the board specific WiFi settings and then calls the WiFi_init API to initialize the WiFi module.
The hardware resources controlled by the WiFi module are determined by the WiFi_config variable.
A SimpleLink CC3100 device or module is required and must be connected to use the WiFi driver.


§48. Signing bills. That, except as herein provided, all bills passed by the legislature shall, in order to be valid, be signed by the governor.
Referred to in 16 H. 245, 253.


Uses of Class
org.drip.execution.dynamics.LinearPermanentExpectationParameters
| Package | Description |
|---|---|
| org.drip.execution.athl |
Almgren-Thum-Hauptmann-Li Calibration
|
| org.drip.execution.dynamics |
Arithmetic Price Evolution Execution Parameters
|
| org.drip.execution.nonadaptive |
Almgren-Chriss Static Optimal Trajectory
|
| org.drip.execution.principal |
Information Ratio Based Principal Trades
|
-
Uses of LinearPermanentExpectationParameters in org.drip.execution.athl
Methods in org.drip.execution.athl that return LinearPermanentExpectationParameters Modifier and Type Method Description LinearPermanentExpectationParameters
DynamicsParameters. almgren2003()
Generate an Instance of the Almgren 2003 Dynamics Parameters -
Uses of LinearPermanentExpectationParameters in org.drip.execution.dynamics
Methods in org.drip.execution.dynamics that return LinearPermanentExpectationParameters Modifier and Type Method Description static LinearPermanentExpectationParameters
ArithmeticPriceEvolutionParametersBuilder. Almgren2003(ArithmeticPriceDynamicsSettings apds, BackgroundParticipationRateLinear bprlPermanentExpectation, BackgroundParticipationRate bprTemporaryExpectation)
Almgren 2003 Version of LinearPermanentExpectationParameters Instancestatic LinearPermanentExpectationParameters
ArithmeticPriceEvolutionParametersBuilder. LinearExpectation(ArithmeticPriceDynamicsSettings apds, BackgroundParticipationRateLinear bprlPermanentExpectation, BackgroundParticipationRateLinear bprlTemporaryExpectation)
Linear Expectation Version of LinearPermanentExpectationParameters Instancestatic LinearPermanentExpectationParameters
ArithmeticPriceEvolutionParametersBuilder. ReferenceCoordinatedVariation(CoordinatedVariation cv)
Construct a Linear Permanent Evolution Parameters from a Deterministic Coordinated Variation Instance -
Uses of LinearPermanentExpectationParameters in org.drip.execution.nonadaptive
Methods in org.drip.execution.nonadaptive with parameters of type LinearPermanentExpectationParameters Modifier and Type Method Description static ContinuousAlmgrenChriss
ContinuousAlmgrenChriss. Standard(double dblStartHoldings, double dblFinishTime, LinearPermanentExpectationParameters lpep, double dblRiskAversion)
Create the Standard ContinuousAlmgrenChriss Instancestatic ContinuousHighUrgencyAsymptote
ContinuousHighUrgencyAsymptote. Standard(double dblStartHoldings, double dblFinishTime, LinearPermanentExpectationParameters lpep, double dblRiskAversion)
Create the Standard ContinuousHighUrgencyAsymptote Instancestatic ContinuousLowUrgencyAsymptote
ContinuousLowUrgencyAsymptote. Standard(double dblStartHoldings, double dblFinishTime, LinearPermanentExpectationParameters lpep, double dblRiskAversion)
Create the Standard ContinuousLowUrgencyAsymptote Instancestatic ContinuousPowerImpact
ContinuousPowerImpact. Standard(double dblStartHoldings, double dblFinishTime, LinearPermanentExpectationParameters lpep, double dblRiskAversion)
Create the Standard ContinuousPowerImpact Instancestatic DiscreteAlmgrenChriss
DiscreteAlmgrenChriss. Standard(double dblStartHoldings, double dblFinishTime, int iNumInterval, LinearPermanentExpectationParameters lpep, double dblRiskAversion)
Create the Standard DiscreteAlmgrenChriss Instancestatic DiscreteAlmgrenChrissDrift
DiscreteAlmgrenChrissDrift. Standard(double dblStartHoldings, double dblFinishTime, int iNumInterval, LinearPermanentExpectationParameters lpep, double dblRiskAversion)
Create the Standard DiscreteAlmgrenChrissDrift InstanceConstructors in org.drip.execution.nonadaptive with parameters of type LinearPermanentExpectationParameters Constructor Description ContinuousAlmgrenChriss(OrderSpecification os, LinearPermanentExpectationParameters lpep, MeanVarianceObjectiveUtility mvou)
ContinuousAlmgrenChriss Constructor -
Uses of LinearPermanentExpectationParameters in org.drip.execution.principal
Constructors in org.drip.execution.principal with parameters of type LinearPermanentExpectationParameters Constructor Description Almgren2003Estimator(PowerImpactContinuous pic, LinearPermanentExpectationParameters lpep)
Almgren2003Estimator Constructor


nginx-training-01.local
this is http://nginx-training-01.local
served on nginx container.
test of multiple basic authentication
The following directories require different basic authentication.
- http://nginx-training-01.local:18080/user001
- ID: user001 / password: USER001
- http://nginx-training-01.local:18080/user002
- ID: user002 / password: USER002
- http://nginx-training-01.local:18080/user003
- ID: user003 / password: USER003


Preparation time
less than 30 mins
Cooking time
30 mins to 1 hour
Serves
Serves about 8
Dietary
Beetroot cake is the hero of vegetable-based cakes – moist, tasty and full of goodness. Nigel Slater shows you how.
Preheat the oven at 180C/350F/Gas 4. Lightly grease a rectangular loaf tin (20cm x 9cm x 7cm/8in x 4in x 3in), then line the base with baking parchment.
Sift together the flour, bicarbonate of soda, baking powder and cinnamon.
Beat the oil and sugar in a food mixer until well combined, then introduce the egg yolks one by one, mixing after you add each egg yolk. Grate the beetroot coarsely and fold it into the egg mixture, then add the lemon juice, sultanas (or raisins) and the assorted seeds. Pulse until combined.
Fold the flour and raising agents into the egg mixture whilst the machine is on a slow setting.
Beat the egg whites until light and almost stiff. Fold gently but thoroughly into the cake mixture, using a large metal spoon (a wooden one will knock the air out). Pour the mixture into the cake tin and bake for 50-55 minutes, covering the top with a piece of foil after thirty minutes. Test with a skewer for doneness. The cake should be moist inside but not sticky. Leave the cake to settle for a good twenty minutes before turning out of its tin onto a wire cooling rack.
To make the icing, sift the icing sugar into a bowl and stir in enough lemon juice or orange blossom water to achieve a consistency where the icing will run over the top of the cake and drizzle slowly down the sides (about three teaspoonfuls), stirring to remove any lumps. Drizzle it over the cake and scatter with the poppy seeds. Leave to set before eating.


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About
A completely free and open source project initiated by Michal Paszkiewicz.It has only been possible to run this project thanks to kind donations. Any amount will help us provide a better service for you.
Plugin manager
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Android , IOS ,Windows Phone ,Android Gear
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Computer Engineer
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java.lang.Object dxf2svg.sally.SymbolTable
This class represents the symbol table within the Svg Animation Language's. A symbol table is a binary tree that contains all the defined variables currently available in this instance of SALly.
| Constructor Summary | |
SymbolTable()
|
| Method Summary | |
Symbol |
getSymbol(java.lang.String name)
Searches Symbol table for the argument value. |
java.lang.Object |
getSymbolValue(java.lang.String name)
Retrieves the value of the desired symbol, and returns it. |
void |
insert(Symbol s)
Inserts a new Symbol object into the symbol table. |
java.util.Vector |
list()
Returns a vector that contains all the names of the variables currently stored in the symboltable. |
void |
printTable()
Prints the Symbol table with dots to represent the level of the tree that the symbol was found. |
int |
size()
Returns the total number of nodes in the binary tree. |
java.lang.String |
toString()
Prints the symbol table and associated classes version information. |
| Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait |
| Constructor Detail |
public SymbolTable()
| Method Detail |
public void insert(Symbol s)
public void printTable()
public java.lang.Object getSymbolValue(java.lang.String name)
public Symbol getSymbol(java.lang.String name)
public java.util.Vector list()
public int size()
public java.lang.String toString()
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Wave 1
A work-in-progress library of responsive music simulations for children with hearing impairments or on the autism/ADHD spectrum.
This is an audio equalizer, and it's generally used for seeing the balance between frequencies in electronic signals. You can customize its appearance with a number of modifiers, such relating its α to amplitude, or linear interpolation strength to tempo.


General examples¶
General-purpose and introductory examples for scikit-image.
The narrative documentation introduces conventions and basic image manipulations.
Examples for developers¶
In this folder, we have examples for advanced topics, including detailed explanations of the inner workings of certain algorithms.
These examples require some basic knowledge of image processing. They are targeted at existing or would-be scikit-image developers wishing to develop their knowledge of image processing algorithms.


Title:
{{newTitle}}
Full Name:
{{newFullName}}
Dollar Amount:
{{newDollarAmount}}
Description:
{{newDescription}}
↓ Add to List
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Id: {{contact.id}}
Title: {{contact.title}}
Name: {{contact.fullName}}
${{contact.dollarAmount}}
{{contact.description}}
Delete


This tool is intended to help match up the font descriptor for your fallback font to your primary font to minimize the Content Layout Shift (CLS). The paragraph below is the text for comparison, use the form below it to enter your fonts and adjust the CSS descriptors.
Bilged on her anchor Jack Ketch Arr galleon heave to pink sloop haul wind Sink me weigh anchor. Scallywag ye rutters matey pirate starboard ahoy aft mutiny American Main.
Bilged on her anchor Jack Ketch Arr galleon heave to pink sloop haul wind Sink me weigh anchor. Scallywag ye rutters matey pirate starboard ahoy aft mutiny American Main.


Charles is your new lightweight map, quick of access and easy to use, it diplays the transports, traffic and satelite view.
Developed by DesireYavro
* Irresponsible use of this geolocation service is punishable under federal law.


Some time ago I took up a new, quite inglorious hobby – dumpster raiding. Where I live right now people throw away a lot of electronics and some of it is still fairly functional. Of course, many of the items are damaged beyond repair and I don't want to invest in dying tech without good reasons. Regardless, from time to time I find real treasure. For instance, roughly a month ago I found a complete PC tower. I was shocked, because everything was inside – RAM, a hard drive (HDD), a graphics card, etc. All I needed to do was get a screen, a keyboard, a mouse and some cables.
The PC had an 80 GB HDD, 4 x 256 MB RAM and a Pentium IV processor. I installed the i386 version of Debian on it and it was super fast! I chose a fairly light window manager, IceWM, so the base memory footprint was around 100 MB. Thanks to that I had plenty of RAM for daily tasks, which by the by felt like a breeze thanks to Intel's hyper-threading (HT). Then, I realized Pentium IV is actually quite ancient. It's mid 2015 and one can already buy PCs with several CPU cores, tens of GB RAM, 6+ TB hard drives and what not. However, does one really need hardware that powerful?
 Fair point. Remember though, I was born in the 8-bit era and began my computing adventures in the 16-bit era. To me, Pentium IV was a beast of a processor. A monumental achievement in hardware development.
What I realized was that until a certain point in time, computers were getting noticeably better. My Matrox 3D graphics card would be too weak for more modern games, the hard drive was full to the point that I had to remove unused programs, the system would swap due to lack of RAM, and so on. But then came Pentium IV and with 4 GB RAM it was just enough. Disk space was becoming relatively cheap as well. For daily tasks like browsing the Web, doing homework, etc. this setup was fully sufficient.
Of course, comparing computer specs by sheer numbers is like comparing apples to oranges. 4 GB RAM nowadays is way faster than 4 GB RAM back in the Pentium IV days. Especially, considering that now we have DDR3 RAM and back then it was DDR2 or the slower DDR. However, I feel computer retailers are sometimes doing a disfavor to customers by pushing overpowered gaming hardware to unsuspecting casual shoppers.  Also, with our new core i7-8000K Ultra Hyper Book X you will be able to browse the Web a thousand times faster! I'm sure that's absolutely true, especially f the website is hosted on a tiny underpowered SPARC server from the early 1990s.
Every year we produce more and more electronic garbage. We generate more than we actually need. Ha, more than we'll ever use. And this is exactly where Linux comes in. Linux can turn this garbage into treasure!


Playing Around With Guilloche Patterns
By drawing 16 curves each offset by a phase angle of PI / 8, we finally get a proper rosette:
There's nothing magic about 16, except that PI / 8 is a 1/16th of a circle, so 20 curves offset by PI / 10, or 30 curves offset by PI / 15 would work as well. In general: N curves offset by 1/Nth of a circle.
See: guilloche13.pde


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