ContractCallOperationBuilder()
Represents builder for ContractCreateOperation. Checks required data.
Author Daria Pechkovskaya


| HYTOOL Function Reference |
Draw the slug test type curves of Cooper et al. (1967)
csl_drw()
Draw the type curves of Cooper et al. (1967) Reference: Cooper, H.H.J., J.D. Bredehoeft, and I.S. Papadopulos. 1967. Response of a finite-diameter well to an instantaneous charge of water. Water Resources Research 3, no. 1: 263-269.
csl_dim, csl_dls Published output in the Help browser showdemo csl_drw
| Hytool |
© 1998-2017 Philppe Renard and Co. Terms of Use Acknowledgments


Suriya turns rapper for ‘Soorarai Pottru’
The film based on Air Deccan founder GR Gopinath, has music composed by G.V. Prakash
by Special CorrespondentSuriya, whose last outings on the big screen was Selvaraghavan’s NGK and KV Anand’s Kaappaan, is currently busy with Soorarai Pottru, directed by Sudha Kongara. The latest is that the actor has rapped the ‘Maara Theme’ in the film, composed by music director GV Prakash. The composer tweeted, “Mr.Maara raps ... #maaratheme will be rapped by @Suriya_offl sir #sooraraipottru #sudha #arivu.”
Soorarai Pottru is said to be a film based on Air Deccan founder GR Gopinath. The biographical film, jointly produced by Suriya’s 2D Entertainment and Guneet Monga, boasts a stellar cast including Aparna Balamurali, Jackie Shroff, Karunas, Mohan Babu and Paresh Rawal in prominent roles.
Singing is relatively new for Suriya: he has sung only a couple of times before. The actor lent his voice to a coffee advertisement jingle a few years ago and also sung the Yuvan Shankar Raja-composed Ek Do Teen number in Anjaan.
Actors of Tamil cinema have often sung in their projects, in order to provide a novelty factor to the album. Actors Vijay and Dhanush are regular singers these days – the former’s last song was Verithanam from Bigil.


Initiate | Invent | Inspire
The ChalkBoard was started with an aim to provide quality education in the best possible environment for children. We focus on making learning fun where children can gain knowledge by practical and experimental approach to education. Although we are a new establishment, we are determined to provide nothing but the best to the students.
Under the able guidance of esteemed and qualified founders and staff members the students are guaranteed to bloom and prosper
We at The ChalkBoard believe that each child is born with a potential to make the world a better, brighter place and to prosper with it. We understand that the preparatory age is extremely crucial as this is the time when the foundation is laid, where a child acquires traits and skills to achieve his/her goals. Children are like a clean chalk-board and only the right form of education can help them create an environment that is beneficial to them as well as to the entire community at large.
Our focus revolves around the fact that education isn’t limited just to academics. It needs to be holistic in nature. This is why we relentlessly try to incorporate a well-researched curriculum that will help with the multi-faceted development of children.
While academics is an integral part of the growing years, it is equally important to have a well-rounded personality that will help children become kind, caring and more responsible towards the world around them.


The industrial-grade, Category 5e TOUGHCable™ protects your networks from the most brutal environments.
TOUGHCable™ dramatically increases your Ethernet link states, speeds and overall performance.
TOUGHCable™ has been built to perform in even the harshest weather and environments.
TOUGHCable™ protects your networks from devastating electrostatic discharge attacks.
TOUGHCable™ has been developed to increase power handling performance for extended cable run lengths.
Specifically designed for use with TOUGHCable™, the TOUGHCable Connectors protect against ESD attacks and Ethernet hardware damage, while allowing rapid field deployment without soldering.
|
TC-PRO
|
TC-CARRIER
|
|
|---|---|---|
| Cable Distance | 5e | 5e |
| Anti-Crosstalk Divider | No | Yes |
| Braid Shield | No | Yes |
| Foil Shield | Yes | Yes |
|
TC-CON-100
|
TC-GND
|
|
|---|---|---|
| Ground Wire | No | Yes |
| Quantity | 100 Pieces per Box | 20 Pieces per Box |


Learners
Describe the grade level and course that the lesson is designed to cover. For example: "This lesson is anchored in seventh grade language arts and involves social studies and math to a lesser extent." If the lesson can easily be extended to additional grades and subjects, mention that briefly here as well.
Describe what the learners will need to know prior to beginning this lesson. Limit this description to the most critical skills that could not be picked up on the fly as the lesson is given.


For Freshmen Council President
Welcome to the campaign website for Cavan Meade! Thanks for stopping by, I'm glad you're here.
If I had the honor of your vote, this would not be my first leadership experience. From Elementary school through middle school, I stayed committed to my work while helping others and leading by example. I was a part of many clubs through middle school, ranging from the morning announcements to NJHS, where I have learned how to work as a team member and leader.
I have lived in Charlotte my whole life. I have grown to love and appreciate this wonderful community, and thus have been dedicated my whole life to improving it the most I can. As an elected representative, I would continue to work towards the benefit of the community.
As a student in Tae Kwon Do, I am committed to doing my best in every aspect of my work. Whether it be physical, in my training, or mental, in my academics, I never quit on my goals.
Hi. My name is Cavan Meade, and I attend South Mecklenburg High School, and I'm running for Freshmen Council.
But who am I beneath that line?
Outside of school, I stay active with Tae Kwon Do. As a black belt, it is my job to help and (hopefully) inspire younger ranks. I help judge competitions, and work with the younger students to improve their skills as martial artists. I attend Tae Kwon Do multiple times a week, showing my constant dedication to this leadership role.
I also participate in Musical Theatre. This is something I have been doing my whole life, and I enjoy both working as a cast member, and as a director. I enjoy performing, but my favorite part of theatre – what draws me to it – is the people. From the shows I have performed in, I have learned the most valuable part of success is the community – the cast. This is something I apply to my everyday life, striving to build a public sphere of support, to create that “cast” of people, who together can make the community - the show - and our school a better place.
I have an older brother (pictured behind) who has led me throughout my life. That said, I have led him too. As the younger one, I often felt overshadowed by my brother’s successes. I felt I needed to prove to everyone that I was just as good. However, through the years I came to learn that I didn’t need to be like him, I needed to be myself. And that is who I am. I am not afraid to accept challenges that seem beyond me, and I work everyday to show this. Despite being the younger brother, I helped my sibling when he needed it, trying to look at whatever problem through a different lens, my lens. I hope, with your support, that I can be the lens this school needs.
During my time at Carmel Middle School, I, along with some of my classmates, began the Student Betterment Council, a group of students dedicated to helping improve the school for the students. As a founder of this council, I both helped and led my classmates in and with our endeavors to improve the school. I used my connections with teachers and other students to help realize our goals - the student body's goals.
Along with SBC, I was a part of the Yearbook Staff. I worked on both pictures and design, while making sure to get student input. This dedication to creating a yearbook for the students, by the students helped to shape me as a leader and as a good team member.
As both a black belt and a member of the National Junior Honors Society, I helped with many community projects, obtaining many service hours through these. However, it was never about the number of hours, but instead my wish to better the community, which I did through leading different areas of each service project. Every year I participate in a service project with my family to clean-up schools around Charlotte. Ever since the first year I did it, I have always asked my family to go each year, as it is both fulfilling and enjoyable. With your vote, I hope to bring this joy and feeling of commitment to South Mecklenburg High School.


| LIN | NSN | Item Name | PBIC | Type | DND | Qnty |
|---|---|---|---|---|---|---|
| WARLOCK SSVJ, ECM: SSVJ TMC DESI | V | TPE | N | 1 | ||
| CELL PHONE | 9 | INS | N | 1 | ||
| CONVERTER,POWER | 9 | INS | N | 1 | ||
| OVEN,MICROWAVE-(1200 WATTS ) | 9 | INS | N | 9 | ||
| PROJ SCREEN | 9 | INS | N | 1 | ||
| PROJECTOR MULTIMEDIA COMPLETE PR | 9 | INS | N | 1 | ||
| LIGHT SET W/ GENERATR | 9 | INS | N | 1 | ||
| CHARGER,BATTERY-(PP4127AU) | 9 | INS | N | 3 | ||
| ANTENNA, BASE RADIO STATION/REPE | N | TPE | N | 4 | ||
| COMPUTER: PERSONAL WORKSTATION H | N | TPE | N | 2 | ||
| COMPUTER, MICRO LAP-TOP PORTABLE | 9 | INS | N | 4 | ||
| COMPUTER, MICRO LAP-TOP PORTABLE | 9 | INS | N | 1 | ||
| MONITOR, COLOR IMPE: X5G | 9 | INS | N | 2 | ||
| PRINTER INKJET: HP, PSC 1315 HP | 9 | INS | N | 7 | ||
| WARLOCK LX, ECM: LX CECOM | V | TPE | N | 2 | ||
| CAMERA, DIGITAL: PANASONIC LUMIX | 9 | INS | N | 1 | ||
| FREEZER GENERIC | 9 | INS | N | 1 | ||
| PAPER SHREDDER:SD9510 | 9 | INS | N | 2 | ||
| TELEVISION 21" | 9 | INS | N | 1 | ||
| TELEVISION RECEIVER HOME TYPE, W | 9 | INS | N | 1 | ||
| TV, COLOR 19" GENERIC | 9 | INS | N | 1 | ||
| AC UNIT | 9 | INS | N | 1 | ||
| M24 BINOCULAR | 9 | INS | N | 4 | ||
| BATTERY CHARGERS | 9 | INS | N | 1 | ||
| COM SE AN/UYK-128(V)2-(AN/UYK-12 | N | TPE | N | 1 | ||
| COM SE AN/UYK-128(V)2-(AN/UYK-12 | P | TPE | N | 1 | ||
| COM SE AN/UYK-128(V)1 | N | TPE | N | 6 | ||
| COM SE AN/UYK-128(V)3 | N | TPE | N | 7 | ||
| COM SE AN/UYK-128(V)3 | T | TPE | N | 3 | ||
| CARR CMND POST M577A2 | T | TPE | N | 2 | ||
| CARR CMND POST M577A3 | T | TPE | N | 2 | ||
| SHIRT, COOLING PLUTO: SIZE: L | N | TPE | N | 3 | ||
| SHIRT, COOLING PLUTO: SIZE: ME | N | TPE | N | 4 | ||
| SHIRT, COOLING PLUTO: SIZE: SM | N | TPE | N | 1 | ||
| INSERT,SMALL ARMS P | N | TPE | N | 304 | ||
| CARRIER ASSEMBLY,UN | N | TPE | N | 152 | ||
| SOLAR SYSTEM TY I-(5-4-09696) | 9 | INS | N | 4 | ||
| SOLAR SYSTEM TY II-(5-4-09697) | N | TPE | N | 5 | ||
| MODULE VOICE, SECURE: 9505 IRIDI | N | TPE | N | 1 | ||
| TELEPHONES AND, ANSWERING EQUIPM | 9 | INS | N | 1 | ||
| TELEPHONE CELLULAR: SAGEM MYX-1 | 9 | INS | N | 4 | ||
| TELEPHONE-(23400-502) | 9 | INS | N | 1 | ||
| TELEPHONE SET: PHONE 7940 CISCO | 9 | INS | N | 1 | ||
| TELEPHONE SET: TA-1042A/U SCI SY | 9 | INS | N | 1 | ||
| TELEPHONE CONFERENCE SYSTEM: VSX | 9 | INS | N | 1 | ||
| TOUGHBOOK-WARLOCK SYSTEM: LT CF2 | N | TPE | N | 1 | ||
| TOUGHBOOK-WARLOCK SYSTEM: LT CF2 | V | TPE | N | 1 | ||
| RECEIVER-TRANSMITTER, FACSIMILE: | 9 | INS | N | 1 | ||
| HP DIGITAL SENDER: 9200C | 9 | INS | N | 2 | ||
| RECEIVER/TRANSMITTER RADIO: ASTR | N | TPE | N | 20 | ||
| RADIO COMMUNICATIONS SYSTEMS: IC | N | TPE | N | 7 | ||
| RADIO SET, AN/PRC 152 C: HARRIS | T | TPE | N | 24 | ||
| TELEVISION SET-(MDL# PT-52LCX65) | 9 | INS | N | 3 | ||
| EXTERNAL DVD-RW: SIMPLY BETTER | 9 | INS | N | 1 | ||
| COUNTERMEASURES SET-(WARLOCK DUK | V | TPE | N | 12 | ||
| TACTICAL OPERATIONS CENTER: SPEC | 9 | INS | N | 2 | ||
| TACTICOMP T1.5: A INTER-4 | N | TPE | N | 2 | ||
| VIDEO ACCESS POINT: VAP INTER-4 | N | TPE | N | 1 | ||
| TACTISIGHT: A INTER-4 | N | TPE | N | 2 | ||
| POWER SUPPLY | N | TPE | N | 3 | ||
| ICOM RAPID CHRGR DSKTP:BC160 | N | TPE | N | 2 | ||
| BATTERY CHARGER: BC121N 220V | N | TPE | N | 1 | ||
| SIREN ELECTRIC MOTOR OPERATED: S | 9 | INS | N | 5 | ||
| TAILORABLE INTEGRATED SECURITY S | 9 | INS | N | 1 | ||
| ANALYZER SPEC:MS2711D | N | TPE | N | 5 | ||
| BINOCULARS: 8 X 32, DIGITAL IMAG | N | TPE | N | 4 | ||
| SCOPE SPOTTING VARIABLE: 12X-40X | 9 | INS | N | 5 | ||
| COMPUTER NOTEBOOK/LAPTOP RUGGED | 9 | INS | N | 2 | ||
| LAPTOP: COMPUTER | 9 | INS | N | 2 | ||
| COMPUTER,DIGITAL-(0Y5 CPU (EV4)) | N | TPE | N | 1 | ||
| PRINTER DESKJET: HP 460C | 9 | INS | N | 2 | ||
| PRINTER LASERJET: 2600N HP | 9 | INS | N | 2 | ||
| PRINTER LASERJET: LBP-1120 CANON | 9 | INS | N | 1 | ||
| PRINTER LASERJET: 1600C HP | 9 | INS | N | 1 | ||
| PRINTER LASERJET: HP 1010 HP | 9 | INS | N | 2 | ||
| PRINTER LASERJET: LASERJET 4650 | 9 | INS | N | 1 | ||
| PRINTER LASERJET:HP F380 ALL-IN- | 9 | INS | N | 1 | ||
| EXTERNAL HD DR: 300GD MAXTOR | 9 | INS | N | 1 | ||
| PRINTER, MULTIFUNCTION CENTER / | 9 | INS | N | 1 | ||
| BISA HANDHELD: DSVII-SC AZIMUTH | 9 | INS | N | 1 | ||
| JIOC-I BASIC ANALYST LAPTOP (BAL | N | TPE | N | 1 | ||
| ENTER DIG ASSIST: MC70 | N | TPE | N | 4 | ||
| HARD DRIVE, EXTERNAL: SMARTDISK | 9 | INS | N | 2 | ||
| SHREDDER PAPER, ASSORTED: PS-220 | 9 | INS | N | 2 | ||
| SHREDDER PAPER: SD 9331 SUNWOOD | 9 | INS | N | 1 | ||
| WEAPONS SIGHTS: EO TECH RED DOT | N | TPE | N | 5 | ||
| WEAPONS SIGHTS: EO TECH RED DOT | P | TPE | N | 5 | ||
| SIGHT,BORE,OPTICAL | N | TPE | N | 2 | ||
| TELESCOPE,STRAIGHT-(TA01M4A1) | P | TPE | N | 4 | ||
| ADAPTER,COMMUNICATI-(11838876) | 9 | INS | N | 1 | ||
| SAFE-(SPECIFY ON REQUISITION) | 9 | INS | N | 4 | ||
| SAFE: BS-K510 | 9 | INS | N | 1 | ||
| MT MACH GUN M197 | H | LTT | N | 2 | ||
| MT MACH GUN M197 | T | TPE | N | 11 | ||
| MT MAC GUN 40MM MK93 | T | TPE | N | 13 | ||
| MT MAC GUN 40MM MK93 | H | LTT | N | 3 | ||
| MT MAC GUN MK64 MOD1 | T | TPE | N | 5 | ||
| MT MACH GUN MK64 MOD7 | H | LTT | N | 1 | ||
| MT MG MK64 MOD9 | T | TPE | N | 1 | ||
| MACH GUN 40MM: MK19 | T | TPE | N | 2 | ||
| MACH GUN 7.62MM M240B | T | TPE | N | 1 | ||
| ROUTER | 9 | INS | N | 1 | ||
| NAVIGATION SET: SAT | H | LTT | N | 28 | ||
| NAVIGATION SET: SAT | N | TPE | N | 8 | ||
| NAV SET SAT AN/PSN-13 | H | LTT | N | 1 | ||
| NAVIGATION SET,SATE | N | TPE | N | 7 | ||
| DEFIBRILLATOR/MONITOR-RECORDER S | 9 | INS | N | 1 | ||
| POWER SUPP PP-6224B/U | N | TPE | N | 1 | ||
| POWER SUPP PP-6224B/U | T | TPE | N | 2 | ||
| PRINTER,AUTOMATIC D-(Q5927A) | 9 | INS | N | 1 | ||
| RT-1523E(C)/U | H | LTT | N | 1 | ||
| RT-1523E(C)/U | T | TPE | N | 18 | ||
| RT-1523E(C)/U | V | TPE | N | 1 | ||
| RIFLE SNIPER M107 | N | TPE | N | 1 | ||
| RADIO SET: AN/VRC-90A | N | TPE | N | 2 | ||
| RECEIVER-TRANSMITTER | P | TPE | N | 2 | ||
| RADIO EMBITR AN PRC 148 | N | TPE | N | 7 | ||
| WANSA REFRIGER: RS-14DR4HA | 9 | INS | N | 3 | ||
| REFRIGERATOR: SAMSUNG SR148 | 9 | INS | N | 2 | ||
| REFRIGERATOR: IRAQI/WHITE | 9 | INS | N | 16 | ||
| TR 1 1/4T 4X4 M998P1 | N | TPE | N | 2 | ||
| TR 1 1/4T 4X4 M998P1 | T | TPE | N | 1 | ||
| TR AM 1 1/4 4L M997P1 | N | TPE | N | 1 | ||
| TR AM 1 1/4 4L M997P1 | P | TPE | N | 1 | ||
| TR AM 1 1/4 4L M997P1 | T | TPE | N | 1 | ||
| SHOTGUN 12 GAUGE | H | LTT | N | 6 | ||
| TR CARGO W/O WINCH M985P1 | N | TPE | N | 1 | ||
| TRK CGO W/E M1083A1 | N | TPE | Y | 1 | ||
| TRUCK CARGO M1078A1P1 | N | TPE | N | 1 | ||
| TRUCK CARGO M1078A1P1 | P | TPE | N | 1 | ||
| TRUCK CARGO M1078P1 | N | TPE | N | 1 | ||
| TRK UTIL HMMWV M1114 | T | TPE | N | 3 | ||
| PHONE IRIDIUM | N | TPE | N | 1 | ||
| ATV POLARIS RANGER | 9 | INS | N | 1 | ||
| TOWBAR MOTOR VEHICLE | N | TPE | N | 6 | ||
| PASSENGER MOTOR VEHICLE: TAHOE C | 9 | INS | N | 1 | ||
| HMMWV M1114: W/ OFK5 | N | TPE | N | 1 | ||
| HMMWV M1114: W/ OFK5 | T | TPE | N | 9 | ||
| HMMWV: M1151 W/ OFK5 | N | TPE | N | 1 | ||
| HMMWV: M1151 W/ OFK5 | T | TPE | N | 11 | ||
| VEHICLE ALL TERRAIN: POLARIS RAN | 9 | INS | N | 3 | ||
| PROCESSOR GROUP,SIG-(OL-700/TYQ) | N | TPE | N | 1 | ||
| MULTIBAND INTER/INTRA TEAM RADIO | N | TPE | N | 2 | ||
| MULTIBAND INTER/INTRA TEAM RADIO | T | TPE | N | 5 |


You gaze into your familiar's miniature crystal ball and ponder all the possible futures it has shown:


--- layout: post title: Mobile and Accessible: React Native for Everyone tags: - react-native - accessibility - coding ---
Recently in a meeting at my company, we were doing a retrospective — you know, the kind where you write "Start," "Stop," and "Keep" up on a board and everyone offers their thoughts on how we can improve moving forward. I raised my hand and asked, "Have we talked about accessibility lately?"
I quasi-knew the answer. We were so set on getting a prototypical version of our app out there in front of clients and the public that we hadn't focused on that particular detail, despite our mission being to make healthcare more accessible and understandable for everyone. The good news is, it landed in the "Start" column, and we set up a meeting with our architect, front-end devs, and designer to talk through accessibility guidelines and what it would take to make our React Native app more accessible.
Here's what I learned.
React Native's documentation is pretty good on this point! Essentially, the main thing (besides the guidelines for web accessibility, which still apply to mobile — think color contrast and other visual concerns) is making sure UI elements are exposed to native screen readers, which include VoiceOver for iOS and TalkBack on Android. This can be as easy as adding accessibility={true}
as a prop on a component, and also adding an accessibilityLabel
. That label is what will be read aloud when a user focuses on the element.
render() {
return (
<View
accessible={true} // fun fact: you don't need the "={true}" here!
accessibilityLabel="Benefits information section"
>
<Text>Plan information for 2018</Text>
<Text>More info here</Text>
</View>
)
}
What's really nice about this structure is that all those <Text>
elements will also have their accessibility prop set to true, because they are nested inside an accessible component.
Another nifty feature is accessibilityHint
. This prop is pretty self-explanatory; it provides a hint that will be read aloud after the label, which can be useful for touchable elements. You can alert the user as to what action will occur if they touch that element.
render() {
return (
<TouchableOpacity
accessible
accessibilityLabel="Benefits information"
accessibilityHint="Navigates to screen with benefits information"
accessibilityRole="link"
>
<Text>Plan info for 2018</Text>
</TouchableOpacity>
)
}
You'll notice I also included the accessibilityRole
in this example, which tells the screen reader tool what kind of element you're presenting. Other examples include button
, search
, image
, and header
. Any clues you can give the screen reader about your content will help it to better interpret and present your app to the user. It can be super revealing to turn on VoiceOver on your iPhone and experience firsthand how it navigates your favorite apps!
There are plenty of other features specific to iOS or Android, and you can check them all out in the documentation. I definitely found it to be a useful starting point for making our app more usable for everyone.


Using a multi-detector scanner, 0.625 mm axial scans of the temporal bone were acquired using a high-resolution bone technique. The scans were retrospectively targeted for right and left side, and subsequently reconstructed in the coronal plane, again targeting the right and left sides individually, as well as the entire skull base. Both axial and coronal images were reviewed.
None.
Middle ear cleft:
Ossicles:
Cochlea:
Vestibule:
Vestibular and cochlear aqueduct:
Facial nerve canal:
Semicircular canals:
Internal auditory canal:
External auditory canal:
Carotid canal:
Jugular foramen:
Temporomandibular joint:


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I'm a full stack developer who have 10+ years practical experience in Web & Mobile development.
Here I don't want mention about my coding skills, but my philosophy.
" Where there is a will, there is a way. "
" Relationships are much more important than the budget. "
" No pain, no gain. "
Have you got any issue you couldn't figure out yourself? Don't hesitate to contact me.


This section describes each prompt when entering into interactive configuration mode of the Geode HTTP Session Management Module for tc Server.
Please enter a value for 'geode-cs.maximum.vm.heap.size.mb'. Default '512':
Please enter a value for 'geode-cs.initial.vm.heap.size.mb'. Default '512':
Please enter a value for 'geode-cs.cms.initiating.heap.percentage'.  For more information, refer to Managing Heap and Off-heap Memory.
Please specify whether to enable a Geode listener that logs session create,
update, destroy and expiration events. Default 'false':
The above property determines whether a debug cache listener is added to the session region. When true, info-level messages are logged to the Geode log when sessions are created, updated, invalidated, or expired.
With the geode-p2p template:
Please specify whether to maintain a local Geode cache. Default 'false':
With the geode-cs template:
Please specify whether to maintain a local Geode cache. Default 'true':
The above property determines whether a local cache is enabled; if this parameter is set to true, the app server load balancer should be configured for sticky session mode.
With the geode-p2p template:
Please enter the id of the attributes of the Geode region used to cache
sessions.
Default 'REPLICATE':
With the geode-cs template:
Please enter the id of the attributes of the Geode region used to cache
sessions.
Default 'PARTITION_REDUNDANT':
The above property determines the ID of the attributes for the cache region; possible values include PARTITION, PARTITION_REDUNDANT, PARTITION_PERSISTENT, REPLICATE, REPLICATE_PERSISTENT, and any other region shortcut that can be found in Region Shortcuts and Custom Named Region Attributes. When using a partitioned region attribute, it is recommended that you use PARTITION_REDUNDANT (rather than PARTITION) to ensure that the failure of a server does not result in lost session data.
Please enter the name of the Geode region used to cache sessions.
Default 'gemfire_modules_sessions':
The above property determines the Geode region name.
Please enter the port that Tomcat Shutdown should listen on. Default '-1':
Please enter the port that the JMX socket listener should listen on.
Default '6969':
The above properties are application server properties.
Please enter a value for 'bio.http.port'. Default '8080':
Please enter a value for 'bio.https.port'. Default '8443':
tc Server requires information about connector ports. bio.http.port
is the http port for tc Server and bio.https.port
is the secure http port for tc Server.
With the geode-p2p template:
Please enter the name of the Geode cache configuration file.
Default 'cache-peer.xml':
With the geode-cs template:
Please enter the name of the Geode cache configuration file.
Default 'cache-client.xml':
You can change the name of the cache configuration file with the above property. If you do change this value, be sure to include an xml file by that name in the conf
subdirectory.
Please enter the percentage of heap at which updates to the cache are refused.
0.0 means disabled. Default '0.0':
Please enter the percentage of heap at which sessions will be evicted from the
local cache. Default '80.0':
The above properties allow you to control the critical and eviction watermarks for the heap. By default, the critical watermark is disabled (set to 0.0) and the eviction watermark is set to 80%.
Please enter the list of locators used by Geode members to discover each other.
The format is a comma-separated list of host[port]. Default ' ':
The above property specifies the list of locators.
Please enter the name of the file used to log Geode messages.
Default 'gemfire_modules.log':
The above property determines the file name for the Geode log file.
Applicable to the geode-p2p template ONLY:
Please specify whether to rebalance the Geode cache at startup.
Default 'false':
This property allows you to rebalance a partitioned Geode cache when a new Geode peer starts up.
Please enter the name of the file used to store Geode statistics.
Default 'gemfire_modules.gfs':
The above property determines the filename for the Geode statistics file.
Please specify whether Geode statistic sampling should be enabled.
Default 'false':
The above property determines whether statistics sampling should occur. See Statistics for more information.


by Jason Lengstorf
@jlengstorf ·<EMAIL_ADDRESS>Slides: git.io/adv-career
“I can’t do that! It doesn’t help me reach my goals. I’m wasting my time!”
“I’m not like that; I don’t have natural talent.”
“People, for some reason, do not want to believe it. They seem to think that excellence is only open to others, not themselves.”László Polgár
Jason Lengstorf
Follow me on Twitter: @jlengstorf


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Web Developer - Graphic Artist - User Experience
Designer


I am an undergraduate majoring in Art Practice, UC Berkeley. My main interests are in UX/UI and human-centered design.
GPA: 3.90 / 4.00
GPA: 3.91 / 4.00


Best What 3 word address is:
Nearest Place:
Country:
Easting
Northing
Get what3words Address
Goto Easting & Northing
Possible Other ///words & Locations:


 First, Larry King, who provides a two-minute cameo, gives the most zestful performance among the reputable ensemble cast. On his show, he lustfully berates his guest, film actress Gwen Harriston (Catherine Zeta-Jones). She's a beautiful and an accomplished celebrity, but her stardom has faded over the past two years. Why? As call-in listeners will tell her, they lament and complain about her previous decision to separate from her popular husband, film actor Eddie Thomas (John Cusack) in favor of a Castilian boy toy (Hank Azaria). Eddie, shattered by that incident, has become a permanent fixture at a secluded Serenity Institute where he is taught peace, gratitude, and unfathomable words of wisdom ("life is like a cookie," says its wizened leader).
Second, the most intriguing character, Lee Phillips (played by Billy Crystal), is swept aside into obscurity by the end of the film. Lee is a lubricious press agent who is tasked with assembling a blowout press junket for the estranged couple's last film. At first, he seems to provide a welcome dimension of edginess to a somewhat uninspired film. Watch him scheme and put his plan into motion as he coddles, appeases, maneuvers, and exercises damage control. This formidable task requires wit and energy. Unfortunately, he is eventually relegated to flippant one-liners and crude sight gags with a Doberman pinscher.
Third, the center of this film (the real reason we're going to see it) never generates any real momentum. This involves a budding love triangle between the narcissistic Gwen; her frumpy but sweet sister, Kiki (Julia Roberts); and the neurotic Eddie. But the tension is only lukewarm. In fact, the best scene of the film is given away if you've seen any of the trailers. It's really no contest between the two siblings. Kiki's character is the only one grounded in reality, and becomes the only person that you begin to care about.
All of this overwrought melodrama makes much of the film uncomfortably ham-fisted. The first 30 minutes (when we meet the major characters in their individual environments) are especially corny rather than funny and sadly laughable rather than amusing. It strains for laughter and the interplay between the characters feels laborious.
All of these actors are much better than this material would suggest. Zeta-Jones was delicious in "Traffic"; Roberts charm was played to perfection in "Notting Hill", and you'll miss Cusack's unique brand of quirkiness, offbeat humor, and wry behaviorism from films past. Perhaps, this role was a bit too mainstream for him.
"America's Sweethearts" failed to evoke that indescribable feeling of joy, weirdness, and goodness that are the necessary ingredients in a love story. Partly, Roberts could not particularly shine as she was obscured behind the star power of the other actors. Partly, the film spends too much time as a lampoon about the junket scene (where multitudes of critics ask the same questions, eat for free, and think up quotes like "America's Sweethearts Lacking Chemistry"). Thus, the film lacked conviction and its tone becomes fuzzy. Sometimes it's cute. Sometimes it's clever satire about Inside Hollywood. But more often, it's just unremarkable. Ultimately, it's inability to generate more than just some fleeting joy will leave you heartbroken.
Grade: C
S: 1 out of 3 L: 2 out of 3 V: 1 out of 3
========== X-RAMR-ID: 28979 X-Language: en X-RT-ReviewID: 240274 X-RT-TitleID: 1108688 X-RT-AuthorID: 1370 X-RT-RatingText: C
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Related links: index of all rec.arts.movies.reviews reviews


Submitted Team Reports
This is a list of past team reports. To add a new report, click on the 'Add new report' button.
| Date | Team | N | Cycle | Lead | D | Quality | Value |
|---|---|---|---|---|---|---|---|
| Date | Team | N | Cycle | Lead | D | Quality | Value |
| {{ .Date }} | {{ .Team }} | {{ .MemberCount }} | {{ .CycleTime }} | {{ .LeadTime }} | {{ .DeployCount }} | {{ printf "%.2f" .QualityScore }} | {{ printf "%.2f" .ValueScore }} |


Football: Roma win again as Napoli's frustrating run continues
ROME: AS Roma missed one penalty and converted another on Saturday on their way to a 2-1 Serie A win over Napoli who hit the woodwork twice in the first half as their frustrating run continued.
Napoli, missing coach Carlo Ancelotti after he was sent off during Wednesday's 2-2 draw at home to Atalanta, have taken only two points from their last three games and their hopes of mounting another title challenge are fading fast
The defeat left them seventh with 18 points from 11 games, eight behind leaders Juventus who were away to neighbours Torino later on Saturday.
Roma, on the other hand, climbed to third with 22 points despite a continuing injury crisis.
The match was stopped for one minute because of anti-Naples chanting - officially referred to as territorial discrimination - midway through the second half.
Nicolo Zaniolo opened the scoring for Roma in the 19th minute when Leonardo Spinazzola pulled the ball back for him to control and fire his shot into the roof of the net.
Minutes later, Roma were awarded a penalty for handball by Jose Callejon but Aleksandar Kolarov saw his effort saved by goalkeeper Alex Meret.
That seemed to give Napoli new impetus and Arkadiusz Milik had a header cleared off the line by Chris Smalling.
The Pole then saw another effort hit the bar before Piotr Zielinski struck the post in the same move while Dries Mertens should then have done better from another opening which he wasted by blasting the ball over the bar.
Roma won another penalty 10 minutes after the break for an obvious handball by Mario Rui and this time Jordan Veretout took the kick and converted despite Meret getting a hand to it.
Roma forward Justin Kluivert blasted a shot against the crossbar before Milik pulled one back for Napoli, rolling in Hirving Lozano's low cross at the far post, to give them hope.
Roma had Mert Cetin sent off deep into stoppage time but Milik saw the resulting free kick deflected wide.


#include <i_graph.h>
Data Fields | |
| void * | ptr |
| GraphNode | root |
| List | nodes |
| List | arcs |
| List | equiv_cds |
| List | topo_list |
| List | rev_topo_list |
| List | dfs_list |
| HashTable | hash_id_node |
| HashTable | hash_id_arc |
| HashTable | hash_id_equiv_cd |
| int | flags |
| int | arc_id |
| int | node_id |
Definition at line 196 of file i_graph.h.
| int graph::arc_id |
| int graph::flags |
Definition at line 212 of file i_graph.h.
Referenced by Graph_bfs_topo_sort(), and Graph_create_graph().
| int graph::node_id |
Definition at line 200 of file i_graph.h.
Referenced by Graph_bfs_topo_sort(), and Graph_create_graph().
| void* graph::ptr |
Definition at line 199 of file i_graph.h.
Referenced by Graph_bfs_topo_sort(), and Graph_create_graph().
Definition at line 204 of file i_graph.h.
Referenced by Graph_bfs_accept_node(), Graph_bfs_topo_sort(), and Graph_create_graph().


 Mr Greville remarked that “King William had considerable facility in expressing himself, but what he said was generally useless and improper”.
King William IV, described by Edith Sitwell in Victoria Of England (1936)


This is a modern port at the southern end of the Adriatic Sea, and the main ferry port for Greece. In ancient times it was a Roman naval station and now it is also a modern one. Brindisi also has Romanesque churches, and a 13th century castle.
There is a good boatyard here for wintering your boat.
Position 40°39.6'N,17°59.8'E (harbour entrance)
As this is a naval port, ensure your paperwork is in order and be sure to check in correctly.
The Harbourmaster's office overlooks the main quay in the inner harbour, directly opposite as you as you pass through the entrance. Customs are also based here.
Police are based in the town.
Yachts can berth on the NE-facing quay immediately in front of the harbour office in the old city. The area of this quay, opposite the entrance, is now used by the large fishing fleet when it returns each evening.
There are several marinas: Lega Navale, where there are also naval boats. A new marina just outside the city, Marina di Brindisi, also known as Porto Touristico, which is reported to be very good value for money if not a little isolated. There is also a mooring station named “Approdo delle Indie” located inside the port on the town quay providing shore power, water and internet.
Stopping in the new marina is the simplest option if on passage and only pausing for a night, or if planning on leaving your boat for an extended period.
The most comfortable berth is on the yacht club (Lega Navale) pontoons, which are close to town and amenities.
Anchorage
A good place to anchor in bad weather, is on the northern side of the north arm of the inner harbour. The holding is good in about 7m.
Last updated August 2016.


Deleted User
24-Oct-18 02:54 PM
The same applies for the validation stuff : steps 1 2 3. We can change the behaviour to skip these steps
The thing is they allow to be able to call the address gen right after we re sure both agreed on the trade
But it can be changed to be launched just after the receiver gives his addresses
It would remove 2 extra steps
So the trade remains "unconfirmed" until the receiver gives his addresses. And on that state it also allows us to delete unanswered asks for trades older than x days/minutes
You basically go from step 0 to 3
That is more user friendly as this logic will prevent one step on each side.
(edited)
I don t know how you intend to build that part but it could also mean that only one side has to type anything. The other could just use a link with his generated username inside
Or basically the creator of the trade fills every info like source amount / coin and dest amount / coin and the receiver only have to check that it matches when he launches the page
(edited)
That would make the autogen very cool as no noone has to enter anything else than trade details and own addresses
The best way maybe is to just draw on paper a clean flowchart and brainstorm it here
As long as we have a clear trade status, like state 3 : both have validated. Step 4 : address gen ok, step 5 : sender tx ok etc... it won t be a big change on queries and such
I figured out rethinking about parallelization that half steps are a problem. For example step 3 atm is when both agreed and addresses are not generated. So the scripts that will handle have to query unnecessary columns. Splitting into 2 clear steps would make the inside work more fluid and focused on specific steps instead of flood of if... if...
(edited)
Basically each step could be associated and launched in the same time than others. As there won t be overlapping in queries and scripts
(I planned to add extra step when address are generated, that would let me run concurrent address gen and tx check without any possible glitch.


This is a form. When submitted, it will show main.html
This is a form with an ID, Title and name.
This is a form with an ID and name.
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Psalms 59:7
Behold, they belch out with their mouth: swords are in their lips: for who, say they, doth hear?


Skip to Main Content
Accessible date picker calendar with selectable date ranges using hidden divs
Author: Tom Leadholm , adapted from Mike Foskett's calendar at WebSemantics. Updated: 17th June 2009
Created for Department of Veterans Affairs Adverse Drug Event Reporting System
** NOTE: sample date picker, no date range is specified so any date can be selected...
** NOTE: in this example, the calendar is given a date range. Start date cannot exist in the future, so dates beyond today are disabled...
** NOTE: in this example, the calendar is given a date range. Dates before the date selected above are disabled...


This site hosts a collection of Dwarf Fortress-related projects and links.
Packs for all platforms containing Dwarf Fortress as well as numerous utilities and improvements
A memory hacking library for Dwarf Fortress and a set of tools that use it.
Github Readme Download »A host for Dwarf Fortress-related files: mods, utilities, tilesets, pre-generated worlds, and more
The Dwarf Fortress Map Archive, a repository and online viewer for maps exported from DF
A collection of world seeds and embark sites, as well as some pre-generated worlds


UTETACM - Use 'this' Explicitly To Access Class Members
Tries to make you use 'this' explicitly to access class members. Using same class members' and parameters' names makes references confusing.
Wrong
int attr = 10;
void func () {
// do something
}
void oper () {
func();
attr = 20;
}
}
Right
int attr = 10;
void func () {
// do something
}
void oper () {
this.func();
this.attr = 20;
}
}


For as low as₹2800/night
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Lorem ipsum dolor sit amet, consectetur adipisicing elit. Molestias officia perferendis modi impedit, rem quasi veritatis. Consectetur obcaecati incidunt, quae rerum, accusamus sapiente fuga vero at. Quia, labore, reprehenderit illum dolorem quae facilis reiciendis quas similique totam sequi ducimus temporibus ex nemo, omnis perferendis earum fugit impedit molestias animi vitae.
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Laboriosam neque blanditiis eveniet nesciunt, beatae similique doloribus, ex impedit rem officiis placeat dignissimos molestias temporibus, in! Minima quod, consequatur neque aliquam.
Service Hours 8:00 AM - 11:00 PM
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Lorem ipsum dolor sit amet, consectetur adipisicing elit. Laboriosam neque blanditiis eveniet nesciunt, beatae similique doloribus, ex impedit rem officiis placeat dignissimos molestias temporibus, in! Minima quod, consequatur neque aliquam.
Time 6:30 PM - 12:00 AM
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Molestias officia perferendis modi impedit, rem quasi veritatis. Consectetur obcaecati incidunt, quae rerum, accusamus sapiente fuga vero at. Quia, labore, reprehenderit illum dolorem quae facilis reiciendis quas similique totam sequi ducimus temporibus ex nemo, omnis perferendis earum fugit impedit molestias animi vitae.
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Laboriosam neque blanditiis eveniet nesciunt, beatae similique doloribus, ex impedit rem officiis placeat dignissimos molestias temporibus, in! Minima quod, consequatur neque aliquam.
Service Hours 7:30 AM - 8:00 PM
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Molestias officia perferendis modi impedit, rem quasi veritatis. Consectetur obcaecati incidunt, quae rerum, accusamus sapiente fuga vero at. Quia, labore, reprehenderit illum dolorem quae facilis reiciendis quas similique totam sequi ducimus temporibus ex nemo, omnis perferendis earum fugit impedit molestias animi vitae.
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Laboriosam neque blanditiis eveniet nesciunt, beatae similique doloribus, ex impedit rem officiis placeat dignissimos molestias temporibus, in! Minima quod, consequatur neque aliquam.
Service Hours 9:00 AM - 11:00 PM
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Molestias officia perferendis modi impedit, rem quasi veritatis. Consectetur obcaecati incidunt, quae rerum, accusamus sapiente fuga vero at. Quia, labore, reprehenderit illum dolorem quae facilis reiciendis quas similique totam sequi ducimus temporibus ex nemo, omnis perferendis earum fugit impedit molestias animi vitae.
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Laboriosam neque blanditiis eveniet nesciunt, beatae similique doloribus, ex impedit rem officiis placeat dignissimos molestias temporibus, in! Minima quod, consequatur neque aliquam.
Service Hours 7:30 AM - 8:00 PM
“A go to place to relax in south goa right next to the beach. Sea facing rooms, friendly staff , less crowded beaches , pleasant nights with tasty food. Teno and her team has been very responsive and an amazing hosting team. Budget friendly and worth every penny. Most relaxed holiday i had post lockdown.”
“Teno's place is located centrally on Benaulim beach and is absolutely amazing to stay at. The staff is courteous and super helpful and try to give you the best experience possible at everything from cleaning the room to getting you extra plates. The place is quite close to Margoa as well, so it is easy to reach the city.  Teno is absolutely amazing host who will make your stay the best one you have had yet in Goa!”
“Teno is doing quite a good job with the property at Pele's Fisherman Village. We had a very comfortable stay. The place is spacious, clean and airy and you literally feel like you have a house almost at the beach. Lots of positive vibes here. There is a small patio outside the property on the ground floor that we stayed at and that was perfect for my morning yoga and meditation. Also the place is pet friendly which is a big plus for me.”


Calculating execution plan...
This will migrate {{source.asgName}} in {{source.region}} to VPC0.
As part of this migration, the following items will be created:
The following items have been created:
Security Groups
{{securityGroup.mutationDetails.awsReference.identity.groupName}}
Load Balancers
{{loadBalancer.mutationDetails.awsReference.identity.loadBalancerName}}
Note: if Route53 or sticky session policies are configured for the current load balancer,
you will need to set that up in the AWS console.
Server Groups
{{serverGroup.mutationDetails.awsReference.identity.autoScalingGroupName}}
Note: server groups will be started in a disabled state with zero instances.
Note: server group has been started in a disabled state with zero instances.


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In backtests, you can add and remove cash from the cash book. To add or remove cash, call the AddAmount
method.
var newUSDBalance = Portfolio.CashBook["USD"].AddAmount(100); var newBTCBalance = Portfolio.CashBook["BTC"].AddAmount(-1.5m);
new_usd_balance = self.Portfolio.CashBook["USD"].AddAmount(100) new_btc_balance = self.Portfolio.CashBook["BTC"].AddAmount(-1.5)
In live trading, add and withdraw cash through your brokerage account. If you adjust the cash balances in your algorithm with the AddAmount
method, the cash balance in your algorithm will be out of sync with the cash balance in your brokerage account.


Add a fill layer to the map
add_fill_layer(map, source = NULL, filter = NULL, fill_antialias = TRUE, fill_color = NULL, fill_opacity = NULL, fill_outline_color = NULL, fill_pattern = NULL, fill_sort_key = NULL, fill_translate = NULL, fill_translate_anchor = NULL, visibility = TRUE, popup = NULL, id = "fill-layer")
| map | A mapboxer object. |
|---|---|
| source | A Mapbox source. Uses the source from the mapboxer object if no source is supplied. |
| filter | A filter expression that is applied to the |
| fill_antialias | (paint) Whether or not the fill should be antialiased. |
| fill_color | (paint) The color of the filled part of this layer. This color can be specified as rgba with an alpha component and the color's opacity will not affect the opacity of the 1px stroke, if it is used. |
| fill_opacity | (paint) The opacity of the entire fill layer.
In contrast to the |
| fill_outline_color | (paint) The outline color of the fill.
Matches the value of |
| fill_pattern | (paint) Name of image in sprite to use for drawing image fills. |
| fill_sort_key | (layout) Sorts features in ascending order based on this value. Features with a higher sort key will appear above features with a lower sort key. |
| fill_translate | (paint) The geometry's offset. Values are [x, y] where negatives indicate left and up, respectively. |
| fill_translate_anchor | (paint) Controls the frame of reference for |
| visibility | (layout) Whether the layer should be displayed. |
| popup | A mustache template in which the tags refer to the properties of the layer's data object. |
| id | The unique id of the layer. |
map <- as_mapbox_source(geojsonsf::geo_melbourne) %>% mapboxer() %>% set_view_state( lng = 144.9624, lat = -37.8105, zoom = 10, pitch = 35 ) %>% add_fill_layer( fill_color = c("get", "fillColor"), fill_opacity = 0.6, popup = "Area: {{AREASQKM}} km2", # AREASQKM > 5 filter = list(">", c("get", "AREASQKM"), 5) ) if (interactive()) map


Hi, I'm Lietu, an enthusiast variety streamer. I'm about as variety as variety goes, I play recent games, old games, code, dance and cut my hair on stream. I do this for fun rather than aiming to be a huge streamer or similar, so I don't often go after the same things other streamers might, e.g. popular games. I'm a real person, sometimes I get annoyed, sometimes I'm angry, but all my content is the real me.


{% load static %}
The Weird Shop
Categories
[[ category ]]
Products
[[ product.name ]]: [[ product.clicks ]] clicks
Selected Product
Product name: [[ product.name ]]
This product has been clicked on [[ product.clicks ]] times.


Nitpicking Accross The Web
Bashing athletes is as American as apple pie. Stick it to them with a well thought out rant on our sports page!
Everybody hates celebrities and ripping them online is the easiest way to let them know how much you despise them
If you are looking for a topic sure to encite rage, look no further than our politics page
Nonsensical arguments about trivial situations should never be contained within one topic. If your looking for adventurious complaints this category is just what you need!


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For Fall 2020: MAT221, MAT222, MAT321 and MAT322 will use Stewart's Early Transcendentals 9th edition, published by Cengage Learning.
This book is included as an e-book in Cengage's WebAssign online homework system, which is required. In addition, an optional hard copy is available for rent from Cengage, or students can purchase at higher cost a Cengage package from the bookstore, or direct from Cengage, that includes a looseleaf paper text in addition to the e-book.
Every section of MAT221, MAT222, MAT321 and MAT322 will have a WebAssign component integrated with the text. WebAssign costs money, but the FIRST TWO WEEKS ARE FREE and you should test drive it during drop/add. Your WebAssign class site will become available for viewing on the day of or the day before the start of classes.
To get access to WebAssign: Simply go to the WebAssign link on the Canvas course home page and follow the instructions in the "Start Here" page. There is no course key -- you register for your section's WebAssign class through your class Canvas site.
If you have had a previous WebAssign account and if your username on your old account does not exactly match the username on the new one contact Student Support at WebAssign to sync your new account to the old one.
The most flexible option and recommended option is called Cengage Unlimited https://www.cengage.com/unlimited. We recommend what is called "Cengage Unlimited 4-month", $119.99 purchased online or at the bookstore, which gives you the following:
The cheapest choice for just one semester of calculus is the one semester access (Fall 2020 price: $75) using the "opt-in" program through the TC Bookstore.
The opt-in URL is https://www.bkstr.com/tougaloostore/home and is open from approximately two weeks before classes start and closes shortly after the semester starts. If you miss the opt-in window you will have to purchase access using one of the other options.
After opting-in you will receive your materials via e-mail within a few hours, with the subject line "Digital Material For ___ Has Arrived!"". This e-mail will contain a link to the Canvas login and an access code for MindTap and the etext which you can use to register from within your Canvas course. After opting-in the charge of $75 will be posted to your TC account. Note that the opt-in one semester access includes the e-text and WebAssign, but does not include the option to rent a hard copy for $8.
If you opt-in and then realize you will need Cengage Unlimited, you can still pay the difference to upgrade within the first two weeks.
Need help registering for your Cengage course materials, or have questions? The Cengage team is hosting
Zoom office hours at the below times for you to join. Feel free to join any of the sessions to ask
your questions or learn more about the materials for this course:
August 10 to September 1 (M-F)
10AM to 12PM and 2PM to 5PM
Meeting ID: 915 4832 8394
This page was last updated on 09/22/2020.


Steps away from California
Mar 22, 2015
Last year I took on-site interview visits to two of the most greatest tech companies in the bay area. Being spent in California about two weeks and talked with so many talented geeks, I think there should be something for me that can be memorized.
Another benifit of construct test cases as the 1st priority is that, for complicated code logic, test cases lead us to find the solution.So once get stuck, think several test cases and it makes everything clear.
Software programming seems to be a kind of brain-based work. However it's not true, with a well-organized body benefits a lot, at least during the interviews. Both of the companies have 2 phone screens and 5 on-site face-to-face interviews. Each interview takes about 45-60 minutes with 1-2 coding questions and there is almost no rest time during interview rounds. That means during the 4-5 hours there is only a 30 minutes break for lunch, which is a very heavy pressure on brain. For international interviewee it is much worse because we also have to fight with jet-lag. So keep physical practicing all the life, then we can be strong enough to deal with the pressure which leads to career succeess.1. Test, test and test
2. Keep practicing, keep young, all my life
3. if succeed, goto exit; else if too old, goto exit; else goto 1.
Test, test and test
We may think that testing is the work for testers, we may start coding ASAP and think about testing when it is finished, we may .... --- all kinds of bad habits in modern software engineering. Writing code with avoidable bugs wastes both our and other's time. It's our software developer's responsibility to deliever bug-free code at first time. So please consider test cases once the software architecture and interfaces have been defined. Run test cases whenever any code is changed so that no regression can be made.Another benifit of construct test cases as the 1st priority is that, for complicated code logic, test cases lead us to find the solution.So once get stuck, think several test cases and it makes everything clear.
Keep practicing, keep young, all my life
No matter how many years we have worked as software engineers, we need to keep our brain activated all the time. Brain is actually quite lazy so it's easy to be content to current state, to lost interests and care nothing. Whenever we find any software interesting, think about how it works, how to design and implement it if we are the developers, think about any algorithm which it may apply, think whether the alogrithms can be used in any project we taking ... Don't limit only to your expertise area (for me it's Android), there are always something to be learn from outside that.Software programming seems to be a kind of brain-based work. However it's not true, with a well-organized body benefits a lot, at least during the interviews. Both of the companies have 2 phone screens and 5 on-site face-to-face interviews. Each interview takes about 45-60 minutes with 1-2 coding questions and there is almost no rest time during interview rounds. That means during the 4-5 hours there is only a 30 minutes break for lunch, which is a very heavy pressure on brain. For international interviewee it is much worse because we also have to fight with jet-lag. So keep physical practicing all the life, then we can be strong enough to deal with the pressure which leads to career succeess.


This example shows how to use the gmf.editing.EditFeature
service to insert, update and delete features from a layer using
a GeoMapFish server. First, you must log in. Then, you can either
click on the Insert button to insert a random feature, or
click on a feature on the map to either Update or
Delete it.


참고
Click here to download the full example code
Introduction || Tensors || Autograd || Building Models || TensorBoard Support || Training Models || Model Understanding
The Fundamentals of Autograd¶
Follow along with the video below or on youtube.
PyTorch’s Autograd feature is part of what make PyTorch flexible and fast for building machine learning projects. It allows for the rapid and easy computation of multiple partial derivatives (also referred to as gradients) over a complex computation. This operation is central to backpropagation-based neural network learning.
The power of autograd comes from the fact that it traces your computation dynamically at runtime, meaning that if your model has decision branches, or loops whose lengths are not known until runtime, the computation will still be traced correctly, and you’ll get correct gradients to drive learning. This, combined with the fact that your models are built in Python, offers far more flexibility than frameworks that rely on static analysis of a more rigidly-structured model for computing gradients.
What Do We Need Autograd For?¶
A machine learning model is a function, with inputs and outputs. For this discussion, we’ll treat the inputs as an i-dimensional vector \(\vec{x}\), with elements \(x_{i}\). We can then express the model, M, as a vector-valued function of the input: \(\vec{y} = \vec{M}(\vec{x})\). (We treat the value of M’s output as a vector because in general, a model may have any number of outputs.)
Since we’ll mostly be discussing autograd in the context of training, our output of interest will be the model’s loss. The loss function L(\(\vec{y}\)) = L(\(\vec{M}\)(\(\vec{x}\))) is a single-valued scalar function of the model’s output. This function expresses how far off our model’s prediction was from a particular input’s ideal output. Note: After this point, we will often omit the vector sign where it should be contextually clear - e.g., \(y\) instead of \(\vec y\).
In training a model, we want to minimize the loss. In the idealized case of a perfect model, that means adjusting its learning weights - that is, the adjustable parameters of the function - such that loss is zero for all inputs. In the real world, it means an iterative process of nudging the learning weights until we see that we get a tolerable loss for a wide variety of inputs.
How do we decide how far and in which direction to nudge the weights? We want to minimize the loss, which means making its first derivative with respect to the input equal to 0: \(\frac{\partial L}{\partial x} = 0\).
Recall, though, that the loss is not directly derived from the input, but a function of the model’s output (which is a function of the input directly), \(\frac{\partial L}{\partial x}\) = \(\frac{\partial {L({\vec y})}}{\partial x}\). By the chain rule of differential calculus, we have \(\frac{\partial {L({\vec y})}}{\partial x}\) = \(\frac{\partial L}{\partial y}\frac{\partial y}{\partial x}\) = \(\frac{\partial L}{\partial y}\frac{\partial M(x)}{\partial x}\).
\(\frac{\partial M(x)}{\partial x}\) is where things get complex. The partial derivatives of the model’s outputs with respect to its inputs, if we were to expand the expression using the chain rule again, would involve many local partial derivatives over every multiplied learning weight, every activation function, and every other mathematical transformation in the model. The full expression for each such partial derivative is the sum of the products of the local gradient of every possible path through the computation graph that ends with the variable whose gradient we are trying to measure.
In particular, the gradients over the learning weights are of interest to us - they tell us what direction to change each weight to get the loss function closer to zero.
Since the number of such local derivatives (each corresponding to a separate path through the model’s computation graph) will tend to go up exponentially with the depth of a neural network, so does the complexity in computing them. This is where autograd comes in: It tracks the history of every computation. Every computed tensor in your PyTorch model carries a history of its input tensors and the function used to create it. Combined with the fact that PyTorch functions meant to act on tensors each have a built-in implementation for computing their own derivatives, this greatly speeds the computation of the local derivatives needed for learning.
A Simple Example¶
That was a lot of theory - but what does it look like to use autograd in practice?
Let’s start with a straightforward example. First, we’ll do some imports to let us graph our results:
# %matplotlib inline
import torch
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import math
Next, we’ll create an input tensor full of evenly spaced values on the
interval \([0, 2{\pi}]\), and specify requires_grad=True
. (Like
most functions that create tensors, torch.linspace()
accepts an
optional requires_grad
option.) Setting this flag means that in
every computation that follows, autograd will be accumulating the
history of the computation in the output tensors of that computation.
a = torch.linspace(0., 2. * math.pi, steps=25, requires_grad=True)
print(a)
tensor([0.0000, 0.2618, 0.5236, 0.7854, 1.0472, 1.3090, 1.5708, 1.8326, 2.0944,
2.3562, 2.6180, 2.8798, 3.1416, 3.4034, 3.6652, 3.9270, 4.1888, 4.4506,
4.7124, 4.9742, 5.2360, 5.4978, 5.7596, 6.0214, 6.2832],
requires_grad=True)
Next, we’ll perform a computation, and plot its output in terms of its inputs:
b = torch.sin(a)
plt.plot(a.detach(), b.detach())
[<matplotlib.lines.Line2D object at 0x7efe18a70c70>]
Let’s have a closer look at the tensor b
. When we print it, we see
an indicator that it is tracking its computation history:
print(b)
tensor([ 0.0000e+00, 2.5882e-01, 5.0000e-01, 7.0711e-01, 8.6603e-01,
9.6593e-01, 1.0000e+00, 9.6593e-01, 8.6603e-01, 7.0711e-01,
5.0000e-01, 2.5882e-01, -8.7423e-08, -2.5882e-01, -5.0000e-01,
-7.0711e-01, -8.6603e-01, -9.6593e-01, -1.0000e+00, -9.6593e-01,
-8.6603e-01, -7.0711e-01, -5.0000e-01, -2.5882e-01, 1.7485e-07],
grad_fn=<SinBackward0>)
This grad_fn
gives us a hint that when we execute the
backpropagation step and compute gradients, we’ll need to compute the
derivative of \(\sin(x)\) for all this tensor’s inputs.
Let’s perform some more computations:
c = 2 * b
print(c)
d = c + 1
print(d)
tensor([ 0.0000e+00, 5.1764e-01, 1.0000e+00, 1.4142e+00, 1.7321e+00,
1.9319e+00, 2.0000e+00, 1.9319e+00, 1.7321e+00, 1.4142e+00,
1.0000e+00, 5.1764e-01, -1.7485e-07, -5.1764e-01, -1.0000e+00,
-1.4142e+00, -1.7321e+00, -1.9319e+00, -2.0000e+00, -1.9319e+00,
-1.7321e+00, -1.4142e+00, -1.0000e+00, -5.1764e-01, 3.4969e-07],
grad_fn=<MulBackward0>)
tensor([ 1.0000e+00, 1.5176e+00, 2.0000e+00, 2.4142e+00, 2.7321e+00,
2.9319e+00, 3.0000e+00, 2.9319e+00, 2.7321e+00, 2.4142e+00,
2.0000e+00, 1.5176e+00, 1.0000e+00, 4.8236e-01, -3.5763e-07,
-4.1421e-01, -7.3205e-01, -9.3185e-01, -1.0000e+00, -9.3185e-01,
-7.3205e-01, -4.1421e-01, 4.7684e-07, 4.8236e-01, 1.0000e+00],
grad_fn=<AddBackward0>)
Finally, let’s compute a single-element output. When you call
.backward()
on a tensor with no arguments, it expects the calling
tensor to contain only a single element, as is the case when computing a
loss function.
out = d.sum()
print(out)
tensor(25., grad_fn=<SumBackward0>)
Each grad_fn
stored with our tensors allows you to walk the
computation all the way back to its inputs with its next_functions
property. We can see below that drilling down on this property on d
shows us the gradient functions for all the prior tensors. Note that
a.grad_fn
is reported as None
, indicating that this was an input
to the function with no history of its own.
print('d:')
print(d.grad_fn)
print(d.grad_fn.next_functions)
print(d.grad_fn.next_functions[0][0].next_functions)
print(d.grad_fn.next_functions[0][0].next_functions[0][0].next_functions)
print(d.grad_fn.next_functions[0][0].next_functions[0][0].next_functions[0][0].next_functions)
print('\nc:')
print(c.grad_fn)
print('\nb:')
print(b.grad_fn)
print('\na:')
print(a.grad_fn)
d:
<AddBackward0 object at 0x7efd66d3aa60>
((<MulBackward0 object at 0x7efe8b05b190>, 0), (None, 0))
((<SinBackward0 object at 0x7efd66d3aa60>, 0), (None, 0))
((<AccumulateGrad object at 0x7efe8b05b190>, 0),)
()
c:
<MulBackward0 object at 0x7efd66d3aa60>
b:
<SinBackward0 object at 0x7efd66d3aa60>
a:
None
With all this machinery in place, how do we get derivatives out? You
call the backward()
method on the output, and check the input’s
grad
property to inspect the gradients:
out.backward()
print(a.grad)
plt.plot(a.detach(), a.grad.detach())
tensor([ 2.0000e+00, 1.9319e+00, 1.7321e+00, 1.4142e+00, 1.0000e+00,
5.1764e-01, -8.7423e-08, -5.1764e-01, -1.0000e+00, -1.4142e+00,
-1.7321e+00, -1.9319e+00, -2.0000e+00, -1.9319e+00, -1.7321e+00,
-1.4142e+00, -1.0000e+00, -5.1764e-01, 2.3850e-08, 5.1764e-01,
1.0000e+00, 1.4142e+00, 1.7321e+00, 1.9319e+00, 2.0000e+00])
[<matplotlib.lines.Line2D object at 0x7efe0a127820>]
Recall the computation steps we took to get here:
a = torch.linspace(0., 2. * math.pi, steps=25, requires_grad=True)
b = torch.sin(a)
c = 2 * b
d = c + 1
out = d.sum()
Adding a constant, as we did to compute d
, does not change the
derivative. That leaves \(c = 2 * b = 2 * \sin(a)\), the derivative
of which should be \(2 * \cos(a)\). Looking at the graph above,
that’s just what we see.
Be aware that only leaf nodes of the computation have their gradients
computed. If you tried, for example, print(c.grad)
you’d get back
None
. In this simple example, only the input is a leaf node, so only
it has gradients computed.
Autograd in Training¶
We’ve had a brief look at how autograd works, but how does it look when it’s used for its intended purpose? Let’s define a small model and examine how it changes after a single training batch. First, define a few constants, our model, and some stand-ins for inputs and outputs:
BATCH_SIZE = 16
DIM_IN = 1000
HIDDEN_SIZE = 100
DIM_OUT = 10
class TinyModel(torch.nn.Module):
def __init__(self):
super(TinyModel, self).__init__()
self.layer1 = torch.nn.Linear(1000, 100)
self.relu = torch.nn.ReLU()
self.layer2 = torch.nn.Linear(100, 10)
def forward(self, x):
x = self.layer1(x)
x = self.relu(x)
x = self.layer2(x)
return x
some_input = torch.randn(BATCH_SIZE, DIM_IN, requires_grad=False)
ideal_output = torch.randn(BATCH_SIZE, DIM_OUT, requires_grad=False)
model = TinyModel()
One thing you might notice is that we never specify
requires_grad=True
for the model’s layers. Within a subclass of
torch.nn.Module
, it’s assumed that we want to track gradients on the
layers’ weights for learning.
If we look at the layers of the model, we can examine the values of the weights, and verify that no gradients have been computed yet:
print(model.layer2.weight[0][0:10]) # just a small slice
print(model.layer2.weight.grad)
tensor([ 0.0420, -0.0028, 0.0361, -0.0887, 0.0739, -0.0280, -0.0326, -0.0877,
-0.0122, -0.0613], grad_fn=<SliceBackward0>)
None
Let’s see how this changes when we run through one training batch. For a
loss function, we’ll just use the square of the Euclidean distance
between our prediction
and the ideal_output
, and we’ll use a
basic stochastic gradient descent optimizer.
optimizer = torch.optim.SGD(model.parameters(), lr=0.001)
prediction = model(some_input)
loss = (ideal_output - prediction).pow(2).sum()
print(loss)
tensor(161.0877, grad_fn=<SumBackward0>)
Now, let’s call loss.backward()
and see what happens:
loss.backward()
print(model.layer2.weight[0][0:10])
print(model.layer2.weight.grad[0][0:10])
tensor([ 0.0420, -0.0028, 0.0361, -0.0887, 0.0739, -0.0280, -0.0326, -0.0877,
-0.0122, -0.0613], grad_fn=<SliceBackward0>)
tensor([ 1.5680, 3.4214, 4.5222, -2.4172, 6.6147, -0.3309, 4.0945, 2.8874,
1.6588, 7.2661])
We can see that the gradients have been computed for each learning weight, but the weights remain unchanged, because we haven’t run the optimizer yet. The optimizer is responsible for updating model weights based on the computed gradients.
optimizer.step()
print(model.layer2.weight[0][0:10])
print(model.layer2.weight.grad[0][0:10])
tensor([ 0.0404, -0.0063, 0.0316, -0.0862, 0.0673, -0.0277, -0.0367, -0.0906,
-0.0139, -0.0685], grad_fn=<SliceBackward0>)
tensor([ 1.5680, 3.4214, 4.5222, -2.4172, 6.6147, -0.3309, 4.0945, 2.8874,
1.6588, 7.2661])
You should see that layer2
’s weights have changed.
One important thing about the process: After calling
optimizer.step()
, you need to call optimizer.zero_grad()
, or
else every time you run loss.backward()
, the gradients on the
learning weights will accumulate:
print(model.layer2.weight.grad[0][0:10])
for i in range(0, 5):
prediction = model(some_input)
loss = (ideal_output - prediction).pow(2).sum()
loss.backward()
print(model.layer2.weight.grad[0][0:10])
optimizer.zero_grad(set_to_none=False)
print(model.layer2.weight.grad[0][0:10])
tensor([ 1.5680, 3.4214, 4.5222, -2.4172, 6.6147, -0.3309, 4.0945, 2.8874,
1.6588, 7.2661])
tensor([ -5.7502, 1.0886, 9.9504, -5.3917, 20.2262, -18.0501, 16.7105,
23.2145, 11.5041, 16.4386])
tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
After running the cell above, you should see that after running
loss.backward()
multiple times, the magnitudes of most of the
gradients will be much larger. Failing to zero the gradients before
running your next training batch will cause the gradients to blow up in
this manner, causing incorrect and unpredictable learning results.
Turning Autograd Off and On¶
There are situations where you will need fine-grained control over whether autograd is enabled. There are multiple ways to do this, depending on the situation.
The simplest is to change the requires_grad
flag on a tensor
directly:
a = torch.ones(2, 3, requires_grad=True)
print(a)
b1 = 2 * a
print(b1)
a.requires_grad = False
b2 = 2 * a
print(b2)
tensor([[1., 1., 1.],
[1., 1., 1.]], requires_grad=True)
tensor([[2., 2., 2.],
[2., 2., 2.]], grad_fn=<MulBackward0>)
tensor([[2., 2., 2.],
[2., 2., 2.]])
In the cell above, we see that b1
has a grad_fn
(i.e., a traced
computation history), which is what we expect, since it was derived from
a tensor, a
, that had autograd turned on. When we turn off autograd
explicitly with a.requires_grad = False
, computation history is no
longer tracked, as we see when we compute b2
.
If you only need autograd turned off temporarily, a better way is to use
the torch.no_grad()
:
a = torch.ones(2, 3, requires_grad=True) * 2
b = torch.ones(2, 3, requires_grad=True) * 3
c1 = a + b
print(c1)
with torch.no_grad():
c2 = a + b
print(c2)
c3 = a * b
print(c3)
tensor([[5., 5., 5.],
[5., 5., 5.]], grad_fn=<AddBackward0>)
tensor([[5., 5., 5.],
[5., 5., 5.]])
tensor([[6., 6., 6.],
[6., 6., 6.]], grad_fn=<MulBackward0>)
torch.no_grad()
can also be used as a function or method decorator:
def add_tensors1(x, y):
return x + y
@torch.no_grad()
def add_tensors2(x, y):
return x + y
a = torch.ones(2, 3, requires_grad=True) * 2
b = torch.ones(2, 3, requires_grad=True) * 3
c1 = add_tensors1(a, b)
print(c1)
c2 = add_tensors2(a, b)
print(c2)
tensor([[5., 5., 5.],
[5., 5., 5.]], grad_fn=<AddBackward0>)
tensor([[5., 5., 5.],
[5., 5., 5.]])
There’s a corresponding context manager, torch.enable_grad()
, for
turning autograd on when it isn’t already. It may also be used as a
decorator.
Finally, you may have a tensor that requires gradient tracking, but you
want a copy that does not. For this we have the Tensor
object’s
detach()
method - it creates a copy of the tensor that is detached
from the computation history:
x = torch.rand(5, requires_grad=True)
y = x.detach()
print(x)
print(y)
tensor([0.1587, 0.8605, 0.4364, 0.7301, 0.2533], requires_grad=True)
tensor([0.1587, 0.8605, 0.4364, 0.7301, 0.2533])
We did this above when we wanted to graph some of our tensors. This is
because matplotlib
expects a NumPy array as input, and the implicit
conversion from a PyTorch tensor to a NumPy array is not enabled for
tensors with requires_grad=True. Making a detached copy lets us move
forward.
Autograd and In-place Operations¶
In every example in this notebook so far, we’ve used variables to
capture the intermediate values of a computation. Autograd needs these
intermediate values to perform gradient computations. For this reason,
you must be careful about using in-place operations when using
autograd. Doing so can destroy information you need to compute
derivatives in the backward()
call. PyTorch will even stop you if
you attempt an in-place operation on leaf variable that requires
autograd, as shown below.
참고
The following code cell throws a runtime error. This is expected.
a = torch.linspace(0., 2. * math.pi, steps=25, requires_grad=True)
torch.sin_(a)
Autograd Profiler¶
Autograd tracks every step of your computation in detail. Such a computation history, combined with timing information, would make a handy profiler - and autograd has that feature baked in. Here’s a quick example usage:
device = torch.device('cpu')
run_on_gpu = False
if torch.cuda.is_available():
device = torch.device('cuda')
run_on_gpu = True
x = torch.randn(2, 3, requires_grad=True)
y = torch.rand(2, 3, requires_grad=True)
z = torch.ones(2, 3, requires_grad=True)
with torch.autograd.profiler.profile(use_cuda=run_on_gpu) as prf:
for _ in range(1000):
z = (z / x) * y
print(prf.key_averages().table(sort_by='self_cpu_time_total'))
------------------------- ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------
Name Self CPU % Self CPU CPU total % CPU total CPU time avg Self CUDA Self CUDA % CUDA total CUDA time avg # of Calls
------------------------- ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------
aten::div 49.81% 5.509ms 49.81% 5.509ms 5.509us 10.757ms 50.11% 10.757ms 10.757us 1000
aten::mul 49.04% 5.423ms 49.04% 5.423ms 5.423us 10.709ms 49.89% 10.709ms 10.709us 1000
cudaDeviceSynchronize 1.15% 127.000us 1.15% 127.000us 127.000us 0.000us 0.00% 0.000us 0.000us 1
------------------------- ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------
Self CPU time total: 11.059ms
Self CUDA time total: 21.466ms
The profiler can also label individual sub-blocks of code, break out the data by input tensor shape, and export data as a Chrome tracing tools file. For full details of the API, see the documentation.
Advanced Topic: More Autograd Detail and the High-Level API¶
If you have a function with an n-dimensional input and m-dimensional output, \(\vec{y}=f(\vec{x})\), the complete gradient is a matrix of the derivative of every output with respect to every input, called the Jacobian:
If you have a second function, \(l=g\left(\vec{y}\right)\) that takes m-dimensional input (that is, the same dimensionality as the output above), and returns a scalar output, you can express its gradients with respect to \(\vec{y}\) as a column vector, \(v=\left(\begin{array}{ccc}\frac{\partial l}{\partial y_{1}} & \cdots & \frac{\partial l}{\partial y_{m}}\end{array}\right)^{T}\) - which is really just a one-column Jacobian.
More concretely, imagine the first function as your PyTorch model (with potentially many inputs and many outputs) and the second function as a loss function (with the model’s output as input, and the loss value as the scalar output).
If we multiply the first function’s Jacobian by the gradient of the second function, and apply the chain rule, we get:
Note: You could also use the equivalent operation \(v^{T}\cdot J\), and get back a row vector.
The resulting column vector is the gradient of the second function with respect to the inputs of the first - or in the case of our model and loss function, the gradient of the loss with respect to the model inputs.
``torch.autograd`` is an engine for computing these products. This is how we accumulate the gradients over the learning weights during the backward pass.
For this reason, the backward()
call can also take an optional
vector input. This vector represents a set of gradients over the tensor,
which are multiplied by the Jacobian of the autograd-traced tensor that
precedes it. Let’s try a specific example with a small vector:
x = torch.randn(3, requires_grad=True)
y = x * 2
while y.data.norm() < 1000:
y = y * 2
print(y)
tensor([-1648.1788, -466.4771, 603.6970], grad_fn=<MulBackward0>)
If we tried to call y.backward()
now, we’d get a runtime error and a
message that gradients can only be implicitly computed for scalar
outputs. For a multi-dimensional output, autograd expects us to provide
gradients for those three outputs that it can multiply into the
Jacobian:
v = torch.tensor([0.1, 1.0, 0.0001], dtype=torch.float) # stand-in for gradients
y.backward(v)
print(x.grad)
tensor([2.0480e+02, 2.0480e+03, 2.0480e-01])
(Note that the output gradients are all related to powers of two - which we’d expect from a repeated doubling operation.)
The High-Level API¶
There is an API on autograd that gives you direct access to important differential matrix and vector operations. In particular, it allows you to calculate the Jacobian and the Hessian matrices of a particular function for particular inputs. (The Hessian is like the Jacobian, but expresses all partial second derivatives.) It also provides methods for taking vector products with these matrices.
Let’s take the Jacobian of a simple function, evaluated for a 2 single-element inputs:
def exp_adder(x, y):
return 2 * x.exp() + 3 * y
inputs = (torch.rand(1), torch.rand(1)) # arguments for the function
print(inputs)
torch.autograd.functional.jacobian(exp_adder, inputs)
(tensor([0.3104]), tensor([0.4254]))
(tensor([[2.7280]]), tensor([[3.]]))
If you look closely, the first output should equal \(2e^x\) (since the derivative of \(e^x\) is \(e^x\)), and the second value should be 3.
You can, of course, do this with higher-order tensors:
inputs = (torch.rand(3), torch.rand(3)) # arguments for the function
print(inputs)
torch.autograd.functional.jacobian(exp_adder, inputs)
(tensor([0.0642, 0.1508, 0.3020]), tensor([0.0760, 0.8969, 0.6902]))
(tensor([[2.1326, 0.0000, 0.0000],
[0.0000, 2.3255, 0.0000],
[0.0000, 0.0000, 2.7051]]), tensor([[3., 0., 0.],
[0., 3., 0.],
[0., 0., 3.]]))
The torch.autograd.functional.hessian()
method works identically
(assuming your function is twice differentiable), but returns a matrix
of all second derivatives.
There is also a function to directly compute the vector-Jacobian product, if you provide the vector:
def do_some_doubling(x):
y = x * 2
while y.data.norm() < 1000:
y = y * 2
return y
inputs = torch.randn(3)
my_gradients = torch.tensor([0.1, 1.0, 0.0001])
torch.autograd.functional.vjp(do_some_doubling, inputs, v=my_gradients)
(tensor([ 924.4047, 668.9372, -1215.7491]), tensor([1.0240e+02, 1.0240e+03, 1.0240e-01]))
The torch.autograd.functional.jvp()
method performs the same matrix
multiplication as vjp()
with the operands reversed. The vhp()
and hvp()
methods do the same for a vector-Hessian product.
For more information, including performance notes on the docs for the functional API
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--- title: Jackpot! author: Jonathan Carroll date: 2016-01-13 15:18:22 slug: jackpot categories: [R] tags: [rstats] type: '' subtitle: '' image: '' ---
The powerball lottery in the USA has jackpotted to a first prize of $1.3 billion, which is just a silly amount of money.
The powerball lottery in the USA has jackpotted to a first prize of $1.3 billion, which is just a silly amount of money.
The cost of an entry (if you happen to be in the USA) is just $2, which is very much a ‘take a gamble’ sort of amount. If you’re an Aussie (except from SA like me) you can still have a go, but it will cost you considerably more ($10.50) and you’ll still have to pay the relevant US taxes if you win.
The following scenario has been raised a few times around the intertubes; if it costs $2 per ticket, the chances of winning (1/number of combinations of the drawn numbers) is 1/292,201,338, and the prize is over a billion dollars – why not buy one of every ticket and guarantee a win?
First, let’s look at the game.  There is also a pool of 26 powerballs from which 1 will be drawn. You need 5/5 + 1 to win the jackpot.
The odds of getting that right, if you recall your combinatorics, is one in
\[ \displaystyle{{69 \choose 5} \cdot {26 \choose 1} = 292,201,338}\ . \]
Doubling that makes buying all the tickets a mere $600 million or so. I’ll get my wallet.
This would be easy money if it weren’t for three important facts; first, the cash prize is actually $930 million if you take it right away, so we’re already out of pocket quite a bit. Second, you may need to split the jackpot with one or more people, meaning a significantly lower return, possibly less than you invested. Lastly, you also need to pay tax on the income, which is around 40% on that. Maybe it’s not such a good deal.
If you have one of every ticket however, you win every prize. How much does that get you? Back to combinatorics. To figure out how many combinations there are of each division we need to calculate the number of ways to get the number of correct and incorrect balls comparing our ticket to the draw, and multiply by the value of that prize.
So, for the next best prize (a mere $1 million) we need to have all 5 of the white balls but not the powerball on our ticket. There are 5 possibilities of white ball, and we need all 5 of them. We need to match one of the 25 non-winning powerballs too, so the number of matching combinations is
\[ \displaystyle{{5 \choose 5} \cdot {25 \choose 1} = 25}\ . \]
So, there are 25 ways in which we could do this (get all 5 of the white ball numbers on our ticket, but not the powerball). That means that if we have one of each ticket, 25 of them will be worth a million dollars each.
Continuing this logic the total winnings would be
\[ \begin{array}{lcl} {\rm WINNINGS} &=& 930,000,000\times{5 \choose 5} \cdot {1 \choose 1} \\ &+& 1,000,000\times{5 \choose 5} \cdot {25 \choose 1} \\ &+& 50,000\times{5 \choose 4} \cdot {64 \choose 1} \cdot {1 \choose 1} \\ &+& 100\times{5 \choose 4} \cdot {64 \choose 1} \cdot {25 \choose 1} \\ &+& 100\times{5 \choose 3} \cdot {64 \choose 2} \cdot {1 \choose 1} \\ &+& 7\times{5 \choose 3} \cdot {64 \choose 2} \cdot {25 \choose 1} \\ &+& 7\times{5 \choose 2} \cdot {64 \choose 3} \cdot {1 \choose 1} \\ &+& 4\times{5 \choose 1} \cdot {64 \choose 4} \cdot {1 \choose 1} \\ &+& 4\times{5 \choose 0} \cdot {64 \choose 5} \cdot {1 \choose 1}\end{array}\ . \]
or programmed as
winnings <- 930e6*1 + ## cash prize for jackpot, 1 winner
1e6*choose(5,5)*choose(25,1) + ## match 5 out of 5 white, don't match powerball
5e4*choose(5,4)*choose(69-5,1)*choose(1,1) + ## match 4 out of 5 white, match powerball
1e2*choose(5,4)*choose(69-5,1)*choose(25,1) + ## match 4 out of 5 white, don't match powerball
1e2*choose(5,3)*choose(69-5,2)*choose(1,1) + ## match 3 out of 5 white, match powerball
7*choose(5,3)*choose(69-5,2)*choose(25,1) + ## match 3 out of 5 white, don't match powerball
7*choose(5,2)*choose(69-5,3)*choose(1,1) + ## match 2 out of 5 white, match powerball
4*choose(5,1)*choose(69-5,4)*choose(1,1) + ## match 1 out of 5 white, match powerball
4*choose(5,0)*choose(69-5,5)*choose(1,1) ## match 0 out of 5 white, match powerball
prettyNum(winnings, big.mark=",")
## [1] "1,023,466,048"
So, winning all prizes all by yourself (everyone else who might have won the jackpot lost their tickets) nets you a little over a billion pre-tax dollars on its own. Not bad, but still pretty risky since you’re betting on not sharing.
The big question will be how big does the lottery need to get before this starts to look like a plausible option? The cost of tickets and total number of combinations are constants, so there must be some jackpot prize for which it’s a good bet to buy all the tickets, given that the chances of sharing don’t go up considerably (if you trust the FiveThirtyEight analysis of historical entries);
devtools::session_info()
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## tz Australia/Adelaide
## date 2019-08-13
##
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## xfun 0.8 2019-06-25 [1] CRAN (R 3.5.2)
## yaml 2.2.0 2018-07-25 [1] CRAN (R 3.5.1)
##
## [1] /home/jono/R/x86_64-pc-linux-gnu-library/3.5
## [2] /usr/local/lib/R/site-library
## [3] /usr/lib/R/site-library
## [4] /usr/lib/R/library


WQMY
Germanium (Ge) - Chemical properties, Health and Environmental ...
Germanium. Pure germanium is a hard, lustrous, gray-white, brittle metalloid. It has a diamondlike crystalline structure and it is similar in chemical and physical .
http://www.lenntech.com/periodic/elements/ge.htm
What is the formula for germanium oxide
Answer: chemical formula is GeO2. Its other names are germanic acid, G-15, and ACC10380. It forms as a passivation layer on pure germanium in contact with .
http://wiki.answers.com/Q/What_is_the_formula_for_germanium_oxide
Analysis of Germanium Hydride Molecular Clusters
Jan 28, 2011 . Key words: Germanium hydrides, Molecular clusters, Isotopomeric . Germanium also forms several compounds with 3D cyclic structures [7, 8], .
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3042106/
Contents |
[weather forecast in fuerteventura today] History
GERMANIUM TETRAIODIDE | 13450-95-8 - ChemicalBook
Visit ChemicalBook To find more GERMANIUM TETRAIODIDE(13450-95-8) information like chemical properties,Structure,melting point,boiling point,density .
http://www.chemicalbook.com/ChemicalProductProperty_EN_CB3161616.htm
Determination of the molecular structure of the largest metalloid ...
Determination of the molecular structure of the largest metalloid germanium cluster compound. Metalloid cluster compounds represent ideal model compounds .
http://ankaweb.fzk.de/website.php?page=science_micronano&id=4
Germanium dioxide | GeO2 | ChemSpider
Germanium dioxide. ChemSpider ID: 14112; Molecular Formula: GeO2; Average mass: 104.638802 Da; Monoisotopic mass: 105.911011 Da; Systematic name .
http://www.chemspider.com/Chemical-Structure.14112.html
Germanium dioxide - Wikipedia, the free encyclopedia
Germanium dioxide, also called germanium oxide and germania, is an inorganic compound, an oxide of germanium. Its chemical formula is GeO2. Other names .
http://en.wikipedia.org/wiki/Germanium_dioxide
Determination of the molecular structure of the largest... - ANKA - KIT
Determination of the molecular structure of the largest metalloid germanium cluster compound. Metalloid cluster compounds represent ideal model compounds .
http://www.anka.kit.edu/english/2380.php
Germanium | crystal structures - WebElements
WebElements » Chemistry » Periodic Table » Germanium » Crystal structures . ( space filling style). germaniumcrystal structure image (ball and stick style) .
http://www.webelements.com/germanium/crystal_structure.html
Chemical Elements.com - Germanium (Ge)
Germanium at Chemical Elements.com. Basic Information | Atomic Structure | Isotopes | Related Links | Citing This Page .
http://www.chemicalelements.com/elements/ge.html
Germanium tetraiodide - WebElements
. oxidation state 4. Crystal structure of germanium tetraiodide . A.F. Wells in Structural inorganic chemistry, 4th edition, Oxford, UK, 1975. J.D.H. Donnay, (ed.) .
http://www.webelements.com/compounds/germanium/germanium_tetraiodide.html
Germanium - Environmental Chemistry
Comprehensive data on the chemical element Germanium is provided on . Common Chemical Compounds of Germanium . Atomic Structure of Germanium .
http://environmentalchemistry.com/yogi/periodic/Ge.html
Organic Germanium - Douglas Laboratories
biologically active compound of the element germanium containing carbon, hydrogen and oxygen. Its chemical formula is (GeCH2CH2COOH)2O3. This .
http://douglaslabs.com/pdf/pds/ges.pdf
Monolayer Passivation of Ge(100) Surface via Nitridation and ...
a Department of Chemistry and Biochemistry, University of California at San Diego,. La Jolla . formed semi-ordered Ge-O structures and Ge ad-species at room .
http://kummelgroup.ucsd.edu/pubs/paper/108.pdf
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| Channel | Name | movies filmed in galveston texas | hunters glen apartments pinehurst nc | Programming |
|---|---|---|---|---|
| 53.1 | chemical structure of germanium DT | peter brown west virgina | create free animated banners education | Main chemical structure of germanium programming / MyNetworkTV |
| 53.2 | WOLF DT | pete kaufman oregon pigeons | elders of eastern kalimdor | Simulcast of patrick frey army reserve |
| 53.3 | WSWB DT | 480i | 4:3 | Simulcast of maine sunrise sunset table |
Synthesis and Molecular Structure of Germanium and Tin ...
Aug 20, 2006 . The reaction of 1,4-dilithiotetraphenylbutadiene with germanium(IV) chloride and tin(IV) chloride affords heterocycles Ph4C4GeCl2, I and .
http://www.tandfonline.com/doi/full/10.1080/13583140012331339109
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Response
public class Response
An HTTP response for an incoming request.
-
Headers to include with the response.
See also
HTTPHeadersDeclaration
Swift
public var headers: HTTPHeaders
-
Declaration
Swift
public var status: HTTPResponseStatus
-
The response body.
Declaration
Swift
public var body: Data
-
Sends the response back to the client. This will write the Response to the wire in it’s current state.
Note
You do not need to call this function directly if using one of thewrite()
functions to populate the response body.Declaration
Swift
public func send()
-
Writes a text string to the response body and sends it.
Note
This function will
send()
the response.Declaration
Swift
public func write(_ text: String)
Parameters
text
the response string
-
Encodes the given object as a JSON string, writes it to the response body, and sends it.
Note
This function will
send()
the response.Declaration
Swift
public func write<T>(json: T) where T : Decodable, T : Encodable
Parameters
json
The
Codable
to be encoded to JSON


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Urban Haiku
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Packet Switching is type of network in which relatively small units of data called packets are routed through a network based on the destination address contained within each packet.
Intergalatic Network refered to a computer networking concept similar to today’s internet.


“Disvā pāsādachāyāyaṁ,
caṅkamantaṁ naruttamaṁ;
Tattha naṁ upasaṅkamma,
vandissaṁ purisuttamaṁ.
I saw the supreme person
walking mindfully in the shade of the terrace,
so I approached,
and bowed to the supreme among men.
Ekaṁsaṁ cīvaraṁ katvā,
saṁharitvāna pāṇayo;
Anucaṅkamissaṁ virajaṁ,
sabbasattānamuttamaṁ.
Arranging my robe over one shoulder
and clasping my hands together,
I walked alongside that stainless one,
supreme among all beings.
Tato pañhe apucchi maṁ,
pañhānaṁ kovido vidū;
Acchambhī ca abhīto ca,
byākāsiṁ satthuno ahaṁ.
The wise one, expert in questions,
questioned me.
Brave and fearless,
I answered the teacher.
Vissajjitesu pañhesu,
anumodi tathāgato;
Bhikkhusaṅghaṁ viloketvā,
imamatthaṁ abhāsatha.
When all his questions were answered,
the Realized One congratulated me.
Looking around the mendicant Saṅgha,
he said the following:
‘Lābhā aṅgānaṁ magadhānaṁ,
Yesāyaṁ paribhuñjati;
Cīvaraṁ piṇḍapātañca,
Paccayaṁ sayanāsanaṁ;
Paccuṭṭhānañca sāmīciṁ,
Tesaṁ lābhāti cābravi.
“It is a blessing for the people of Aṅga and Magadha
that this person enjoys their
robe and almsfood,
requisites and lodgings,
their respect and service—
it’s a blessing for them,” he declared.
Ajjatagge maṁ sopāka,
dassanāyopasaṅkama;
Esā ceva te sopāka,
bhavatu upasampadā’.
“Sopāka, from this day on
you are invited to come and see me.
And Sopāka, let this
be your ordination.”
Jātiyā sattavassohaṁ,
laddhāna upasampadaṁ;
Dhāremi antimaṁ dehaṁ,
aho dhammasudhammatā”ti.
… Sopāko thero ….
At seven years old
I received ordination.
I bear my final body—
oh, the excellence of the teaching!


The Uberspect, JexlMethod, JexlPropertyGet and JexlPropertySet interfaces form the exposed face of introspective services.
The Uberspectimpl is the concrete class implementing the Uberspect interface. Deriving from this class is the preferred way of augmenting Jexl introspective capabilities when special needs to be fullfilled or when default behaviors need to be modified.


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Review: Spice World (1998) by Chris Webb copyright 1998
Cast: The Spice Girls, Alan Cummings, George Wendt, Richard E. Grant, Claire Rushbrook, Roger Moore. Directed by Bob Spiers
I am a Spice Girls fan, and am one of the few over eighteen who will admit that they indeed do have talent. Part of the Spice Girl backlash is that many feel they are simply pretty faces and became extremely popular extremely fast. I have no problem with that, yet they can sing, and their songs are enjoyable. They are entertainers, not a band, so in my mind, they do not have to play instruments to be legitimate.
Can they act though and do they deserve a movie? This could only be proven by watching "Spice World," which I did, and enjoyed considerably. Of course its silly and is not a filmmaking clinic, but it does what some of what are considered great films have failed to do: entertain.
To answer my first question, some Spice Girls are better actors than others, and the winner of most-talented Spice Girl goes to Melanie Chisholm (Sporty Spice). Her distinctive voice is highlighted in the song "Too Much," and is peppered throughout the rest. Yet, she can really act, as well, to the point where it felt as if she was being more than just a Spice Girl. Among the others, Melanie Brown (Scary Spice) and Geri Halliwell (Ginger Spice) act well, and Emma Bunton (Baby Spice) and Victoria Addams (Posh Spice) were given lesser roles in the scheme of things. You can really feel their bond and comfort with each other.
The plot basically takes us through the follies of the girls as they go from rehearsals to press conferences to one final big live show. Members of the press try to ruin them, and their managers are put to the test trying to keep things together. The tabloid run by Kevin McMaxford (Barry Humphries) is the weak link in this, as they try to show his evil through visual effects of thundering galestorms and streams of spittle as he talks. The managers, played adeptly by Richard E. Grant (Portrait of a Lady) to ridiculous extremes and Claire Rushbrook (Secrets and Lies) create some on-screen chemistry that Grant thwarts each time he talks to her. Roger Moore plays an omnipotent producer known as The Chief who carries on sight gags and mixes his cliches a la "Naked Gun."
Other appearances are made by George Wendt (Cheers) as a film producer and Alan Cummings (Circle of Friends, Emma) as a documentarian who both attempt to make films of the Spice Girls and their travails. What is churned out is a film about the making of a film about a film in which the Spice Girls are the main attraction. The Spice Girls sing their tunes to the delight of fans, stringing in what are essentially four or five music videos.
The Spice Girls show they are not all work when they help out a pregnant friend (Naoko Mori) who missed the Spice boat, but who supported them when they just got started. Cameos by Elton John, Elvis Costello, and Bob Geldolf are thrown in, but the movie is at its strongest when the girls themselves are on screen.
I was expecting it to be dumb, but there was even some adult humor in there that kept me going, and it never disappointed me. If you're a fan, you'll enjoy the movie. If you're not, then don't go, because we don't need to hear yet another person mocking them because that is the popular thing to do.
My grade: B+/A-
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