Push state actors to provide information – Lecturer urges media
Lecturer at the Ghana Institute of Journalism (GIJ), Mr. Zakaria Tanko Musa has urged the media to push for information at state institutions and outfits.
According to the lecturer, with the passage of the Right to Information (RTI) law journalists have every right to demand every information they may need.
This information, he noted may aid the public to know how these state outfits function or operate.
Addressing the media in Kumasi at a forum put together by the Media Foundation for West Africa (MFWA), Mr. Zakaria said the RTI law empowers every journalist to ask or seek for information they may need from public outfits so that they can properly educate and inform the public on same.
”It is now your right to demand every information that will aid you in your profession so that you can also adequately and appropriately educate and pass knowledge to the public”, the lecturer posited.
CONFIDENCE:
Mr. Zakaria, a lawyer in addition to his teaching profession tasked the media to go for information from the right sources at the public outfits they may choose to visit.
This, he noted will enable them to adequately plot their stories from informed positions.
Knowing and having the right information, the GIJ lecturer mentioned will enable journalists to write or report confidently about the subject they may be dwelling on.
He stressed that the media will be spared the agony of twisting facts when they have all the right information from outfits they may be writing about or institutions they may be investigating.
RESPONSES:
Hafiz Tijani, a journalist with Citi FM told the Ghanaian Observer newspaper that the RTI will empower the media in its work.
He noted that nothing is as good as having the right and accurate information to write or report on.
Oheneba Nana Asiedu, morning show host at Fox 97.9 FM in Kumasi expressed delight at the opportunity to demand information from public outfits.
He said these state and public institutions have for long denied the media access to their operations and noted that even when one had information about a wrong act being done at these places the media were blocked from having the full facts.


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A number of different motives for drinking alcohol🍾 have been examined, including drinking to enhance sociability, to increase power, to escape problems, to get drunk, for enjoyment, or for ritualistic reasons. Despite this diversity, most research has focused on two broad categories of motivation.
Stop Imitating
Envying someone else’s gift will cause you to neglect your own.
Chances are the person you’re imitating didn’t become a great leader by mimicking someone else. Far more likely, they developed the gifts God gave them to their fullest potential.
Which leads us to the first problem with the constant imitation of leaders: Envying someone else’s gift will cause you to neglect your own.


PtoMYB secondary wall Fig. poplar lignin transcription Plant cell biosynthesis plants transgenic formation Arabidopsis MYB expression gene genes biosynthetic analysis factors phenylpropanoid transcriptional Supplementary showed {border-style AtMYB px;} regulation using #ffffff;} involved wood °C Zhong Cell Table ZmMYB control promoters including lines pathway tissues GUS biosynthesis. cells compared leaves poplar. (Potri.G.), Populus S). Wang described performed previously reporter target vector EgMYB NAC Physiol. PtoMYB, activity amplified content different knockout plant promoter tobacco tomentosa used B). PCR PtrMYB RT-PCR assays domain extracted gene-specific negatively protein px; repressor repressors resulted sequence wild-type xylem {font-family ml AmMYB C). Liu Sci. Zhang acid demonstrated direct fibers font-size method negative primers repressed repression results sequences stem study, total A). Agrobacterium-mediated CRISPR/Cas DNA Mol. Overexpression PdMYB PtrCH RR-MYB Total able cross-sections deposition detected directly double; factor fused indicated measured plants. poplar, products reduced reduction regulates repress significantly switches walls C-terminal Lee McCarthy PtoMYB-KO PtrGTB Quantitative RNA Transcriptional Yang amino based binary bind binding border-color border-width cellulose characterization construct determine ectopic encoding factors, followed function functional independent indicating master metabolism observed overexpression phenolic regulate regulating regulator resulting revealed sgRNA shown significant sites solid; staining study transformation xylan {font-style {font-weight (w/v) -month-old Arabidopsis, AtMYB, BY- Biol. Biotechnol. CH, Carr. Chen D). E). Eucalyptus Fan However, New Phytol. Rep. accumulation activators activities affects biological bold coding courier; determined development driven effector em; extraction flavonoid functions genetic greenhouse growth increase key line liquid network obtained phloem plants, program pt;} quantitative region represses solution stems structural transient wood-associated yeast


We have an ambitious goal, but to accomplish it, we need to take smalls steps in order to guarantee the user security and well functioning of the platform built on top of the ethereum smart contracts.
Our final objective, should include a full decentralized bet platform, on where users can create and decide from where results should be fetched from, using external trusted oracles. By now, we aim to test how well a single game will be handled by the users, including the feedback given its taxes from Ethereum transaction fees, and also, check for relevant vulnerabilities that the bug bounty program can expose with the help of the community
This is a follow up of previous research and work from the Bitkup—Etherkup team, that can enable the creation and innovation on multiple opportunities that we are currently involved, ranging from real state to political research.
Prior to the launch on the Mainnet, we are going to update fractions of the Alpha and possible features on the Ropsten Ethereum testnet, and as we feel comfortable and secure with a live production version of our platform with the help of the community feedback, you will be notified and invited to gamble with us.
This is currently an open source project that you can contribute and get involved with, and we are pleased to welcome people that aim to learn and create disruptive things. We are also currently developing private related projects that you can know more about in touch with us on our slack channel at Bitkup; feel free to come by and say hi.


AWS::EC2::NetworkAclEntry
Creates an entry (i.e., a rule) in a network ACL with a rule number you specify. Each network ACL has a set of numbered ingress rules and a separate set of numbered egress rules.
Syntax
To declare this entity in your AWS CloudFormation template, use the following syntax:
JSON
{ "Type" : "AWS::EC2::NetworkAclEntry", "Properties" : { "CidrBlock" :
String
, "Egress" :Boolean
, "Icmp" :EC2 ICMP
, "Ipv6CidrBlock" :String
, "NetworkAclId" :String
, "PortRange" :EC2 PortRange
, "Protocol" :Integer
, "RuleAction" :String
, "RuleNumber" :Integer
} }
YAML
Type: AWS::EC2::NetworkAclEntry Properties: CidrBlock:
String
Egress:Boolean
Icmp:EC2 ICMP
Ipv6CidrBlock:String
NetworkAclId:String
PortRange:EC2 PortRange
Protocol:Integer
RuleAction :String
RuleNumber :Integer
Properties
CidrBlock
-
The IPv4 CIDR range to allow or deny, in CIDR notation (e.g., <IP_ADDRESS>/24).
Required: Conditional. You must specify the
CidrBlock
orIpv6CidrBlock
property.Type: String
Update requires: No interruption
Egress
-
Whether this rule applies to egress traffic from the subnet (
true
) or ingress traffic to the subnet (false
). By default, AWS CloudFormation specifiesfalse
.Required: No
Type: Boolean
Update requires: Replacement.
Icmp
-
The Internet Control Message Protocol (ICMP) code and type.
Required: Conditional required if specifying 1 (ICMP) for the protocol parameter.
Type: EC2 NetworkAclEntry Icmp
Update requires: No interruption
Ipv6CidrBlock
-
The IPv6 CIDR range to allow or deny, in CIDR notation.
Required: Conditional. You must specify the
CidrBlock
orIpv6CidrBlock
property.Type: String
Update requires: No interruption
NetworkAclId
-
ID of the ACL where the entry will be created.
Required: Yes
Type: String
Update requires: Replacement.
PortRange
-
The range of port numbers for the UDP/TCP protocol.
Required: Conditional Required if specifying 6 (TCP) or 17 (UDP) for the protocol parameter.
Type: EC2 NetworkAclEntry PortRange
Update requires: No interruption
Protocol
-
The IP protocol that the rule applies to. You must specify
-1
or a protocol number (go to Protocol Numbers at iana.org). You can specify-1
for all protocols.Note
If you specify
-1
, all ports are opened and thePortRange
property is ignored.Required: Yes
Type: Number
Update requires: No interruption
RuleAction
-
Whether to allow or deny traffic that matches the rule; valid values are "allow" or "deny".
Required: Yes
Type: String
Update requires: No interruption
RuleNumber
-
Rule number to assign to the entry, such as
100
. ACL entries are processed in ascending order by rule number. Entries can't use the same rule number unless one is an egress rule and the other is an ingress rule. For valid values, see the CreateNetworkAclEntry action in the Amazon EC2 API Reference.Required: Yes
Type: Number
Update requires: Replacement.
Return Values
Ref
When the logical ID of this resource is provided to the Ref
intrinsic
function, Ref
returns the resource name.
For more information about using the Ref
function, see Ref.
Example
JSON
{ "AWSTemplateFormatVersion" : "2010-09-09", "Resources" : { "myNetworkAclEntry" : { "Type" : "AWS::EC2::NetworkAclEntry", "Properties" : { "NetworkAclId" : { "Ref" : "myNetworkAcl" }, "RuleNumber" : "100", "Protocol" : "-1", "RuleAction" : "allow", "Egress" : "true", "CidrBlock" : "<IP_ADDRESS>/24", "Icmp" : { "Code" : "-1", "Type" : "-1" }, "PortRange" : { "From" : "53", "To" : "53" } } } } }
YAML
AWSTemplateFormatVersion: '2010-09-09' Resources: myNetworkAclEntry: Type: AWS::EC2::NetworkAclEntry Properties: NetworkAclId: Ref: myNetworkAcl RuleNumber: '100' Protocol: "-1" RuleAction: allow Egress: 'true' CidrBlock: <IP_ADDRESS>/24 Icmp: Code: "-1" Type: "-1" PortRange: From: '53' To: '53'
See Also
-
NetworkAclEntry in the Amazon EC2 API Reference
-
Network ACLs in the Amazon Virtual Private Cloud User Guide.


I was prescribed 100 milligrams of Trazodone to help with insomnia, and to alleviate mild anxiety/depression. The first night I took a dose (on an empty stomach), all my thoughts were focused on getting to bed and sleeping. And of course, this is what happened. Before I got to bed though, I suspected that I would be drowsy in the morning.  There was no way I could go back to sleep.
About two months after starting the drug, I started to see positive benefits. I could go out with friends more, I was making better grades, and was all around happier. Also, I could smoke weed again without running the risk of severe anxiety or too much introspection which would trigger a depressed state.
One time, my bottle ran out and I couldn't get to the pharmacy for a while. After three nights of not having my regular dosage, I was back to where I was without the drug, possibly even worse off. On that fourth night, I took my dose again.  It took no more that two nights for the drug to not rip me apart.
I often eat a small snack right before I take it. Once I take it, I have 10-20 minutes to be ready for bed. If I am not in bed at this time, I will feel like I have an inner-ear infection. It can occasionally cause breathing problems; I typically alleviate this by using my albuterol inhaler. This drug allows me to go to sleep at the same time every night, and wake up at the same time every morning. Its wonderful.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


David Nugent’s promotion knowledge from the Championship will be vital
by Dave SeddonDavid Nugent’s game time at Preston might have been limited so far but Alex Neil thinks the striker’s vast experience will be key as the season goes on.
The 34-year-old will be in PNE’s squad for today’s clash with his former club Derby County at Pride Park.
He spent two-and-a-half years with the Rams, scoring 17 goals, and when they freed him in the summer Nugent made the switch to Deepdale on a Bosman.
Since his return, Nugent has started three games in the Championship and come off the bench three times.
In the League Cup match against Manchester City, he also got a run as a substitute.
Neil said: “It’s good when you have got someone who is used to winning, used to being in the play-offs, used to getting promoted.
“Just to have that experience for the younger players to lean on is really important.
“There is a formula for getting promoted which is difficult to put your finger on, there’s a certain feeling you need to get – a determination and hunger.
“The more you have felt that, the more you want to recreate it.
“It is no surprise that you get guys who go from club to club and get promoted with them.
“Gary O’Neil who I had at Norwich has had promotions a few times, teams look at him and think ‘We’ll have a bit of that, he knows how to do it’.
“Nuge fits into the same sort of category. While I think he is having an influence now, if it does start to heat up for us, he will have more and more influence.
“That is when the pressure kicks in and it is how you handle the pressure that matters.

Nugent, Paul Gallagher, Paul Huntington and Tom Clarke are the senior heads in the squad, Neil says striking a balance in age is important.
“While these lads have been brilliant for us, if you had 20 of them in the squad there would be a problem,” said Neil.
“When you’ve got Nuge, Gally, Tom and big Hunts who know what it takes and have been around the block, it is great to have them.”
Nugent and Huntington both played in a midweek bounce game at Burnley to boost their fitness, together with several other first-team squad players.
That was arranged after the Lancashire Senior Cup tie with Barrow scheduled for last week was forfeited by the Cumbrian side.
Said Neil: “Ideally we wouldn’t have had a game this week but we took the game to make sure some of the lads got minutes into their legs to keep at the right level.”


The Taste Of India
Location: Fremont, California
Open Hours: 6:00 PM - 12:00 AM
Menu
Appetizers
- Hara-Bhara Kebab ............... $6.69
- Deep fried spicy snack made with potatoes and spinach
- Paneer Chilly ............... $7.79
- Indian cottage cheese stir-fry in soya sauce with onions, peppers and chillies
- Chese Balls ............... $6.69
- cubed pieces of real mozzarella cheese dipped in breadscrum with Hot Pepper
- Dry Manchurian ............... $8.89
- Golden fried vegetable dumplings tossed with a spicy manchurian sauce
Curries
- Paneer Tikka .................$12.99
- Homemade cheese chunks marinated in Indian spices and slow-cooked in a tandoor
- Paneer Tufani .................$13.49
- Spicy curry made of cottage cheese mixed with veggies
- Malai Kofta .................$15.49
- Mixed vegetable croquettes. Cooked in a creamy sauce w/ cashews and raisins
- Cheese Butter Masala .................$14.39
- Cheese grilled in tandoor, then cooked in a tomato based gravy with a hint of butter and cream
- Mix vegetables .................$13.99
- Diced carrot and potato, green peas, beans, and capsicum, tossed in butter and cooked in a tomato and cashew nut gravy
Breads
- Naan ............. $2.39
- Made with plain floor and cooked in tandoor
- Aloo Kulcha ............. $3.19
- Made by stuffing potato and cooked in tandoor
- Missi Roti ............. $2.39
- Indian bread made with whole-wheat flour and butters
- Garlic Naan ............. $2.29
- Delicious naan bread topped with chopped garlic
- Chese Naan ............. $4.59
- Naan stuffed with cottage cheese and mild spices
Rice
- Haydrabadi Biryani ................... $10.99
- Speciallity of South India, made with rice and veggies
- Veg Biryani ................... $9.99
- stir fried peas, green beans, carrots, cauliflower, broccoli, basmati rice w/herbs and spices
- Veg Pulav ................... $8.99
- Rice fried with onions, potatoes and carrots
- Masala Khichdi ................... $8.69
- Popular Gujarati rice dish made with indian spices
- Chese Pulav ................... $9.39
- Rice mixed with vegetables and cheese
Desserts
- Gulab jamun .................... $6.79
- Fried golden brown balls, served in sugar syrup.
- Rasgulla .................... $7.29
- Steamed balls made with milk and soaked in sugar syrup.
- Shrikhand .................... $8.29
- Made with hung curd
- Fruit Salad .................... $6.39
- Creamy custord milk made with various fruits
- Kheer .................... $5.99
- Creamy rice pudding garnished with almonds and pistachios.


Salario: {{selectedJob.salary | currency:'RD$':2}} Tipo: {{selectedJob.type}} Horario: {{selectedJob.schedule}}


|
geopm
|
#include <ApplicationSamplerImp.hpp>
Classes | |
| struct | m_process_s |
Public Member Functions | |
| ApplicationSamplerImp () | |
| ApplicationSamplerImp (std::shared_ptr< ApplicationStatus > status, const PlatformTopo &platform_topo, const std::map< int, m_process_s > &process_map, bool is_filtered, const std::string &filter_name, const std::vector< bool > &is_cpu_active) | |
| virtual | ~ApplicationSamplerImp () |
| void | time_zero (const geopm_time_s &start_time) override |
| Set the reference time that will be used for all future record time reporting. More... | |
| void | update (const geopm_time_s &curr_time) override |
| Update the record buffer by clearing out old records and providing a new cache for subsequent calls to the get_records() method. Also update cache of application status for use by hint and progress APIs. More... | |
| std::vector< record_s > | get_records (void) const override |
| Get all of the application events that have been recorded since the last call to update_records(). More... | |
| short_region_s | get_short_region (uint64_t event_signal) const override |
| std::map< uint64_t, std::string > | get_name_map (uint64_t name_key) const override |
| Called after observing a EVENT_NAME_KEY event to get a map from any hash returned in a previous record to the string that generated the hash. The result includes the names of all entered regions and the profile name. More... | |
| uint64_t | cpu_region_hash (int cpu_idx) const override |
| Get the region hash associated with a CPU. More... | |
| uint64_t | cpu_hint (int cpu_idx) const override |
| Get the hint associated with a CPU. More... | |
| double | cpu_hint_time (int cpu_idx, uint64_t hint) const override |
| Get the amount of time a CPU has been measured running with a hint. More... | |
| double | cpu_progress (int cpu_idx) const override |
| Get the progress reported on a CPU. More... | |
| std::vector< int > | per_cpu_process (void) const override |
| Get the process ID for each linux logical CPU. More... | |
| void | connect (const std::string &shm_key) override |
| Connect with an application using a key. More... | |
| int | sampler_cpu (void) |
| void | set_sampler (std::shared_ptr< ProfileSampler > sampler) override |
| std::shared_ptr< ProfileSampler > | get_sampler (void) override |
Additional Inherited Members | |
| Static Public Member Functions inherited from geopm::ApplicationSampler | |
| static ApplicationSampler & | application_sampler (void) |
| Singleton accessor for the application sampler. More... | |
| static std::set< uint64_t > | region_hash_network (void) |
| Returns set of region hashes associated with application network functions. More... | |
| Protected Member Functions inherited from geopm::ApplicationSampler | |
| virtual | ~ApplicationSampler ()=default |
| ApplicationSampler ()=default | |
| geopm::ApplicationSamplerImp::ApplicationSamplerImp | ( | ) |
| geopm::ApplicationSamplerImp::ApplicationSamplerImp | ( | std::shared_ptr< ApplicationStatus > | status, |
| const PlatformTopo & | platform_topo, | ||
| const std::map< int, m_process_s > & | process_map, | ||
| bool | is_filtered, | ||
| const std::string & | filter_name, | ||
| const std::vector< bool > & | is_cpu_active | ||
| ) |
|
virtual |
|
overridevirtual |
Connect with an application using a key.
Called by the Controller to set up all channels of communication with the application using the provided string as a key. This string corresponds to the –geopm-shmkey geopmlaunch option or the GEOPM_SHMKEY environment variable used by the application that is connected.
| [in] | app_key | String known to the application that will be used to identify a channel of communication. |
Implements geopm::ApplicationSampler.
|
overridevirtual |
Get the hint associated with a CPU.
Returns the most recently sampled value for the hint associated with the Linux logical CPU specified by the user. An exception is raised if the value of cpu_idx is negative or greater or equal to platform_topo().num_domain(GEOPM_DOMAIN_CPU).
| [in] | cpu_idx | The index of the linux logical CPU to query. |
Implements geopm::ApplicationSampler.
|
overridevirtual |
Get the amount of time a CPU has been measured running with a hint.
Returns a total amount of time in seconds that a CPU was measured to be running with the hint value on the Linux logical CPU specified by the user. An exception is raised if the value of cpu_idx is negative or greater or equal to platform_topo().num_domain(GEOPM_DOMAIN_CPU) or if the specified hint is invalid.
| [in] | cpu_idx | The index of the linux logical CPU to query. |
Implements geopm::ApplicationSampler.
|
overridevirtual |
Get the progress reported on a CPU.
Returns the most recently sampled value for the fraction of the work units completed by the thread running on the specified CPU. If the CPU queried is not currently executing a thread that is reporting progress, then the value NAN is returned. An exception is raised if the value of cpu_idx is negative or greater or equal to platform_topo().num_domain(GEOPM_DOMAIN_CPU).
| [in] | cpu_idx | The index of the linux logical CPU to query. |
Implements geopm::ApplicationSampler.
|
overridevirtual |
Get the region hash associated with a CPU.
Returns the most recently sampled value for the region hash associated with the Linux logical CPU specified by the user. An exception is raised if the value of cpu_idx is negative or greater or equal to platform_topo().num_domain(GEOPM_DOMAIN_CPU).
| [in] | cpu_idx | The index of the linux logical CPU to query. |
Implements geopm::ApplicationSampler.
|
overridevirtual |
Called after observing a EVENT_NAME_KEY event to get a map from any hash returned in a previous record to the string that generated the hash. The result includes the names of all entered regions and the profile name.
| [in] | name_key | The signal from the name key event record. |
Implements geopm::ApplicationSampler.
|
overridevirtual |
Get all of the application events that have been recorded since the last call to update_records().
Implements geopm::ApplicationSampler.
|
overridevirtual |
Implements geopm::ApplicationSampler.
|
overridevirtual |
Implements geopm::ApplicationSampler.
|
overridevirtual |
Get the process ID for each linux logical CPU.
A vector of length the number of CPUs on the system is returned. If a CPU has been claimed by a process, the returned value in the vector corresponding to the CPU will be the process identifier. If a CPU is unclaimed the value will be populated with -1.
Implements geopm::ApplicationSampler.
| int geopm::ApplicationSamplerImp::sampler_cpu | ( | void | ) |
|
overridevirtual |
Implements geopm::ApplicationSampler.
|
overridevirtual |
Set the reference time that will be used for all future record time reporting.
| [in] | start_time | The reference zero time. |
Implements geopm::ApplicationSampler.
|
overridevirtual |
Update the record buffer by clearing out old records and providing a new cache for subsequent calls to the get_records() method. Also update cache of application status for use by hint and progress APIs.
Implements geopm::ApplicationSampler.


{%include "sidebar_"+CURRENT_TAB+".html"%}
| Edit Job | ||
| Job Details | ||
|---|---|---|
| Select For Working | View Images | Delete Job |
| {{pair[0]}} |
|
|
| Select For Working | View Images | Delete Job |


{% extends "base_hub.html" %} {% block content %}
Here you can find jobs, or create new job requests.


Zhengwei Bai
Ph.D. Student, in Electrical and Computer Engineering, University of California, Riverside
Transportation System Research (TSR) Group at Center for Environmental Research and Technology
Education:
M.S., Electrical Information and Engineering, Beijing Jiaotong University
B.E., Electrical Information and Engineering, Beijing Jiaotong University
I am currently a 2nd-year Ph.D. student in Electrical and Computer Engineering at UC, Riverside, co-advised by
Dr. Matthew J. Barth and
Dr. Guoyuan Wu. Before I started my Ph.D. study at UCR,
I received my Master's and Bachalor's degrees under the supervision of
Dr. Baigen Cai and Dr. Wei Shangguan at Beijing Jiaotong University.
My current research mainly focus on computer vision for enabling autonomous driving, e.g., 3D object detection, cooperative perception, multi-object tracking, etc.
Below are the strengths of mine:
1084 Columbia Avenue
Riverside, CA 92507
Email:
<EMAIL_ADDRESS>Find me at:
ResearchGate
GoogleScholar
YouTube
Slected Publications
Infrastructure-Based Object Detection and Tracking for Cooperative Driving Automation: A Survey
Zhengwei Bai , Guoyuan Wu, Xuewei Qi, Yongkang Liu, Kentaro Oguchi, Matthew J. Barth
arXiv preprint arXiv:2201.11871
[Paper], [CITE]
Cyber Mobility Mirror for Enabling Cooperative Driving Automation: A Co-Simulation Platform
Zhengwei Bai , Guoyuan Wu, Xuewei Qi, Kentaro Oguchi, Matthew J. Barth
101st Annual Meeting for Transportation Research Board (TRB2022)
[Paper], [CITE]
Hybrid Reinforcement Learning-Based Eco-Driving Strategy for Connected and Automated Vehicles at Signalized Intersections
Zhengwei Bai , Peng Hao, Wei Shangguan, Baigen Cai, Matthew J. Barth
System Architecture, Simulator View, Intersection View,
Accepted by IEEE Transactions on Intelligent Transportation Systems , 2022, doi: 10.1109/TITS.2022.3145798.
[Results Video with introduction on YouTube.]
Deep Reinforcement Learning Based High-level Driving Behavior Decision-making under Heterougeneous Traffic
Zhengwei Bai , Baigen Cai, Wei Shangguan, Linguo Chai
[Paper Link], [PDF], [CITE]
Accepted by Chinese Control Conference 2019
arXiv:1902.05772v2 [cs.LG]
[Results Video]
Deep Learning Based Motion Planning For Autonomous Vehicle Using Spatiotemporal LSTM Network
Zhengwei Bai , Baigen Cai, Wei Shangguan, Linguo Chai
[Paper Link], [PDF], [CITE]
2018 Chinese Automation Congress (CAC), Xi'an, China. , doi: 10.1109/CAC.2018.8623233
Oral presentation on the session of Unmanned Control System, [PPT]
[Results Video]
Design and Implementation of Novel Wireless Field Strength Test System
Zhengwei Bai
[Project Introduction]
Undergraduate Thesis
MAJOR PROJECTS
Cyber Mobility Mirror (CMM)
Advisors: Dr. Guoyuan Wu, Dr. Xuewei Qi, Dr. Yongkang Liu, Dr. Kentaro Oguchi, Dr. Wei Matthew Barth
We focus on building a 3D reconstruction mirror in cyber world based on the perception information from traffic mobilities to support cooperative driving automation (CDA). My research mainly focus on object detection and cooperative perception, which are briefly introduced as follows:
- To propose 3D object detection, classification, tracking and reconstruction methods.
- To build up a real-time field operational system to perceive the traffic objects and distribute and display the reconstruction information for enabling CDA.
- To develop different simulation paltform to support various kinds of perception requirements and model training.
Reinforcement Learning Based Eco-Approach and Departure (EAD) System.
Advisors: Dr. Peng Hao, Dr. Guoyuan Wu, Prof. Wei Matthew Barth
My focus is to propose a RL-based eco-driving strategy under intersections with mixed traffic. My main contributions are shown as follows:
- Propose a reinforcement learning framework that can deal with logically complex driving task, such as passing a signalized intersection with mixed traffic.
- Design and Develop a Unity-based reinforcement learning simulator: signalized intersection scenario with CAVs and human-driven vehicles.
- Design a time-efficient and eco-friendly driving strategy for CAVs by combining vision data, on-board sensor data and V2I communication.
Intelligent Transportation Information Management System
Advisors: Prof. Baigen Cai, Prof. Wei Shangguan, Dr. Dakai Yang
A large-scale transportation information management project I worked on in 2018. My focus is to design and develop several software modules including applications and APIs. My main contributions are shown as follows:
- Designed and Developed Five Software Modules of the ITIMS including bus, taxi, intersection, traffic signal,s and flow detection modules
- Developed several high-reliable multi-source data API (Application Programming Interface) which connects to major transportation information database such as The Traffic Police Database in Zhuhai.
- Management and maintenance overall service infrastructure utilizing remote controller(Using Teamviewer, Xshell and Xftp), Oracle & Mysql database.
Machine Learning Based Autonomous Vehicle Control Methods
Advisors: Prof. Baigen Cai, Prof. Wei Shangguan, Dr. Linguo Chai
A large-scale heterougeneous traffic simulation project I am currently working since 2018. My focus is to explore robust and accurate control methods for CAV in heterougeneous traffic situations. My main contributions are shown as follows:
- Proposed a deep neural network called spatiotemporal LSTM to generalize the steering angle output by fitting in the raw image data.
- Proposed a deep Reinforcement Learning (deep RL) based high-level driving behavior decision-making algorithm.
- Developed a simulation environment based on the Unity3D Engine for the training and testing processes of the deep RL algorithm.
High-speed Railway Based BeiDou Fusion Positioning Performance Test
Advisors: Prof. Baigen Cai, Prof. Debiao Lu, Prof. Wei Jiang
A testing about the performance of the GPS, BeiDou and IMU fusion positioning under high-speed motion scenario (350km/h). My main contributions are shown as follows:
- Setup the test environment on the train and using device SPAN, UB380 recorded IMU and GPS/BeiDou information separately.
- Designed the automatic driving test scenarios based on site characteristics (such as the car-flowing scenario, overtaking scenario, obstacles recognition, etc).
- Wrote the most part (over 80%) of the whole proposal (20134 words in total) and made a presentation to the local transportation department.
China Railway Urumqi Railway Administration Group Co.,Ltd – Field Strength Test system
Advisors: Prof. Baigen Cai, Prof. Wei Shangguan
A Field Strength Test System project that I worked on my last year of Undergraduate (2017). The main purpose of the project is to design and develope a system to test, record, and analyze the wireless field strength along the railway. My main contributions are shown as follows:
- Design and developed a MFC framwork based windows software (about 15000 lines of C++ code) for the novel wireless field strength test (FST) system .
- Developed and tested the hardware system (collecting and packaging the sensor data such as ODO, GPS and TAX) of the FST system.
- Testing the whole FST system between Urumqi railway station and Akesu railway station (2018km in total).


--- layout: post status: publish published: true title: BasicHttpBinding with Transport Security author: display_name: Tomas Restrepo login: tomasr email<EMAIL_ADDRESS>url: http://winterdom.com/ author_login: tomasr author_email<EMAIL_ADDRESS>author_url: http://winterdom.com/ wordpress_id: 141 wordpress_url: http://winterdom.com/2007/11/basichttpbindingwithtransportsecurity date: '2007-11-14 07:10:46 +0000' date_gmt: '2007-11-14 07:10:46 +0000' categories: - WCF - Web Services tags: [] comments: [] ---
This is just something to remind myself next time I need to implement a WCF service using the BasicHttpBinding with transport-only security (SSL + client certificates)':
To use client certificates:
<bindings>
<customBinding>
<binding name="sslBasicHttp">
<textMessageEncoding messageVersion="Soap11" />
<httpsTransport requireClientCertificate="true"/>
binding>
customBinding>
bindings>


|
| Local Properties | ||
| Managed Object Types | Data Object Types | All Properties | All Methods |
Properties
| Name | Type | Description |
|---|---|---|
| error* | LocalizedMethodFault |
Set if the job aborted with failure. If it is not set, it is either still
running or succeeded. See success property.
|
| message | xsd:string |
Status message about current operation of the background job.
|
| phase | xsd:string |
The overall deploy is divided into multiple phases, e.g. validation,
vsanbootstrap, vcsadeploy, vcconfig, etc. See VimVsanVsanVcsaDeploymentPhase |
| progressPct | xsd:long |
Overall progress of the job in percent.
|
| success | xsd:boolean |
Set if the job completed successfully. If it is not set, it is either still
running or failed. See error property.
|
| taskId* | xsd:string |
Unique identifier of the task this progress report is for.
|
| updateCounter | xsd:long |
Any time the progress changes this counter is incremented. This way a client
can determine if the progress has changed when retrieving the progress at
different points in time. Never negative.
|
| vm* | ManagedObjectReference
to a VirtualMachine |
Virtual Machine that was created.
|
| Properties inherited from DynamicData | ||
| None |
| Top of page | Local Properties | ||
| Managed Object Types | Data Object Types | All Properties | All Methods |


{% extends 'inspector/form_base.html' %} {% block title %}Select Probing Tasks - {{ block.super }}{% endblock %} {% block form %}
Every language supports a different set of probing tasks. Probing time will increase slightly with each selected task. Learn more.


Today, I am going to discuss emotional conflict. In the world of a Software Engineer, it is important to be able to work well with others. In the event that a conflict should arise, it is also important to know how you handle conflicts. Without knowing how you handle shame and conflict, you have no way to gauge how effectively you deal with problems. So lets go on a journey and get to "know thyself."
Roughly a year ago, I was in Atlanta, visiting my sister. I rarely get to see her since I live in San Diego, CA, but when I do we usually have a good time. She just had her second child, so I am now an uncle x2. So, you would think this trip would only be a celebratory trip. However, the first night I arrive, we get into a pretty big argument.
My parents got divorced when I was in High School and since the divorce, my Sister and Mother have had a few rough patches. So after she put the kids to bed, my Sister started bashing my Mom pretty hardcore. I took offense to this because I have always been extremely close with my Mom (I guess that makes me a Momma's boy?). Without getting very specific my Sister made some very distasteful comments and was questioning the character of our Mom.
Needless to say, I was pissed. Initially, I just tried to reason with her and change her mind, but that was useless. After that failed, I tried to change the subject..fail. Eventually, after carrying out this discussion for over an hour, I lost my cool and got started to attack her character for attacking my Mom (not very constructive, I know). After arguing back and forth for awhile, we both went to bed angry.
I'll save the remainder of the story for another post. I can openly say that I did not handle the conflict properly. Any conflict that ends in either individual losing their cool is not a successful interaction. I typically don't like to examine what could have been, but in this case I will. If I could go back and handle this situation differently, I would make sure that I never chose words that would attack my Sister. By becoming verbally agressive with my Sister, I was only throwing fuel on the fire. Moving forward, I need to take a deep breath in the moments when conflicts arise and come up with a more productive way of dealing with the conflict.
After completing this exercise, I started thinking about the typical way that I handle shame. Usually, I am able to handle shame pretty well and I rarely let it get to me for an extended period of time. For example, if I am rejected in some fashion or treated rudely by someone that I respect I tend to handle it quite well. While it may sting in the immediate, I am generally able to reflect on this experience over time and get over it in a reasonable amount of time.
After analyzing the Compass of Shame, I have come to the conclusion that I used to practice a combination of these defense mechanisms. During my most shameful moments, I have practiced withdrawal, avoidance, and self-attack (never physical). As I have grown through experience, I have learned what is worth fretting about and what isn't. Generally, most of the 'shameful' things that once would lead me to react, no longer do. I would like to think that I am well-adjusted and that I can draw on experience to handle shame properly.
As an adult, if I were to experience a shameful event, I would most likely try to handle it in the healthiest way possible without hurting anyone (myself included). In the event that I cannot deal with the shame in a healthy way, the defense mechanism that I would most likely choose is Avoidance. While this may not be the healthiest way to get over a shameful experience, it allows me to remain social and still go on living my life until I can effectively deal with the problem. Plus, I am not against going skydiving and having an adult beverage to help forget a painful experience.


| Number of Classes | Line Coverage | Mutation Coverage | Test Strength |
|---|---|---|---|
| 2 | 82% | 67% | 100% |
| Name | Line Coverage | Mutation Coverage | Test Strength |
|---|---|---|---|
| NestedBean.java | 75% |
64% |
100% |
| SimpleBean.java | 86% |
69% |
100% |


Angular is a full fledge Framework, it eases the efforts of developers with lot of functionalities out of box. Most modern websites need modal windows to display or to obtain information. There are plenty of 3rd party packages for modals and i used them myself and main issue faced is that they are quiet less configurable and we need to override CSS classes to meet our requirements.
So i browsed to see how to write a dialog and i was surprised that it only needs only a few magical css lines. In this post i will shared how to use angular features to build a custom dialog.
Angular CLI is like a swiss knife,it makes the development easier and more productive.
Lets consider you dont have CLI installed, then go ahead and install CLI using the command globally.
Now lets create a new CLI app named my-custom-dialog.
This command will create a app in the folder my-custom-dialog, now lets cd into the folder and run the command
This will run the angular app in port 4200 by default. So you can launch the app by browsing to localhost:4200. It will launch a default Angular cli app.
Now lets move on and create a component for dialog using CLI. Now we are creating a folder modules and we will put the contents inside a folder named dialog nested inside modules folder.
Now lets create a module for the dialog component.
And then let’s create the component.
This will create the following files in our src/app/module/dialog folder:
Transclution is very useful and more powerful feature, using angular transclution we can inject any HTML content into a directive template which is the most needed future to construct a modal. We can achieve transclution using ng-content tag.
Now lets open the dialog.component.ts and start building our dialog
import { Component, OnInit, Input, Output, OnChanges, EventEmitter,
trigger, state, style, animate, transition } from '@angular/core';
@Component({
selector: 'dialog',
templateUrl: 'dialog.component.html',
styleUrls: ['./dialog.component.css']
})
export class dialogComponent {
@Input() show: boolean;
@Output() toggle: EventEmitter<boolean> = new EventEmitter<boolean>();
closeDialog() {
this.show = false;
this.toggle.emit(this.show);
}
}
Lets design dialog template in dialog.component.html

<ng-content></ng-content>

X

Now lets utilize some real power of ng-content. What if there is a need to design a dialog where you are giving separate inputs as header and body for the dialog. Here we can use select where we define Multi-slot transclusion. ng-content accepts a select attribute, where we can define a name for a slot. Now lets open our template and make changes and define a multiple slots

<ng-content select="dialog-header"></ng-content>

X

<ng-content select="dialog-body"></ng-content>

import { NgModule } from "@angular/core";
import { CommonModule } from "@angular/common";
import { DialogComponent } from "./dialog.component";
@NgModule({
imports: [CommonModule],
exports: [DialogComponent], ---->Do this
declarations: [DialogComponent]
})
export class DialogModule {}
This ensures that an application that imports our dialogModule will actually be able to use the dialogComponent. Without that exports array, the only components that would be able to use dialog in their templates would be other components inside the dialogModule.
....
import { DialogModule } from "./modules/dialog/dialog.module";
....
@NgModule({
imports: [..,
DialogModule
],
schemas: [NO_ERRORS_SCHEMA]
})
Import dialog module in app.module.ts.
You will get an error Unhandled Promise rejection: Template parse errors: 'dialg-body' is not a known element
Angular won't recognize these custom html tags. A quick way to get around this error is to add schema metadata property in your module, set value to NO_ERRORS_SCHEMA in your module file.
.blurRemainingofScreen {
position: fixed;
top: 0;
bottom: 0;
left: 0;
right: 0;
background-color: rgba(0, 0, 0, 0.5);
z-index: 999;
}
.popover {
z-index: 1000;
position: fixed;
right: 0;
left: 0;
margin-right: auto;
margin-left: auto;
height: 200px;
width: 200px;
background-color: #fff;
box-shadow: 0 7px 8px -4px rgba(0, 0, 0, 0.2), 0 13px 19px 2px rgba(0, 0, 0, 0.14), 0 5px 24px 4px rgba(0, 0, 0, 0.12);
}
.close-btn {
font-size: 35px;
cursor: pointer;
right: 10px;
position: absolute;
}
.dialog-header {
background-color:blue;
border-top-left-radius: 5px;
border-top-right-radius: 5px;
height: 40px;
}
These styles are used to create a pop-over impression for our template content. z-index plays a major role in styling of dialogs.
Lets utilize our dialog component, consider we are in need for a popup in app.component.html.
<button (click)="show = !show" class="btn">Open</button>
<app-dialog [(show)]="show">
<dialog-header>Alert!</dialog-header>
<dialog-body>
<button (click)="show = !show" class="btn">Close</button>
</dialog-body>
</app-dialog>
Now on launching the application you can see a button, upon clicking can toggle the dialog. Here show variable is two way binded with the dialog component, where you can even control visibility of dialog in app.component.ts rather directly toggling value in app.component.html.
Now we can utilize some features like ng-content, two way binding. In ng-content which type of selector to use depends on your preferences. I prefer using HTML tag because it is more readable.


new Cache()
Constructor of Cache
- Source:
Members
-
encoding :string
-
the files' encoding
Type:
- string
- Source:
Methods
-
addFile(file)
-
Watch for file changes and request cache update on change
Parameters:
Name Type Description file
string absolute path to the file - Source:
-
cacheFile(file, async)
-
Read file and cache filecontents triggers GUI reload on asynchronous read
Parameters:
Name Type Argument Description file
string filepath async
boolean <optional>
reads file asynchronous if set - Source:
-
get(key) → {string|Array}
-
Gets the requested cache
Parameters:
Name Type Description key
- Source:
Returns:
cache content or empty string- Type
- string | Array


|
GPUE
v1.0
GPU Gross-Pitaevskii Equation numerical solver for Bose-Einstein condensates
|
#include <lattice.h>
Public Member Functions | |
| Lattice () | |
| Makes stuff exist. More... | |
| ~Lattice () | |
| Makes stuff anti-exist. More... | |
| std::vector< std::shared_ptr< Node > > & | getVortices () |
| Returns the vectors for vortices. More... | |
| std::vector< std::shared_ptr< Edge > > & | getEdges () |
| Returns the edges for edges. More... | |
| std::shared_ptr< Node > | getVortexIdx (unsigned int idx) |
| Returns vortex specified at index idx. More... | |
| std::shared_ptr< Edge > | getEdgeIdx (unsigned int idx) |
| Returns edge specified at index idx. More... | |
| unsigned int | getVortexIdxUid (unsigned int uid) |
| Returns vortex index based on UID. More... | |
| unsigned int | getEdgeIdxUid (unsigned int uid) |
| Returns edge index based on UID. More... | |
| std::shared_ptr< Node > | getVortexUid (unsigned int uid) |
| Returns vortex based on UID. More... | |
| std::shared_ptr< Edge > | getEdgeUid (unsigned int uid) |
| Returns edge based on UID. More... | |
| double | getVortexDistance (std::shared_ptr< Node > n1, std::shared_ptr< Node > n2) |
| Calculates distance between vortices. More... | |
| double | getVortexDistanceD (std::shared_ptr< Node > n1, std::shared_ptr< Node > n2) |
| Calculates distance between vortices. More... | |
| void | setVortex (unsigned int idx, std::shared_ptr< Node > n) |
| Set vortex (node) at a specific position. More... | |
| void | setEdge (unsigned int idx, std::shared_ptr< Edge > e) |
| Set edge at a specific position. More... | |
| void | addVortex (std::shared_ptr< Node > n) |
| Add vortex (node) to the lattice (graph). More... | |
| void | addEdge (std::shared_ptr< Edge > e) |
| Add edge e to the lattice (graph). Assumes edge has nodes already. More... | |
| void | addEdge (std::shared_ptr< Node > n1, std::shared_ptr< Node > n2) |
| Add edge between vortex (node) n1 and n2 to the lattice (graph). More... | |
| void | addEdge (std::shared_ptr< Edge > e, std::shared_ptr< Node > n1, std::shared_ptr< Node > n2) |
| Add edge e between vortex (node) n1 and n2 to the lattice (graph). More... | |
| void | removeVortex (std::shared_ptr< Node > n) |
| Remove vortex n from the lattice. More... | |
| void | removeVortexIdx (unsigned int idx) |
| Remove vortex from the lattice based on index idx. More... | |
| void | removeVortexUid (unsigned int uid) |
| Remove vortex from the lattice based on UID uid. More... | |
| void | removeEdge (std::shared_ptr< Node > n1, std::shared_ptr< Node > n2) |
| Remove edge between n1 and n2 from the lattice. More... | |
| void | removeEdge (std::shared_ptr< Edge > e) |
| Remove edge from the lattice. More... | |
| void | removeEdgeUid (unsigned int uid) |
| Remove edge from the lattice based on UID uid. More... | |
| void | removeEdgeIdx (unsigned int idx) |
| Remove edge from the lattice based index idx. More... | |
| void | removeEdges (std::shared_ptr< Node > n1) |
| Remove all edges from vortex. More... | |
| void | createVortex (double posx, double posy, int winding) |
| Create a vortex at specified location with given winding. More... | |
| void | destroyVortex (unsigned int uid) |
| Destroy a vortex with UID uid. More... | |
| void | createEdges (unsigned int radius) |
| Generate edges between vortices closer than int radius. More... | |
| void | createEdges (double radius) |
| Generate edges between vortices closer than double radius. More... | |
| void | genAdjMat (unsigned int *mat) |
| Output adjacency matrix. More... | |
| void | genAdjMat (double *mat) |
| Output adjacency matrix. More... | |
| void | adjMatMtca (unsigned int *mat) |
| Format adjacency matrix with Mathematica-friendly output. More... | |
| void | adjMatMtca (double *mat) |
| Format adjacency matrix with Mathematica-friendly output. More... | |
| void | swapIdxUid (unsigned int uid1, unsigned int uid2) |
| Swap the elements at indices based on UID uid1 and uid2. More... | |
| void | swapIdx (unsigned int idx1, unsigned int idx2) |
| Swap the elements at indices idx1 and idx2. More... | |
| std::weak_ptr< Edge > | isConnected (std::shared_ptr< Node > n1, std::shared_ptr< Node > n2) |
| Checks if vortex n1 and n2 are connected. More... | |
| LatticeGraph::Lattice::Lattice | ( | ) |
Makes stuff exist.
| LatticeGraph::Lattice::~Lattice | ( | ) |
Makes stuff anti-exist.
| void LatticeGraph::Lattice::addEdge | ( | std::shared_ptr< Edge > | e | ) |
Add edge e to the lattice (graph). Assumes edge has nodes already.
| e | Edge to add |
Add edge between vortex (node) n1 and n2 to the lattice (graph).
| n1 | Vortex to add edge to as connection 1 |
| n2 | Vortex to add edge to as connection 2 |
| void LatticeGraph::Lattice::addEdge | ( | std::shared_ptr< Edge > | e, |
| std::shared_ptr< Node > | n1, | ||
| std::shared_ptr< Node > | n2 | ||
| ) |
Add edge e between vortex (node) n1 and n2 to the lattice (graph).
| e | Edge to add |
| n1 | Vortex to add edge to as connection 1 |
| n2 | Vortex to add edge to as connection 2 |
| void LatticeGraph::Lattice::addVortex | ( | std::shared_ptr< Node > | n | ) |
Add vortex (node) to the lattice (graph).
| n | Vortex to add |
| void LatticeGraph::Lattice::adjMatMtca | ( | unsigned int * | mat | ) |
Format adjacency matrix with Mathematica-friendly output.
| *mat | Adjacency matrix from genAdjMat(*uint) |
| void LatticeGraph::Lattice::adjMatMtca | ( | double * | mat | ) |
Format adjacency matrix with Mathematica-friendly output.
| *mat | Adjacency matrix from genAdjMat(*double) |
| void LatticeGraph::Lattice::createEdges | ( | unsigned int | radius | ) |
Generate edges between vortices closer than int radius.
| radius | Radius cutoff for creating edge connections |
| void LatticeGraph::Lattice::createEdges | ( | double | radius | ) |
Generate edges between vortices closer than double radius.
| radius | Radius cutoff for creating edge connections |
| void LatticeGraph::Lattice::createVortex | ( | double | posx, |
| double | posy, | ||
| int | winding | ||
| ) |
Create a vortex at specified location with given winding.
| posx | Position x to create vortex |
| posy | Position y to create vortex |
| Winding | of vortex to create |
| void LatticeGraph::Lattice::destroyVortex | ( | unsigned int | uid | ) |
Destroy a vortex with UID uid.
| uid | Vortex to destroy with UID uid |
| void LatticeGraph::Lattice::genAdjMat | ( | unsigned int * | mat | ) |
Output adjacency matrix.
| *mat | Modifyable UInt array for adjacency matrix |
| void LatticeGraph::Lattice::genAdjMat | ( | double * | mat | ) |
Output adjacency matrix.
| *mat | Modifyable double array for adjacency matrix |
| std::shared_ptr<Edge> LatticeGraph::Lattice::getEdgeIdx | ( | unsigned int | idx | ) |
Returns edge specified at index idx.
| idx | Index of vortex position |
| unsigned int LatticeGraph::Lattice::getEdgeIdxUid | ( | unsigned int | uid | ) |
Returns edge index based on UID.
| uid | UID of edge |
| std::vector<std::shared_ptr <Edge> >& LatticeGraph::Lattice::getEdges | ( | ) |
Returns the edges for edges.
| std::shared_ptr<Edge> LatticeGraph::Lattice::getEdgeUid | ( | unsigned int | uid | ) |
Returns edge based on UID.
| uid | UID of edge |
| double LatticeGraph::Lattice::getVortexDistance | ( | std::shared_ptr< Node > | n1, |
| std::shared_ptr< Node > | n2 | ||
| ) |
Calculates distance between vortices.
| n1 | Vortex (node) 1 |
| n2 | Vortex (node) 2 |
| double LatticeGraph::Lattice::getVortexDistanceD | ( | std::shared_ptr< Node > | n1, |
| std::shared_ptr< Node > | n2 | ||
| ) |
Calculates distance between vortices.
| n1 | Vortex (node) 1 |
| n2 | Vortex (node) 2 |
| std::shared_ptr<Node> LatticeGraph::Lattice::getVortexIdx | ( | unsigned int | idx | ) |
Returns vortex specified at index idx.
| idx | Index of vortex position |
| unsigned int LatticeGraph::Lattice::getVortexIdxUid | ( | unsigned int | uid | ) |
Returns vortex index based on UID.
| uid | UID of vortex |
| std::shared_ptr<Node> LatticeGraph::Lattice::getVortexUid | ( | unsigned int | uid | ) |
Returns vortex based on UID.
| uid | UID of vortex |
| std::vector<std::shared_ptr <Node> >& LatticeGraph::Lattice::getVortices | ( | ) |
Returns the vectors for vortices.
| std::weak_ptr<Edge> LatticeGraph::Lattice::isConnected | ( | std::shared_ptr< Node > | n1, |
| std::shared_ptr< Node > | n2 | ||
| ) |
Checks if vortex n1 and n2 are connected.
| n1 | Vortex (node) 1 |
| n2 | Vortex (node) 2 |
Remove edge between n1 and n2 from the lattice.
| n1 | Vortex 1 |
| n2 | Vortex 2 |
| void LatticeGraph::Lattice::removeEdge | ( | std::shared_ptr< Edge > | e | ) |
Remove edge from the lattice.
| e | Edge to remove |
| void LatticeGraph::Lattice::removeEdgeIdx | ( | unsigned int | idx | ) |
Remove edge from the lattice based index idx.
| idx | Edge at index idx to remove |
| void LatticeGraph::Lattice::removeEdges | ( | std::shared_ptr< Node > | n1 | ) |
Remove all edges from vortex.
| n1 | Vortex (node) to lose all edges |
| void LatticeGraph::Lattice::removeEdgeUid | ( | unsigned int | uid | ) |
Remove edge from the lattice based on UID uid.
| uid | Edge with UID uid to remove |
| void LatticeGraph::Lattice::removeVortex | ( | std::shared_ptr< Node > | n | ) |
Remove vortex n from the lattice.
| n | Vortex (node) to remove |
| void LatticeGraph::Lattice::removeVortexIdx | ( | unsigned int | idx | ) |
Remove vortex from the lattice based on index idx.
| idx | Vortex at index idx to remove |
| void LatticeGraph::Lattice::removeVortexUid | ( | unsigned int | uid | ) |
Remove vortex from the lattice based on UID uid.
| uid | Vortex with UID uid to remove |
| void LatticeGraph::Lattice::setEdge | ( | unsigned int | idx, |
| std::shared_ptr< Edge > | e | ||
| ) |
Set edge at a specific position.
| idx | Index to set vortex |
| n | Vortex to set |
| void LatticeGraph::Lattice::setVortex | ( | unsigned int | idx, |
| std::shared_ptr< Node > | n | ||
| ) |
Set vortex (node) at a specific position.
| idx | Index to set vortex |
| n | Vortex to set |
| void LatticeGraph::Lattice::swapIdx | ( | unsigned int | idx1, |
| unsigned int | idx2 | ||
| ) |
Swap the elements at indices idx1 and idx2.
| idx1 | Index of element 1 |
| idx2 | Index of element 2 |
| void LatticeGraph::Lattice::swapIdxUid | ( | unsigned int | uid1, |
| unsigned int | uid2 | ||
| ) |
Swap the elements at indices based on UID uid1 and uid2.
| uid1 | UID of element 1 to swap |
| uid2 | UID of element 2 to swap |


{% extends 'base.html' %} {% block navbar %}
All things begin with a Language object in spaCy.
Enter a name for your "new language." For example, "Athabaskan" or "Tolstoy Russian"
Enter a two or five character identifier for your language (such as "ru" or "sr")
If you need to install a Python library such as CamelTools (Arabic) or pymorphy (Russian) enter the name of the library as you would in `pip install ...`. Multiple entries should be separated by a comma.
This languages has upper and lower case system.
This languages has letters rather than symbols.


NANCY had to make up her mind in a hurry whether to try to get away from the man or turn
the tables on him and prove that he had helped a criminal escape.
35
In the distance she saw her father running in her direction. He had called another guard,
who was hurrying along with him.
By the time Nancy’s adversary realized that he was about to be caught, he let go of her and
started to run. But the girl grabbed his wrist and held on tightly. Tug as he would, he could
not get away before Mr. Drew and the other guard had caught up.
“What’s going on here?” the lawyer demanded.
Before the suspect could answer, the guard who had accompanied Mr. Drew spoke up. “This
man is not one of us!” He turned to the impostor. “Who are you?”
The man did not reply, so Nancy told her story and her suspicion that he was in league with
a man who was wanted by numerous United States authorities.
“I will take him into custody and hold him until he talks,” the legitimate guard declared.
He asked the impostor where he had obtained the uniform. The suspect stared defiantly,
but said nothing.
Nancy tried to catch him by surprise. “Where did Benny Caputti go?” she asked him.
The prisoner jumped and blinked, indicating that the girl had touched upon a vital subject.
Still he did not talk.
“It is against the law to impersonate an officer,” the guard declared. “You will have to come
with me!”
Suddenly the suspect cried out, “Why don’t you arrest this girl? She molested a visitor,
trying to photograph him against his will. See her camera? She was bothering people with
it.”
Nancy and her father were startled, but Nancy said calmly, “He’s trying to twist things
around. The fellow who fled, and who I believe is in league with this man, took my
photograph. That’s why I chased him.”
The guard nodded and handcuffed the prisoner. “I understand. Just give me your names and
addresses in case the police want to contact you.”
The Drews did, but before leaving Nara, they went to headquarters and told the full story.
They suggested that the chief get in touch with United States Authorities regarding Caputti
and the suspect, who evidently acted as a bodyguard for Benny the Slippery One. The officer
thanked them for the information and promised to look into the matter.
36
When the Drews and the Mises reached home late next afternoon, they found Haruka, the
delightful maid whom the family employed, had arranged a special dinner. The reason was
that it was Mrs. Mise’s birthday!
Haruka had made paper bird and flower decorations using the famous Oregami method. She
had hung them in various parts of the dining room. On the table she had put live flowers of
various colors interspersed with green vines. In one corner of the room, the maid had
placed a huge jardiniere and filled it with lavender wisteria that gently drooped over the
sides.
Haruka served a delicious dinner that included strips of fish with rice and fried vegetables.
Warm sake, a wine made from rice, was served with the meal. Dessert consisted of a flat
piece of plain cake in which the center was scooped out and had been replaced with half of
a large, ripe peach. Over it she had poured a hot, pink peach sauce, and she had decorated
around the edge with scattered peach leaves.
Everyone enjoyed the delicious meal, and they each proposed toasts to wish Mrs. Mise a
happy birthday. When dinner was over, Haruka brought in a large cage with hundreds of
gorgeous butterflies in it. She announced that this was her personal gift to Mrs. Mise. She
had recently been on vacation and had collected them herself.
“They are beautiful!” Mrs. Mise told her, then translated for the Drews.
“You know my love for the colors blue and yellow,” she continued. “How exquisite these
butterflies are.”
Everyone stood up to look at the fluttering creatures. Nancy liked a particular one that had
various shades of blue from light to dark from its center to the wing tips. Mr. Drew’s favorite
was a reddish brown butterfly with black spots on its wings.
After the excitement about the gift had subsided, Mr. Mise gave his wife his presento. It was
a high comb studded with pearls for his wife’s hair.
“How beautiful!” Nancy exclaimed.
“My husband is a most kind man,” Mrs. Mise said. “This is a lovely way to remind me of this
birthday.”
Nancy and her father were sorry they had not known about the special occasion and did not
have a gift for their hostess.
Suddenly Nancy laughed. “Dad,” she said, “we brought presentos for the Mises from the
United States. In all the excitement about villains we completely forgot!” She asked to be
excused, went upstairs, and got the gifts from her suitcase. She brought them down and
handed a package to each of their friends.
37
Mr. Mise’s presento was a new type of fountain pen. After thanking the Drews, he said that
he was delighted to own one of them, since they had not yet reached the Japanese market.
His wife’s gift was a dainty lace handkerchief. It had been made by a nun who was an expert
at needlecraft. She had learned the art in Belgium. Mrs. Mise was thrilled with the presento.
Further conversation was interrupted by the ringing of the telephone. Haruka answered and
said that it was for Mr. Drew and Miss Nancy. When Mr. Drew said hello, there was a click
on the line, and it went dead.
Nancy and her father looked at each other. Was this a hoax, or was Mr. Caputti trying to find
out whether the Drews had returned to the Mises’ home?
The lawyer asked Haruka if she had any idea from where the call had come.
“It was from overseas, sir,” she said. “I am so sorry you were interrupted.”
A few minutes went by and the phone rang again. This time Mr. Drew answered it himself.
“Is this the Mise home?” a man asked.
“Yes.”
“Am I speaking with Mr. Drew?”
“Yes.”
“This is Professor Joji Mise in River Heights,” the caller identified himself.
“I’m glad to hear from you,” Mr. Drew said. “Did you just call a few minutes ago and were
interrupted?”
“Yes. I had to try again. Unfortunately, I have bad news for you.”
Nancy, who stood close enough to the receiver to overhear the conversation, felt a chill
going down her spine. Instantly she thought of Mr. Moto and Mr. Kikichi.
“I have just learned,” the professor went on, “that burglars broke into Mr. Moto’s shop.
Chief McGinnis asked me to tell you about it. They took everything in the place.”
“That is, indeed, bad news,” Mr. Drew remarked. “What about Mr. Kikichi?”
“He was beaten unconscious and taken to the hospital. But he is all right now.”
“Are there any clues to the burglars?” Mr. Drew inquired.
38
“Yes, the police think that Benny Caputti and his wife were responsible. Further
investigation revealed that they might have fled to Japan. We thought you should know
this.”
Mr. Drew told the professor of their trip to Nara. “My daughter believes she spotted Mr.
Caputti but was stopped from pursuing him by a fake guard.” He gave the details of the
event, and the professor suggested that the Drews try to locate the couple.
“We’ll do our best,” Mr. Drew promised. “I suppose the River Heights police are working on
this, but we may find some good leads here.”
“Fine. I will report this to the chief. And now I would like to say hello to my brother and his
wife.”
As Mr. Drew said good-by to the professor and handed the phone to Mr. Mise, Nancy felt
frustrated. Not only had they failed to find Mr. Moto, but now all his property had been
stolen! While she and her father discussed the case, she did not know that a good lead
would turn up soon. The Mises joined them a few minutes later, but their conversation was
interrupted again when the phone rang a third time.
The call was for Nancy. It proved to be from the chief of the Tokyo police force. “We are
holding a woman here who has been using a phony passport,” the officer told her. “She
resembles the description you gave us of a suspect the other day. Is it possible for you to
come here to identify her?”


{% extends "layout.html" %} {% block page_title %} Publish government data {% endblock %} {% block proposition_header %} {% include "includes/propositional_navigation_service.html" %} {% endblock %} {% block header_class %} with-proposition {% endblock %} {% block content %} {% include "includes/phase_banner_beta.html" %} {% include "includes/side-navigation.html" %} {{formData | safe}} Upload a new dataset You must give your dataset a title and summary. This will make it easier for users to find it on GOV.UK Upload a new dataset Title Titles should be 65 characters or less, including spaces. Most search engines will shorten titles if they’re longer than this. Summary The rst sentence of the summary will appear in search results. It should be 140 characters or less. {% endblock %}
You must give your dataset a title and summary.
This will make it easier for users to find it on GOV.UK


This is the API
This can only be done by the logged in user.
http://petstore.swagger.io/v2/user
POST
Created user object
http://petstore.swagger.io/v2/user/createWithArray
POST
List of user object
http://petstore.swagger.io/v2/user/createWithList
POST
List of user object
This can only be done by the logged in user.
http://petstore.swagger.io/v2/user/{username}
DELETE
The name that needs to be deleted
http://petstore.swagger.io/v2/user/{username}
GET
The name that needs to be fetched. Use user1 for testing.
http://petstore.swagger.io/v2/user/login
GET
The user name for login
The password for login in clear text
http://petstore.swagger.io/v2/user/logout
GET
This can only be done by the logged in user.
http://petstore.swagger.io/v2/user/{username}
PUT
name that need to be deleted
Updated user object


166_rickettsdinner630.py
def flood(i, neighbor, visited):
visited[i] = True
for n in neighbor[i]:
if not visited[n]:
flood(n, neighbor, visited)
return visited
class Solution:
def maxNumEdgesToRemove(self, n: int, edges: List[List[int]]) -> int:
conn = [set([1]), set([1])]
count = 0
neighbor = [[set() for _ in range(n + 1)] for _ in range(4)]
for (t, i, j) in edges:
neighbor[t][i].add(j)
neighbor[t][j].add(i)
if t == 3:
neighbor[1][i].add(j)
neighbor[1][j].add(i)
neighbor[2][i].add(j)
neighbor[2][j].add(i)
if sum(flood(1, neighbor[1], [False for _ in range(n + 1)])) != n:
return -1
if sum(flood(1, neighbor[2], [False for _ in range(n + 1)])) != n:
return -1
visited = [False for _ in range(n + 1)]
block = 0
for i in range(1, n + 1):
if not visited[i]:
b = flood(i, neighbor[3], visited)
block += 1
return len(edges) - sum(visited) + block - (block - 1) * 2


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World Tour Finals 2019
参加対象: - Rated対象: All ペナルティ: 5分
Contest Information
- Duration: 240 minutes
- Writer: rng_58
- Rated Range: Everyone is rated
Point Values
| Task | Score |
|---|---|
| A | 1000 |
| B | 1400 |
| C1 | 1000 |
| C2 | 1000 |
| D | 2000 |
| E | 2718 |
Contest Rules
This contest is full-feedback (solutions are judged during the contest).When you solve a problem, you get a score assigned to it. Competitors are ranked first by total scores, then by penalties. The penalties are computed as (the time you spend to get your current score) + (5 minutes) * (the number of incorrect attempts).


Hello World! Please Click Me
I can inject and use the previous plugin! Please Click Me
I can't use the previous plugin! Please Click Me And See
I have done everything right but still i cant inject! Please Click Me And See


|
Jala Test | ||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
| SUMMARY: FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
Object | +--jala.Test.HttpClient
Instances of this class represent a http client useable for
testing, as any session cookies received by the tested application
are stored and used for subsequent requests, allowing simple "walkthrough"
tests.
Defined in Global/jala.Test.js
| Constructor Summary | |
jala.Test.HttpClient
()
Constructs a new HttpClient instance |
| Method Summary | |
Object
|
executeRequest(<String> method, <String> url, <Object> param)
Sends a HTTP request to the Url passed as argument |
helma.Http
|
getClient()
Returns the http client used |
Array
|
getCookies()
Returns the cookies set for this http client |
Object
|
getUrl(<String> url, <Object> param)
Convenience method for requesting the url passed as argument using method GET |
void
|
setCookies(<Array> arr)
Sets the cookie to use for subsequent requests using this client |
Object
|
submitForm(<String> url, <Object> param)
Convenience method for submitting a form. |
| Constructor Detail |
jala.Test.HttpClient()
| Method Detail |
Object executeRequest(<String> method, <String> url, <Object> param)
method
- The HTTP method to use
url
- The url to request
param
- A parameter object to use with this request
helma.Http getClient()
Array getCookies()
Object getUrl(<String> url, <Object> param)
url
- The url to request
param
- A parameter object to use with this request
void setCookies(<Array> arr)
arr
- The cookie object as received from helma.Http.getUrl
Object submitForm(<String> url, <Object> param)
url
- The url to request
param
- A parameter object to use with this request
|
Jala Test | ||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
| SUMMARY: FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


"""Class to interface with the mongodb store."""
from __future__ import annotations
from pathlib import Path
import ray
from bson.objectid import ObjectId
from pymongo import MongoClient # type: ignore
from xplogger.experiment_manager.record import mongo as mongo_record_utils
from xplogger.experiment_manager.record.record_list import RecordList
from xplogger.experiment_manager.utils.enum import ExperimentStatus
from xplogger.parser.utils import parse_json
from xplogger.types import ConfigType
from xplogger.utils import serialize_log_to_json
# Module "pymongo" does not explicitly export attribute "MongoClient"; implicit reexport disabled
[docs]class MongoStore:
def __init__(
self,
config: ConfigType,
):
"""Class to interface with the mongodb store.
Args:
config (ConfigType): Config to connect with the mongo store.
"""
self._client = MongoClient(host=config["host"], port=config["port"])
db = config["db"]
collection_name = config["collection_name"]
self.collection = self._client[db][collection_name]
[docs] def ray_get_records(self) -> RecordList:
"""Get records from the db using ray."""
futures = [
mongo_record_utils.ray_make_record.remote(record)
for record in self.collection.find()
]
records = ray.get(futures)
assert isinstance(records, list)
return RecordList(records=records)
[docs] def get_records(self, query) -> RecordList: # type: ignore
"""Get records from the db."""
return RecordList(
records=[
mongo_record_utils.make_record(record)
for record in self.collection.find(query)
]
)
[docs] def delete_records(
self, record_list: RecordList, delete_from_filesystem: bool = False
) -> None:
"""Delete records from the db and filesystem (optional).
Args:
record_list (RecordList):
delete_from_filesystem (bool, optional): should delete records
from the filesystem. Defaults to False.
"""
record_list.delete(
collection=self.collection, delete_from_filesystem=delete_from_filesystem
)
[docs] def update_status(self, record_list: RecordList, new_status: str) -> None:
"""Mark records as analyzed in the db.
Args:
record_list (RecordList):
"""
return record_list.update_status(
collection=self.collection, new_status=new_status
)
[docs] def mark_records_as_analyzed(self, record_list: RecordList) -> None:
"""Mark records as analyzed in the db.
Args:
record_list (RecordList):
"""
return self.update_status(
record_list=record_list, new_status=ExperimentStatus.ANALYZED.value
)
[docs] def ignore_records_by_status(self, status: str) -> RecordList:
"""Get a list of records which do not match the status."""
query = {"status": {"$ne": status}}
return RecordList(records=list(self.get_records(query=query)))
[docs] def ray_ignore_records_by_status(self, status: str) -> RecordList:
"""Get a list of records which do not match the status using ray."""
query = {"status": {"$ne": status}}
futures = [
mongo_record_utils.ray_make_record.remote(record)
for record in self.get_records(query=query)
]
records = ray.get(futures)
assert isinstance(records, list)
return RecordList(records=records)
[docs] def get_unanalyzed_records(self) -> RecordList:
"""Get a list of un-analyzed records."""
return self.ignore_records_by_status(status=ExperimentStatus.ANALYZED.value)
[docs] def ray_get_unanalyzed_records(self) -> RecordList:
"""Get unalalyzed records using ray."""
return self.ray_ignore_records_by_status(status=ExperimentStatus.ANALYZED.value)
[docs] def save_to_file(self, filepath: Path) -> None:
"""Save mongo records to a file."""
with open(filepath, "a") as f:
for record in self.collection.find():
record["_id"] = str(record["_id"])
record["mongo_id"] = record["_id"]
f.write(serialize_log_to_json(record))
f.write("\n")
[docs] def load_from_file(self, filepath: Path) -> None:
"""Load records from a file to Mongo DB."""
with open(filepath) as f:
for record in f:
record_dict = parse_json(record)
assert record_dict is not None
record_dict["_id"] = ObjectId(record_dict["_id"])
self.collection.insert_one(record_dict)
[docs] def replace_from_file(self, filepath: Path, upsert: bool = False) -> None:
"""Replace records from a file to Mongo DB."""
with open(filepath) as f:
for record in f:
record_dict = parse_json(record)
assert record_dict is not None
self.collection.replace_one(
{"_id": ObjectId(record_dict.pop("_id"))},
record_dict,
upsert=upsert,
)


Reconnect your calendar
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DrawingBuffer()
Syntax
MemoryAddress = DrawingBuffer()Description
Returns the MemoryAddress of the drawing buffer for direct pixel manipulation.
Parameters
None.
Return value
Returns the address of the pixel data if direct access is possible or zero if the pixel data cannot be accessed directly.
Remarks
This function has to be called again if other drawing commands of this library where used since the last pixel manipulation.
This function is for advanced programmers only. To get more information about the buffer, the following functions are available: DrawingBufferPixelFormat() and DrawingBufferPitch().
The returned address can be directly in video memory if the output is ScreenOutput() or SpriteOutput() and allows very fast pixel manipulation. With ImageOutput() this command allows direct access to the pixels of the target image.
Example
For an example of how the drawing buffer can be used to create a nice visual effect, take a look here:
Example
DirectScreenDrawing.pb
See Also
DrawingBufferPixelFormat(), DrawingBufferPitch()
Supported OS
All


As a full stack web developer, I can take a project from its earliest conception to deloyment on the web.
I am currently seeking employment with a cutting edge tech company!
Sleek Design and Mobile Responsive UIs
Databases, Servers, and Deployment
Effective communication with you and your clients.
I love using the latest tech for streamlined projects


After you've clicked the "Create Exploration" button, you'll be taken to the exploration editor page.
At first, your exploration will contain a single card, called "First Card". Cards are the building blocks for your explorations. You can think of cards as the places a learner (anybody who views your exploration) can potentially reach.
As you continue creating and expanding your exploration, you'll be adding to the number of cards, all of which will be visible in the Exploration Overview (also in the picture above). The editing interface only shows one card at a time, and you can navigate to other cards by clicking on them in the Exploration Overview.
A card consists of three parts: some content, an interaction (or question), and responses that specify what Oppia should do next, based on the learner's answer.
The card's content is an editable block of text.
Here, you can write whatever you want the learner to read. The content can be about any topic/subject, and would end with a question or statement that invites the learner to interact with Oppia. You can also include rich-text elements like images, videos and links.
This is the type of activity that the learner performs while learning. It could be typing in a text field, placing a marker on a map, or various other things.
Note: Oppia provides a number of different interactions out of the box, but if you have an idea for an interaction that doesn't already exist, please file an issue on GitHub describing it and what you'd like it to do. This allows us to keep track of the request, and someone might be able to help you build it in the future. You'd also be welcome to try building it yourself, and contributing it to the open source codebase.
Once you've chosen an interaction, you can decide what Oppia should do, based on the learner's answer. Different interaction types will have different sets of response options to choose from. You can provide feedback and then have the learner redo the interaction, or move them to a different card altogether.
Once you've specified what Oppia should do for answers, you can specify hints for the current card. You can add a list of hints that the learner can use when stuck. To prevent learners from abusing this feature, a hint can be requested only if one of the following conditions are satisfied:


---
layout: page
title: "References"
description: "Phenomenology papers and experimental results using
razor variables"
header-img: "img/about-bg.jpg"
---
- Rogan, C. (2010). Kinematical variables towards new dynamics at the LHC. Retrieved from http://arxiv.org/abs/1006.2727
[url]
- Chatrchyan, S., & others. (2012). Inclusive search for squarks and gluinos in pp
collisions at sqrt(s) = 7 TeV. Phys. Rev., D85, 012004. doi:10.1103/PhysRevD.85.012004
[url]
- CMS Collaboration. (2012). Search for supersymmetry with taus using the razor
variables (No. CMS-PAS-SUS-11-029). Geneva: CERN. Retrieved from http://cds.cern.ch/record/1493504
[url]
- Chatrchyan, S., & others. (2012). Search for third-generation leptoquarks and scalar
bottom quarks in pp collisions at sqrt(s) = 7 TeV. JHEP, 12, 055. doi:10.1007/JHEP12(2012)055
[url]
- Fox, P. J., Harnik, R., Primulando, R., & Yu, C.-T. (2012). Taking a Razor to Dark Matter Parameter Space at the
LHC. Phys. Rev., D86, 015010. doi:10.1103/PhysRevD.86.015010
[url]
- Fowlie, A., Kazana, M., Kowalska, K., Munir, S., Roszkowski, L., Sessolo, E. M., … Tsai, Y.-L. S. (2012). The CMSSM Favoring New Territories: The Impact of New
LHC Limits and a 125 GeV Higgs. Phys. Rev., D86, 075010. doi:10.1103/PhysRevD.86.075010
[url]
- Delgado, A., Giudice, G. F., Isidori, G., Pierini, M., & Strumia, A. (2013). The light stop window. Eur. Phys. J., C73(3), 2370. doi:10.1140/epjc/s10052-013-2370-5
[url]
- Aad, G., & others. (2013). Multi-channel search for squarks and gluinos in
sqrt(s) = 7 TeV pp collisions with the ATLAS detector. Eur. Phys. J., C73(3), 2362. doi:10.1140/epjc/s10052-013-2362-5
[url]
- Chatrchyan, S., & others. (2013). Inclusive search for supersymmetry using the razor
variables in pp collisions at sqrt(s) = 7 TeV. Phys. Rev. Lett., 111(8), 081802. doi:10.1103/PhysRevLett.111.081802
[url]
- CMS Collaboration. (2014). Search for supersymmetry in two-photons+jet events with
razor variables in pp collisions at sqrt(s) = 8 TeV (No. CMS-PAS-SUS-14-008). Geneva: CERN. Retrieved from http://cds.cern.ch/record/1749154
[url]
- Chatrchyan, S., & others. (2014). Measurement of Higgs boson production and properties in
the WW decay channel with leptonic final states. JHEP, 01, 096. doi:10.1007/JHEP01(2014)096
[url]
- Chatrchyan, S., & others. (2014). Search for supersymmetry with razor variables in pp
collisions at sqrt(s) = 7 TeV. Phys. Rev., D90(11), 112001. doi:10.1103/PhysRevD.90.112001
[url]
- Buckley, M. R., Lykken, J. D., Rogan, C., & Spiropulu, M. (2014). Super-Razor and Searches for Sleptons and Charginos at
the LHC. Phys. Rev., D89(5), 055020. doi:10.1103/PhysRevD.89.055020
[url]
- Baldi, P., Sadowski, P., & Whiteson, D. (2014). Searching for Exotic Particles in High-Energy Physics
with Deep Learning. Nature Commun., 5, 4308. doi:10.1038/ncomms5308
[url]
- Aad, G., & others. (2015). Search for squarks and gluinos in events with isolated
leptons, jets and missing transverse momentum at
sqrt(s) = 8 TeV with the ATLAS detector. JHEP, 04, 116. doi:10.1007/JHEP04(2015)116
[url]
- CMS Collaboration. (2015). Inclusive search for supersymmetry using the razor
variables at sqrt(s) = 13 TeV (No. CMS-PAS-SUS-15-004). Geneva: CERN. Retrieved from http://cds.cern.ch/record/2114815
[url]
- CMS Collaboration. (2015). Search for SUSY with Higgs in the diphoton final state
using the razor variables (No. CMS-PAS-SUS-14-017). Geneva: CERN. Retrieved from http://cds.cern.ch/record/2047472
[url]
- Khachatryan, V., & others. (2015). Search for Supersymmetry Using Razor Variables in Events
with b-Tagged Jets in pp Collisions at sqrt(s) = 8
TeV. Phys. Rev., D91, 052018. doi:10.1103/PhysRevD.91.052018
[url]
- Aad, G., & others. (2015). Summary of the searches for squarks and gluinos using
sqrt(s) = 8 TeV pp collisions with the ATLAS experiment
at the LHC. JHEP, 10, 054. doi:10.1007/JHEP10(2015)054
[url]
- CMS Collaboration. (2016). Further SUSY Simplified Model interpretations for Moriond
2016 (No. CMS-PAS-SUS-16-004). Geneva: CERN. Retrieved from http://cds.cern.ch/record/2140312
[url]
- Khachatryan, V., & others. (2016). Search for dark matter particles in proton-proton
collisions at sqrt(s) = 8 TeV using the razor variables. Retrieved from http://arxiv.org/abs/1603.08914
[url]
- Khachatryan, V., & others. (2016). Search for supersymmetry in pp collisions at sqrt(s)
= 8 TeV in final states with boosted W bosons and b jets
using razor variables. Submitted To: Phys. Rev. D. Retrieved from http://arxiv.org/abs/1602.02917
[url]


To run Hspec tests we have to define an executable, the test driver.
By convention, in the context of a Haskell package, we define the test driver
in test/Spec.hs
.
As we
have already seen in previous sections of this manual, the hspec
function can be used to do this. In general a spec
of type Spec
can be
turned into an executable with:
-- file test/Spec.hs
module Main where
import Test.Hspec
main :: IO ()
main = hspec spec
Note: For a real-world project the test driver can be generated with hspec-discover.


@* * Copyright 2021 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import config.ApplicationConfig @import views.html.helpers._ @import views.ViewHelpers @this(gmpMain: gmp_main, viewHelpers: ViewHelpers) @(inflationProofForm: Form[models.InflationProof])(implicit request: Request[_], context: config.GmpContext, messages: Messages,applicationConfig: ApplicationConfig) @implicitFormInstance = @{ Some(inflationProofForm) } @pageScripts = { } @gmpMain(title = Messages("gmp.inflation_proof.question") + " - " + Messages("service.title") + " - " + Messages("gov.uk"), pageScripts = Some(pageScripts)) { @includes.back_link(Messages("gmp.inflation_proof.question")) @views.html.helpers.errorSummary(form = inflationProofForm, focusId = {if(!inflationProofForm.hasGlobalErrors && inflationProofForm.error("revaluate").isDefined) Some("revaluate-revaluation-date") else Some("revaluationDate_day")}) @viewHelpers.form(action = routes.InflationProofController.post) {


--- layout: data_model title: BehaviorCollectionType this_version: 4.1 ---
The BehaviorCollectionType provides a Capability for characterizing collections of behaviors.
| Field Name | Type | Description |
|---|---|---|
| @nameoptional | string |
The name field specifies the name of the collection. |
| Affinity_Type0..1 | string |
The Affinity_Type field provides an abstract way of characterizing how the objects in a collection are related. |
| Affinity_Degree0..1 | string |
The Affinity_Degree field is intended to provide an abstract way of characterizing the degree to which the objects in a collection are related. |
| Description0..1 | string |
The Description field contains a textual description of the collection. |
| @idrequired | QName |
The id field specifies a unique ID for this Behavior Collection. |
| Purpose0..1 | string |
The Purpose field states the intended purpose of the collection of Behaviors. Since Behaviors are not always successful, and may not be fully observed, this is meant as way of absracting the nature of the collection of Behaviors away from its constituent Actions. |
| Behavior_List1..1 | BehaviorListType |
The Behavior_List field specifies a list of Behaviors that make up the collection. |


Please select the area with the mouse that should be displayed inside the iframe. Simply draw a rectangle over the iframe. Afterwards copy the settings to the default config or use the displayed shortcode as base.
The viewport string can be used for the feature "Change the viewport when iframe changes to the next step". See the
pro demo for examples.
Selection: x: y: width: height:
Please check the documentation/demos how this parameters are used! Especially color and the file id are very powerful.
Color/css backend:


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| Member | Titles | Evaluations | Last Post | |
|---|---|---|---|---|
| {{ member.get_full_name }} | {{ member.email }} | CC, CL |
Zero |
Not built yet |


Overview
Skyline is a Python based time series analysis and performance monitoring application that helps you to monitor and better understand complex systems and their interactions. Skyline provides a unique kind of performance monitoring by using real time anomaly detection, threshold and dynamic threshold analysis, user input/training for machine learning with various correlation methods, to provide structured data sets of significant changes within metric populations.
Features
Skyline use the most appropriate state of the art algorithms for real time anomaly detection.
Uses multi stage analysis and filtering to allow for the use of more time complex algorithms to be applied in the real time domain.
Puts the user in control of false positive detections by allowing for simple user input to provide explainable ML methods with training data which massively reduce false positive detections and provides the system with data to begin learning with.
What is anomaly detection and time series analysis performance monitoring good for?
It is extremely useful in the operations and development arenas, to highlight a few use cases:
In operations monitoring to provide a real time feed of significant changes and related events in your systems, the good, the bad and the unknown and unseen.
Enabling developers to monitor components of their development environment helping them to build for performance by giving them a real time view of any performance related impacts any new feature may introduce. For example a developer may create a new feature that periodically runs analysis on some data from a MySQL database. The developer builds the feature, tests it to ensures that it provides the required functionality and releases the feature. The feature works but it introduces an additional 225K of mysql.handler_read_next operations on every run, which were not there before the feature was released. The developer method works, but it could work much better. Skyline provides the developer with information to enable them to be the best they can be in terms of performance and understanding impacts, in a areas where they may have zero or little domain knowledge, such as the internal metrics of MySQL/MariaDB.
Our work can always be better.
The question is why are we NOT doing anomaly detection on our things? Shouldn’t we be analysing our observability data? If the 3 pillars of observability are:
logs
metrics
traces
What do pillars hold up?
Data analysis is the next generation of the observability stack, o11y 1.0 was about collection, storage, retrieval and presentation. Anomaly detection is is a part of the next generation of observability, putting all that data to use in real time - analysis.
What can Skyline be compared to
Although it may seem that there are various services and applications that might be compared to Skyline, in reality most of them are not comparable, for a variety of reason.
Google, AWS and Azure all have some kind of anomaly detection service for timeseries data, but those services are generally one trick ponies or one or two trick black boxes.
What none of those are is a dedicated, multi stage analysis pipeline with first and foremost human in the loop in terms of configuration, choice and model training.
Skyline currently allows for 12 analysis stages by default implementing a number of different analysis methods:
analyzer - 3-sigma (to stage 2 if anomalous at 24h)
mirage - 3-sigma (to stage 3 if anomalous at 7d)
mirage - matrixprofile (to stage 4 if anomalous at 7d)
mirage - anomalous_daily_peaks (to stage 5 if not a normal daily peak)
mirage - irregular_unstable (to stage 6 if true or to stage 7 if not true and trained instances available)
mirage - 3-sigma/matrixprofile/spectral_residual (to stage 7 if anomalous at 30d and trained instances available)
ionosphere - motif matching on 24h trained data (to stage 8 if no matches found)
ionosphere - motif matching on 7d trained data (to stage 9 if no matches found)
ionosphere - features extracted and compared to 24h trained data (to stage 10 if no matches found)
ionosphere - minmax features extracted and compared to 24h minmax trained data (to stage 11 if no matches found)
ionosphere - features extracted and compared to 7d trained data (to stage 12 if no matches found)
ionosphere - minmax features extracted and compared to 7d minmax trained data
Skyline is unique. It is the only real open source anomaly detection system, that allows for state of the art (SOTA) on realtime data and plugs into our existing telemetry systems to enable realtime anomaly detection on your metrics and even traces (converted to metrics).
Skyline allows you to specify what algorithms to run, and even allows you to add your own, it allows you to provide the system with valid training data with the click of a button. It records all anomalies, analysis results and reports on performance on a per metric basis. You can even evaluate your own or other algorithms in the real time pipeline, without having them influence the outcome of any existing analysis. This allows for benchmarking the actual performance of algorithms on your own real data.
And it is cluster ready for production. However be warned, it is a complex stack to set up and implement, once running it is fine, it bit like my Mercedes MB120 van which has 662000 km on the clock. However, like my van, it runs perfectly fine but when it comes to upgrading all the components and the app itself is a fairly large slice of orchestrated work, not for the faint hearted.
human-in-the-loop
The use multi-stage analysis and multiple algorithms decreases false positive detections. However false positives are inevitable so the user can easily train Skyline, which enables massive reductions in false positives by utilising extracted features and shapelets/motifs from the user trained time series and running various state of the art similarity search methods against potentially anomalous instances of data. These methods are extremely accurate and achieving a False negative rate of ~0.34% on a sample of 1000 matches from 246799 matches achieved over a period a 55 month period on the real time analysis of production system and application metrics. Meaning that the system flagged an instance of data as matching a trained instance of data incorrectly only 838 times and it matched correctly 245961 times over the 55 month period.
Your input is more valuable than you know or even imagine! Even though you may think you know nothing or very little about metrics, you and your brain can be magnificent.
In the world of machine learning achieving a 0.34% error rate on classifications is not bad, whether that be unsupervised, semi-supervised or a supervised machine learning algorithm, 0.34% is not bad, it is within acceptable limits in the context of what it is doing.
It must be caveated that this error rate can be affected by data quality, currently no checks are implemented to ensure that the potentially anomalous timeseries being checked is the same length or at least similar length to the trained data set to which it is being compared. Although the SITTSSCA method is fairly robust to some missing data, more dissimilar length timeseries will produce increasingly dissimilar features and the chance of them matching decreases quite rapidly.
Skyline is built for speed.
The analysis stages move from very simple and efficient algorithms, to more complex and specialised algorithms through the analysis pipeline. This ensures that more computationally expensive analysis is only carried out when it is needed and all analysis is conducted as fast as possible.
Every optimisation that can be found is implemented, whether that be optimising a 0.077915 second operation to 0.005692 seconds with the use of bottleneck rather than pandas or finding optimisations in the algorithmic methodologies and pipeline and gaining wherever possible on suitable algorithms/methods with numba JIT compiled algorithms, every millisecond counts. We are not talking about nanoseconds here because we are dealing with big data sets, so we must be realistic.
Unfortunately no single anomaly detection algorithm or method or service is perfect. Using any a single method to do anomaly detection is a flawed approach and achieves suboptimal long term results. Anomaly detection is a constantly shifting space with new methodologies/papers/libraries published almost daily. However the truth is that real world, real time implementations of high volume and high frequency metrics of many of these new methodologies/papers/libraries published are very few and far between. And often they do not fit into the real time space, for many reasons, a few are:
computational time complexity
need for tuned hyperparameters on different types of data sets
need for labelled data
Skyline attempts to implement methods that work with zero domain knowledge, as the system runs and records data, it uses the data it creates itself to do zero knowledge discovery of relationships in the metrics and learn about them.
Anomaly detection is not simply about fault detection and identifying problems, anomaly detection is about describing significant changes. Skyline creates a real time event stream of significant changes to inform the user of what is happening on their systems, applications and metrics of interest. Do not think of Skyline as an alerting system, think of it as an event stream that can be trained.
Skyline teaches you about your systems and allows you to train it on what is normal. You can even configure it to try and learn for its self from what you trained it on.
Skyline runs on Linux on commodity hardware or a cloud instance.
At its core Skyline can currently makes use of the uses the following algorithms (or modifications thereof):
9 three-sigma based algorithms, derived from Shewhart’s statistical process
SITTSSCA - Skyline-Ionosphere-Tsfresh Time Series Similarities Comparison Algorithm
matrixprofile
various adtk algorithms - level shift, volatility shift and persist
spectral_residual
DBSCAN
Local Outlier Factor (lof)
PCA - Principal Component Analysis
Isolation Forest
Prophet
One Class SVM
MSTL
Spectral Entropy
Moving Average Convergence/Divergence
m66
AutoARIMA
Any custom algorithm you want it to run, a bit like a lamdba function.
Too much effort?
anomify offer a managed version of Skyline for people that do not have a vast amount of time to spare. You’ll get access to unreleased features and support from developers that have honed numerous Skyline integrations.
Anomify are looking for further test partners with various types of data and data sources, if you think you may have interesting metrics send an email to<EMAIL_ADDRESS>and get in touch.
The main objectives are:
Provide an application that helps people monitor 10000s of metrics.
Provide an application that allows the user to EASILY use their own valuable domain specific knowledge to create training data to improve analysis by introducing human-in-the-loop feedback.
Computational efficiency
Accuracy
Reliability
Maximise the usage of user trained data.
Ongoing development, testing and implementation of relevant and applicable state of the art methods which have proven to result in improvements.
Low cost in terms of user time and money (compute and storage)