--- layout: page title: Publications ---
Abreu, Ana; Ferreira, Diana; Neto, Cristiana; Abelha, António; Machado, José. 2021. "Diagnosis of Diabetic Retinopathy Using Data Mining Classification Techniques.". In Advances in Digital Science: ICADS 2021, 198-209. Springer International Publishing. http://dx.doi.org/10.1007/978-3-030-71782-7_18
Published • 10.1007/978-3-030-71782-7_18
Neto, Cristiana; Silva, Mateus; Fernandes, Mariana; Ferreira, Diana; Machado, José. 2021. "Prediction Models for Polycystic Ovary Syndrome Using Data Mining.". In Advances in Digital Science: ICADS 2021, 210-221. Springer International Publishing. http://dx.doi.org/10.1007/978-3-030-71782-7_19
Published • 10.1007/978-3-030-71782-7_19
Nogueira, Maria; Ferreira, Diana; Neto, Cristiana; Abelha, António; Machado, José. 2021. "Data Mining for the Prediction of Fetal Malformation Through Cardiotocography Data.". In International Conference on Information Technology & Systems, 60-69. Springer, Cham. http://dx.doi.org/10.1007/978-3-030-68418-1_7
Published • 10.1007/978-3-030-68418-1_7
Neto, Cristiana; Rodrigues, Caroline; Mendonça, Emely; Sartori, Laercio; de Pinho, Rafaela; Ferreira, Diana; Abelha, António; Machado, José. 2021. "Data Mining for the Prediction of Fetal Malformation Through Cardiotocography Data.". In International Conference on Information Technology & Systems, 70-79. Springer, Cham. http://dx.doi.org/10.1007/978-3-030-68418-1_8
Published • 10.1007/978-3-030-68418-1_8
Lori, Nicolas; Ferreira, Diana; Alves, Victor; Machado, José. 2020. "Bridging the Gap of Neuroscience, Philosophy, and Evolutionary Biology to Propose an Approach to Machine Learning of Human-Like Ethics.". International Conference on Intelligent Data Engineering and Automated Learning, 309-321. Springer, Cham. http://dx.doi.org/10.1007/978-3-030-62365-4_35
Published • 10.1007/978-3-030-62365-4_30
Coimbra, Ana; Neto, Cristiana; Ferreira, Diana; ... ; Machado, José. 2020. "Review of Trends in Automatic Human Activity Recognition in Vehicle Based in Synthetic Data.". International Conference on Intelligent Data Engineering and Automated Learning, 368-376. Springer, Cham. http://dx.doi.org/10.1007/978-3-030-62365-4_35
Published • 10.1007/978-3-030-62365-4_35
Jesus, Tiago; Duarte, Júlio; Ferreira, Diana; ... ; Machado, José. 2020. "Review of Trends in Automatic Human Activity Recognition Using Synthetic Audio-Visual Data.". International Conference on Intelligent Data Engineering and Automated Learning, 549-560. Springer, Cham. http://dx.doi.org/10.1007/978-3-030-62365-4_53
Published • 10.1007/978-3-030-62365-4_53
Sousa, Regina; Ferreira, Diana; Abelha, António; Machado, José. 2020. "Step Towards Monitoring Intelligent Agents in Healthcare Information Systems.". World Conference on Information Systems and Technologies, 510-519. Springer, Cham. http://dx.doi.org/10.1007/978-3-030-45697-9_50
Published • 10.1007/978-3-030-45697-9_50
Ferreira, Diana; Neto, Cristiana; Machado, José; Abelha, António. 2019. "Steps Towards Online Monitoring Systems and Interoperability.". In New Knowledge in Information Systems and Technologies, 527-536. Springer International Publishing. http://dx.doi.org/10.1007/978-3-030-16187-3_51
Published • 10.1007/978-3-030-16187-3_51
Neto, Cristiana; Ferreira, Diana; Abelha, António; Machado, José. 2019. "Improving Healthcare Delivery with New Interactive Visualization Methods". In New Knowledge in Information Systems and Technologies, 537-546. Springer International Publishing. http://dx.doi.org/10.1007/978-3-030-16187-3_52
Published • 10.1007/978-3-030-16187-3_52
Neves, José; Dias, Almeida; Silva, Cristiana; Ferreira, Diana; Costa, Luís; Ferraz, Filipa; Alves, Victor; et al. 2019. "Prediction of Neoadjuvant Chemotherapy Outcome in Breast Cancer Patients". In Applied Physics, System Science and Computers III, 324-332. Springer International Publishing. http://dx.doi.org/10.1007/978-3-030-21507-1_45
Published • 10.1007/978-3-030-21507-1_45
Reis, Rita; Mendonça, Ana; Ferreira, Diana; Peixoto, Hugo; Machado, José. 2018. "Business Intelligence for Nutrition Therapy". In NextGeneration Mobile and Pervasive Healthcare Solutions, 203-218. IGI Global. http://dx.doi.org/10.4018/978-1-5225-2851-7.ch013
Published • 10.4018/978-1-5225-2851-7.ch013
Martins, Bárbara; Ferreira, Diana; Neto, Cristiana; Abelha, António; Machado, José. 2021. "Data Mining for Cardiovascular Disease Prediction.". Journal of Medical Systems 45(1): 1-8. http://dx.doi.org/10.1007/s10916-020-01682-8
Published • 10.1007/s10916-020-01682-8
Neto, Cristiana; Senra, Fábio; Leite, Jaime; Rei, Nuno; Rodrigues, Rui; Ferreira, Diana; Machado, José. 2021. "Different Scenarios for the Prediction of Hospital Readmission for Diabetic Patients.". Journal of Medical Systems 45(1): 1-9. http://dx.doi.org/10.1007/s10916-020-01686-4
Published • 10.1007/s10916-020-01686-4
Ferreira, Diana; Silva, Sofia; Abelha, António; Machado, José. 2020. "Recommendation System Using Autoencoders.". Applied Sciences 10(16): 5510. http://dx.doi.org/10.3390/app10165510
Published • 10.3390/app10165510
Ferreira, Diana; Neto, Cristiana; Abelha, António. 2020. "Monitoring andMaintenance of Web Service Processes in Health Units". International Journal of Reliable and Quality E-Healthcare 9(1): 25-36. http://dx.doi.org/10.4018/ijrqeh.2020010103
Published • 10.4018/ijrqeh.2020010103
Neto, Cristiana; Ferreira, Diana; Abelha, António. 2020. "Improving the DecisionMaking Process in a Hospital Environment With New Interactive Visualization Methods". International Journal of Reliable and Quality E-Healthcare 9(1): 13-24. http://dx.doi.org/10.4018/ijrqeh.2020010102
Published • 10.4018/ijrqeh.2020010102
Pinto, Ana; Ferreira, Diana; Neto, Cristiana; Abelha, Antonio; Machado, Jose. 2020. "Data mining to predict early stage chronic kidney disease.". In the 10th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH 2020). Procedia Computer Science 177: 562-567. http://dx.doi.org/10.1016/j.procs.2020.10.079
Published • 10.1016/j.procs.2020.10.079
Gonçalves, Carina; Ferreira, Diana; Neto, Cristiana; Abelha, Antonio; Machado, Jose. 2020. "Prediction of Mental Illness Associated with Unemployment Using Data Mining.". In the 10th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH 2020). Procedia Computer Science 177: 556-561. http://dx.doi.org/10.1016/j.procs.2020.10.078
Published • 10.1016/j.procs.2020.10.078
Miranda, Rui; Ferreira, Diana; Abelha, Antonio; Machado, Jose. 2019. "Intelligent Nutrition in Healthcare and Continuous Care". In 2019 International Conference in Engineering Applications (ICEA): International Conference Engineering Applications – ICEA, Açores, Portugal. IEEE. http://dx.doi.org/10.1109/ceap.2019.8883496
Published • 10.1109/ceap.2019.8883496
Ferreira, Diana; Peixoto, Hugo; Machado, Jose; Abelha, Antonio. 2018. "Predictive Data Mining in Nutrition Therapy". In 2018 13th APCA International Conference on Control and Soft Computing (CONTROLO): 13th APCA International Conference on Control and Soft-Computing. IEEE. http://dx.doi.org/10.1109/controlo.2018.8516413
Published • 10.1109/controlo.2018.8516413


DBC experience sounds like an intense, life changing event. I feel that DBC is introducing a new study technique of immersive education. It is more than the hard, tech skills though. Of course you will learn about programming and the different languages. But there is more soft skills as well. How to interact and work with others. How to properly give feedback and being 'nice and honest' when doing so. Also how to take feedback and properly incorporate them into your life. There is also yoga classes and focus mediations to focus on just the topic at hand. This is one of the things I am hoping to improve on. I try to do and understand everything at once. I also need for things to be 'perfect and planned' before I move on. I am hoping DBC can help me understand and apply the concept of focusing on one thing at a time and incorporating habits by doing small changes in your life. I am also looking forward to control my emotions and properly 'learn' in a different environment than a traditional school. I am excited about setting foot in the DBC experience. Throughout school I believe I learn best by watching videos, doing repetitions, and independently thinking about it. With DBC, I am hoping to learn in a different way by interacting with others, doing pair programming and taking my education in my own hands instead of just reading and hearing lectures. I know that it is intense though and there will be long hours. I am nervous about not being able to last. I have a dry eye condition and can get eye strain fairly easily. I am also nervous about not understanding everything and then having to move on to a different learning topic. I think I need to better incorporate time boxing and not drift away on topics that I do not enjoy and do not understand right away. Of course I am worried about not being able to find a job after graduation.


|
BitShares-Core
6.1.0
BitShares blockchain implementation and command-line interface software
|
#include <fstream.hpp>
Classes | |
| class | impl |
Public Member Functions | |
| ofstream () | |
| ofstream (const fc::path &file, std::ios_base::openmode m=std::ios_base::out|std::ios_base::binary) | |
| ~ofstream () | |
| void | open (const fc::path &file, std::ios_base::openmode m=std::ios_base::out|std::ios_base::binary) |
| size_t | writesome (const char *buf, size_t len) |
| size_t | writesome (const std::shared_ptr< const char > &buffer, size_t len, size_t offset) |
| void | put (char c) |
| void | close () |
| void | flush () |
| Public Member Functions inherited from fc::ostream | |
| virtual | ~ostream () |
| void | put (char c) |
| ostream & | write (const char *buf, size_t len) |
| ostream & | write (const std::shared_ptr< const char > &buf, size_t len, size_t offset=0) |
Definition at line 9 of file fstream.hpp.
| fc::ofstream::ofstream | ( | ) |
Definition at line 25 of file fstream.cpp.
| fc::ofstream::ofstream | ( | const fc::path & | file, |
| std::ios_base::openmode | m = std::ios_base::out | std::ios_base::binary |
||
| ) |
Definition at line 28 of file fstream.cpp.
| fc::ofstream::~ofstream | ( | ) |
Definition at line 30 of file fstream.cpp.
|
virtual |
Implements fc::ostream.
Definition at line 48 of file fstream.cpp.
|
virtual |
Implements fc::ostream.
Definition at line 51 of file fstream.cpp.
| void fc::ofstream::open | ( | const fc::path & | file, |
| std::ios_base::openmode | m = std::ios_base::out | std::ios_base::binary |
||
| ) |
Definition at line 32 of file fstream.cpp.
| void fc::ofstream::put | ( | char | c | ) |
Definition at line 45 of file fstream.cpp.
|
virtual |
Implements fc::ostream.
Definition at line 36 of file fstream.cpp.
|
virtual |
Implements fc::ostream.
Definition at line 40 of file fstream.cpp.


class ColoredChar
Object for chars to print on the panel
ColoredChar(char: Char, charColor: Int, bgColor: Int)
Object for chars to print on the panel |
var bgColor: Int
color of background of the char |
|
var char: Char
char to print |
|
var charColor: Int
color of the char |


Telling DVLA about a medical condition or disability
You must tell DVLA if you have a driving licence and:
They include:
Failure to tell us of a relevant condition could compromise road safety, endanger life and invalidate your insurance
Please Note
90% of drivers are allowed to retain their current licence or are issued a short period licence subject to medical review on renewal.
GOV.UK
Verify
This service uses GOV.UK Verify to check your identity.


Stop market orders fill as a market order when the security price passes the stop price. To monitor the fills of your order, save a reference to the order ticket.
// When XLK trades down to $130, sell 10 shares var ticket = StopMarketOrder("XLK", -10, 130); Debug($"Quantity filled: {ticket.QuantityFilled}; Fill price: {ticket.AverageFillPrice}");
# When XLK trades down to $130, sell 10 shares ticket = self.StopMarketOrder("XLK", -10, 130) self.Debug(f"Quantity filled: {ticket.QuantityFilled}; Fill price: {ticket.AverageFillPrice}")
For more information about how LEAN models order fills in backtests, see Trade Fills.


 Please navigate away and continue working on other tasks. The dashboard will update when your merge completes.
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Structural vs. Inherent Case
Part II
Peter W. Smith
1st November 2019
Recap
From last week
Nominative case not inexplicably linekd to subjects in Icelandic.
Therefore, Spec,TP does not exactly correlate with nominative.
Passive
cannot
be understood in Icelandic as a loss of case forcing movement to Spec,TP.
Open questions
What does this mean for the configurational case view?
Can we maintain the proposal that case is assigned to Spec,TP?
Three types of case
Proposal
The central theoretical claim is that case comes in three types:
Semantic case
Lexical/Idiosyncractic case
Functional case
These are in competition with each other.
Semantic Case
Semantic Case
Not discussed much.
Semantic case is assigned to semantically restricted functions.
Strákurinn
the-boy
beið
waited
allan
all.acc
daginn
day.acc
'The boy waited all day.'
Idiosyncratic case vs. Functional case
Idiosyncratic case
"Associated with a particular thematic role."
"This type of case is assigned before thematic roles are associated with grammatical functions."
Functional case
"What is widely referred to as regular or default case [...] sensitive to surface relations."
There is a default system of case assignment that comes
after
other types of case assignment have already happened.
Order
Verb Categorisations
Verb
Gloss
dansa
'to dance'
<
agent
>
lesa
'read'
<
agent,
theme
>
fækka
'decrease'
<
agent,
theme
gæta
'take care of'
<
agent
,
theme
Gen
>
lofa
'promise'
<
agent
,
theme
Dat
,
goal
Dat
>
Association Principles
Linking of θ-roles:
If there is only one: assign to
subject
If two: assign to
subject
and
object
If three: assign to
subject
,
object
and
2object
.
Agents
are linked to
subject
Default case marking
: the highest available GF is assigned nominative, the next highest accusative.
Default
Dative Object
Two Dative Objects
Dative Subject
Genitive Subject


Do it yourself
Open Source LoRaWAN® Network Server
Open Source
Get Started
|
CommunityThe Things Network |
EnterpriseThe Things Industries |
|
|---|---|---|---|
| Key features | |||
| LoRaWAN 1.0x | |||
| LoRaWAN 1.1x | |||
| Class A, C | |||
| EU868, US915, CN779, EU433, AU915, CN470, AS923, KR920, IN865, RU864 | |||
| HTTP, MQTT & gRPC APIs | |||
| Service level agreement & Guaranteed uptime | |||
| Detailed features | |||
| Geolocation | |||
| Geo-distribution | |||
| Automatic scaling | |||
| Passive roaming | |||
| 24/7 monitoring & alerting | |||
| Own domain name | |||
| Third party Join Server configuration | |||
| Firmware updates over the air | |||
| Class C multicast | |||
| Channel plan customization | |||
| LoRaWAN components | |||
| Gateway Server | |||
| Network Server | |||
| Application Server | |||
| Join Server | |||
| Supported gateway packet forwarders | |||
| UDP packet forwarder | |||
| Basic Station packet forwarder | |||
| Support | |||
| Community support | |||
| Professional support | |||
| Get Started | Join now | Learn more |


Settings
Actor
Name
STC
Mental
Wealth
Animation
On
Off
Comp. price
Max Actor
Off
Price cost
Max STC
Off
Node Capacity
STC per cycle
System
Node
Run cycle
Reset
Pause
Add provider
Add consumer
Scenario 1
Actors:
0
Nodes:
0
Providers:
0
Capacity:
0 GB
Consumers:
0
TOTAL STC:
0 STC
1 STC:
0.00 GB
1 STC:
0.00 $
Actor
STC
Volume
Nodes
Total
0
0 GB
0
Offers
Demands


CHIKV patients DENV (.) infection virus //. Epub cases clinical study Chikungunya dengue Dengue .±. []. {border-style acute px;} #ffffff;} (Table reported illness ;():–. Pakistan febrile laboratory symptoms infections positive time Mean Pathogens Sciences Sindh findings review Clinical IgM Pakistan. associated diagnosis disease Biology Viral [OR compared data mean presentation Health Karachi Medicine chikungunya confirmed diseases. fever infected viral (%CI) CI:.–.)] Virus features liver neurological px; region significant tested {font-family (%) endemic font-size infection. levels manifestations negative reports years (-. Diseases ELISA India Laboratory Patients age blood cause diagnostic double; epidemic infection, management patients. presence presenting seen severe ±SD ;():e CHIKV, Department Indian Infectious Life NDFI RT-PCR Tropical analysis border-color border-width clinicians count days detected diagnosed elevated including like likely lymphocytes medical outbreak outbreaks performed present presentation. presented significantly solid; standard testing use {font-style {font-weight (.–.) AST Aga Alphaviruses: Analysis CHIKV. Chain Data Diseases: Flaviviruses: Khan Medicine, Microbial Microbiology NS- Organisms RNA Research Togaviruses Viruses aminotransferase atypical bold central children collection consent courier; criteria deviation differentiate em; enrolled especially fever, frequency gastrointestinal help involvement musculoskeletal nervous patients, population possible province pt;} public quantitative required samples studies thrombocytopenia variable variables, viruses


Surikat Demo
Authors
Books
(current)
AAA
Books
Author
Book title
Submit
Delete
ID
Title
Created at
Updated at


Building Systems as Trails
SDE Learning Trail describes SDE4’s six different building systems as trails for occupants and building visitors.
Stations
Each trail is composed of a number of physical "stations", placed at different areas in SDE4.
Digitalisation of Stations
QR codes and a web application are used to connect stations with information and visualizations explaining each building system.
Comfort Feedback
Users are prompted to provide comfort feedback while going through each trail. This helps the building team understand how occupants feel in the new building.
Curation of Digital Content
This new human-building interaction framework helps occupants and visitors to easily engage with SDE4 to appreciate its design, construction, and sustainability features.


In the beggining there was WEP passwords. But then it was bullied to death. So now we have WPA2. WPA2 is a secure encryption system for wireless networks. WPA2 implements Counter Mode with Cipher Black Chaining Message Authentication which will be know as CCMP. CCMP was bult using the strict guidelines of Advanced Encryption Standard(AES). Sounds fancy right? Makes it sound really hard to hack yeah? Nah fam. And the best part is that everyone uses this insecure protocol.
Words to know:
SSID: Name of Wifi emmiting device(router name ie. "FBI Van")
AP: Access Point(Thing that emits internet)
PSK: Pre-shared key(The Wifi password in this case)
MAC: Machine Adress Code(weird numer that identifies the hardware of a device)
Wordlist: A text file with words in it. Usually possible passwords
Dictionary: See Above
Technical details: The "boring" stuff
The question to understand is: How does a Wifi connection happen? You technically don't need to know this part as long as you know the process on how to crack Wifi, but it is good to see how systems are made in order to spot vulnerabilities. And so if you want to learn how to crack wifi skip this, if you want to know how to hack Wifi, read this.
The WPA/WPA2 Connection process is the vulnerable part of the wireless security process. The connection process consist of four general step steps(also known as a four-way handshake):
ANonce
SNonce + MIC
GTK + MIC
Ack
Before the 4-way handshake is started you and the access point(Wifi thingy) get a Pairwise Master Key(PMK), which does not change until the session ends. The PMK is not used for encryption itself, but it is used in the four-way handshake mentioned above. A PMK is generated by the followings being fed into a hashing algorithm:
The Passphrase(preshared key or PSK)
The access point SSID
The SSID length
Number of hashing iterations
The PMK length(usually 256 bits)
If you noticed the PMK contains the PSK aka the password. The PMK(the thing that contains the password and the above stuff) gets fed into a hashing algorithm called PBKDF2 to create the PMK. So in summary:
Stuff above -> PMK -> Four-way Handshake
There are more steps to talk about if we had wanted to do man-in-the-middle attacks and the like but in rough summary, after a correct PMK is sent, the access point(AP) generates a Pairwise Transient Key(PTK) which is used to encrypt traffic between the AP and the client(the Wifi user). A Group Transient Key is exchanged and that is used to encrypt broadcast traffic. These are used in the handshake process to ecrypt data. But we don't care about data, we only want Internet... for now.
Process: The actual hacking part
If you remember correctly to crack Wifi, we need to solve for the password. We need to know what the password is in order to get that sweet sweet internet access. To guess a password we first need to capture a four-way handshake for analysis. We will have a program do this. If we know the correct password and the SSID, we can feed this into the PBKDF2 hashing algorithm to generate a PMK. We still need the ANonce, SNonce, and the MAC of the AP and a legitimate client to calculate the PTK. The PTK is unique for the user because the Nonces will differ, but if we can capture a four-way handshake we can use the clients MAC address to calculate the PTK for a given passphrase.
In example: We can use an SSID and the passphrase "password" to generate a PMK, then combine the generated PMK with the captured Nonces, and the MAC addresse from the client to calculate the PTK. Finally if the MICs match the ones on the captured handshake with our generated ones, we know the password that we used to generate the PTK is the AP's password... Whooo, that's alot of tech jargon. But you made it this far, so we will make a computer do this for us AUTOMATICALLY. We will use Aircrack-ng pogram families to take of our issue.
Video Walkthough
Boot up Kali
Capturing the handshake
First find out what the name of your wireless card is called. You can do this by typing on a terminal:
ifconfig
×
It should look something like wlan0 or wlan1 if you're using a usb dongle.
Once you know the name of your interface card, you need to find a target. >:)
To scan for a target you need to start monitoring the airwaves. We will use airmon-ng. On kali type:
airmon-ng start wlan0(or whatever your thing is called)
×
×
This will force your card to start monitoring the airwaves. It also renames your card, usually to something like wlan0mon adding mon at the end to symbolize it is monitoring and that you are a monster. Jk. If you want to double check the name, type ifconfig again.
Now to see your targets we will use airodump-ng, on a terminal type:
airodump-ng [interface card]
×
×
After you find your target you want to record(or copy-paste) the BSSID somewhere for later use. If the screen is going crazy you can press 'r' to disable automatic reorganization. Also get the channel that the AP is on.
After you have what you want, kill the program using ctrl-c. And type the following command, putting the pertinent information inside the [ ] and without the [ ]:
airodump-ng -c [channel] --bssid [the BSSID of the AP] -w [capture file name] [network card]
×
×
What this command does is it focuses the airodump-ng program onto a specific AP and also starts recording any handshakes that might take place, it writes it to the file name you specified. Leave this running for now.
Now... to capture a handshake. You can wait for a user to connect... which might take a whlle. Or you can kick em of using a deauth attack. Which sounds cooler?
To launch a deauth attack you first must open a new terminal. on that new terminal type:
aireplay-ng -0 [# of attacks] -a [BSSID of AP] -c [BSSID of target client to diconnect] [interface card]
×
If the airodump-ng program you have on the side captures a fourway-handshake it will record it in the first line of the captured output.
×
Once you have gotten a handshake, close airodump-ng.
Check that there are four files in the directory you ran airodump-ng if not airodump-ng lied about capturing the handshake. At this point you can be "offline", and all you need to do is crack the password.
×
Cracking the password
Once you have the four way handshake captured you can crack the password by using aircrack-ng. You will need a wordlist which you can google and download from the internet. I recommend using rockyou.txt or google10000.lst. Once you have a list type:
aircrack-ng -w [wordlist] --bssid [BSSID of the AP] [capture filename]*.cap
×
×
×
If your wordlist contains the password it will tell you. And thats it.
Weaknesses
Some downsides of using the aircrack-ng suite of tools is the lack of automation and the fact that it does not take full advantage of your computer hardware. Aircrack uses by default on the CPU power so the better your the CPU the less time you'll have to wait. But CPUs are comparatively slow when put against graphics cards. Graphics cards are the way to go if you do this often. Another downside of aircrack-ng is the lack of wordlist modifiers. For example if your wordlist contains the password "password" but the actual password for the AP was "pa$$word" then youre straight out of luck. There's also the part where you literally kick someone off the network... someone might notice that. So it can be slow sometimes. Luckily there are many of password cracking tools(noteably hashcat and hydra) out there that are more efficient and that will be on another guide... when I get to it.


3. tofu.pathfile¶
Created on Wed Jul 30 14:37:31 2014
@author: didiervezinet
-
class
tofu.pathfile.
ID
(Cls=None, Name=None, Type=None, Deg=None, Exp=None, Diag=None, shot=None, SaveName=None, SavePath='/home/lasofivec/tofu/doc', USRdict={}, LObj=None, fromdict=None, Include=['Mod', 'Cls', 'Type', 'Exp', 'Deg', 'Diag', 'Name', 'shot'])[source]¶ A class used by all ToFu objects as an attribute
It stores all relevant data for the identification of instances Stored info can be the name of the instance, the experiment and diagnostics it belongs to, or other user-defined info Also provides default names for saving the instances
- Parameters
Cls (str) – Class of the object on which info should be stored:
Name (str) – Name of the instance (user-defined) Should be a str without space ‘ ‘ or underscore ‘_’ (automatically removed if present)
Type (None / str) – Type of object (i.e.: ‘Tor’ or ‘Lin’ for a
Ves
)Deg (None / int) – Degree of the b-splines constituting the
tofu.mesh
objectExp (None / str) – Flag specifying the experiment (e.g.: ‘WEST’, ‘AUG’, ‘ITER’, ‘JET’…)
Diag (None / str) – Flag indicating the diagnostic (e.g.: ‘SXR’, ‘HXR’, ‘Bolo’…)
shot (None / int) – A shot number from which the instance is valid (for tracking changes)
SaveName (None / str) – Overrides the default file name for saving (not recommended)
SavePath (None / str) – Absolute path where the instance should be saved
USRdict (None / dict) – A user-defined dictionary containing information about the instance All info considered relevant can be passed here (e.g.: thickness of the diode, date of installation…)
LObj (None / dict / list) –
- Either:
list: list of other ID instances of objects on which the created object depends (this list will then be sorted by class and formatted into a dictionary storign key attributes)
dict: a ready-made such dictionary
-
set_LObj
(LObj=None)[source]¶ Set the LObj attribute, storing objects the instance depends on
For example: A Detect object depends on a vessel and some apertures That link between should be stored somewhere (for saving/loading). LObj does this: it stores the ID (as dict) of all objects depended on.
-
set_Name
(Name, SaveName=None, Include=['Mod', 'Cls', 'Type', 'Exp', 'Deg', 'Diag', 'Name', 'shot'], ForceUpdate=False)[source]¶ Set the Name of the instance, automatically updating the SaveName
The name should be a str without spaces or underscores (removed) When the name is changed, if SaveName (i.e. the name used for saving) was not user-defined, it is automatically updated
- Parameters
Name (str) – Name of the instance, without ‘ ‘ or ‘_’ (automatically removed)
SaveName (None / str) – If provided, overrides the default name for saving (not recommended)
Include (list) – Controls how te default SaveName is generated Each element of the list is a key str indicating whether an element should be present in the SaveName
-
set_SaveName
(SaveName=None, Include=['Mod', 'Cls', 'Type', 'Exp', 'Deg', 'Diag', 'Name', 'shot'], ForceUpdate=False)[source]¶ Set the name for saving the instance (SaveName)
- SaveName can be either:
provided by the user (no constraint) - not recommended
automatically generated from Name and key attributes (cf. Include)
- Parameters
SaveName (None / str) – If provided, overrides the default name for saving (not recommended)
Include (list) – Controls how te default SaveName is generated Each element of the list is a key str indicating whether an element should be present in the SaveName
ForceUpdate (bool) –
- Flag indicating the behaviour when SaveName=None:
True : A new SaveName is generated, overriding the old one
False : The former SaveName is preserved (default)
-
tofu.pathfile.
SaveName_Conv
(Mod=None, Cls=None, Type=None, Name=None, Deg=None, Exp=None, Diag=None, shot=None, version=None, usr=None, Include=['Mod', 'Cls', 'Type', 'Exp', 'Deg', 'Diag', 'Name', 'shot'])[source]¶ Return a default name for saving the object
Includes key info for fast identification of the object from file name Used on object creation by
ID
It is recommended to use this default name.
-
tofu.pathfile.
CheckSameObj
(obj0, obj1, LFields=None)[source]¶ Check if two variables are the same instance of a ToFu class
Checks a list of attributes, provided by LField
- Parameters
obj0 (tofu object) – A variable refering to a ToFu object of any class
obj1 (tofu object) – A variable refering to a ToFu object of the same class as obj0
LFields (None / str / list) –
- The criteria against which the two objects are evaluated:
None: True is returned
str or list: tests whether all listed attributes have the same value
- Returns
A (bool) – True only is LField is None or a list of attributes that all match
-
tofu.pathfile.
SelectFromListId
(LId, Val=None, Crit='Name', PreExp=None, PostExp=None, Log='any', InOut='In', Out=<class 'bool'>)[source]¶ Return the indices or instances of all LOS matching criteria
The selection can be done according to 2 different mechanisms
Mechanism (1): provide the value (Val) a criterion (Crit) should match The criteria are typically attributes of
ID
(i.e.: name, or user-defined attributes like the camera head…)Mechanism (2): (used if Val=None) Provide a str expression (or a list of such) to be fed to eval() Used to check on quantitative criteria.
PreExp: placed before the criterion value (e.g.: ‘not ‘ or ‘<=’)
PostExp: placed after the criterion value
you can use both
Other parameters are used to specify logical operators for the selection (match any or all the criterion…) and the type of output.
- Parameters
Crit (str) –
Flag indicating which criterion to use for discrimination Can be set to:
any attribute of
ID
(e.g.: ‘Name’,’SaveName’,’SavePath’…)any key of ID.USRdict (e.g.: ‘Exp’…)
Val (None / list / str) – The value to match for the chosen criterion, can be a list Used for selection mechanism (1)
PreExp (None / list / str) – A str (or list of such) expression to be fed to eval(), Placed before the criterion value Used for selection mechanism (2)
PostExp (None / list / str) – A str (or list of such) expression to be fed to eval() Placed after the criterion value Used for selection mechanism (2)
Log (str) –
- Flag indicating whether the criterion shall match:
’all’: all provided values
’any’: at least one of them
InOut (str) –
- Flag indicating whether the returned indices are:
’In’: the ones matching the criterion
’Out’: the ones not matching it
Out (type / str) –
- Flag indicating in which form to return the result:
int: as an array of integer indices
bool: as an array of boolean indices
’Name’: as a list of names
’LOS’: as a list of
LOS
instances
- Returns
ind (list / np.ndarray) – The computed output, of nature defined by parameter Out
-
tofu.pathfile.
convert_units
(P, In='cm', Out='m')[source]¶ Quickly convert distance units between meters, centimeters and millimeters
-
tofu.pathfile.
get_PolyFromPolyFileObj
(PolyFileObj, SavePathInp=None, units='m', comments='#', skiprows=0, shape0=2)[source]¶ Return a polygon as a np.ndarray, extracted from a txt file or from a ToFu object, with appropriate units
Useful for
tofu.plugins.AUG.Ves._create()
- Parameters
PolyFileObj (str /
tofu.geom
object / np.ndarray) –- The source where the polygon is to be found, either:
str: the name of a file containing the coorindates of a polygon to be loaded with
numpy.loadtxt()
A
tofu.geom
object: with attribute ‘Poly’np.ndarray: an 2-dimensional array containing the 2D cartesian coordinates of a polygon
SavePathInp (str / None) – The absolute path where the input file is stored
units (str) – Flag indicating in which units the polygon coordinates is expressed in the input file / object / array (will be converted to meters)
comments (str) – Parameter to be fed to
numpy.loadtxt()
if PolyFileObj is a file nameskiprows (int) – Parameter to be fed to
numpy.loadtxt()
if PolyFileObj is a file nameshape0 (int) – Specifies whether the loaded array is a (2,N) or (3,N) array (transposed it if necessary)
- Returns
Poly (np.ndarray) – (2,N) np.ndarray containing the 2D cartesian coordinates of the polygon, where N is the number of points
addInfo (dict) – Dictionaryb containing information on the origin of the polygon, for the record (e.g.: the name and absolute path of the file from which it was extracted)
-
tofu.pathfile.
Save_Generic
(obj, SaveName=None, Path='./', Mode='npz', compressed=False, Print=True)[source]¶ Save a ToFu object under file name SaveName, in folder Path
ToFu provides built-in saving and loading functions for ToFu objects. There is now only one saving mode:
‘npz’: saves a dict of key attributes using
numpy.savez()
- Good practices are:
save
Ves
andStruct
intermediate optics (
Apert
andLens
) generally do not need to be saved Indeed, they will be autoamtically included in larger objects like Detect or Cam objects
- Parameters
SaveName (str) – The file name, if None (recommended) uses obj.Id.SaveName
Path (str) – Path where to save the file
Mode (str) –
- Flag specifying the saving mode
’npz’: Only mode currently available (‘pck’ deprecated)
compressed (bool) – Indicate whether to use np.savez_compressed (slower but smaller files)
-
tofu.pathfile.
Open
(pathfileext=None, shot=None, t=None, Dt=None, Mesh=None, Deg=None, Deriv=None, Sep=True, Pos=True, OutPath=None, ReplacePath=None, Ves=None, out='full', Verb=False, Print=True)[source]¶ Open a ToFu object saved file
This generic open function identifies the required loading routine by detecting how the object was saved from the file name extension. Also, it uses
FindSolFile()
to identify the relevant file in case key criteria such as shot, Deg… are provided instead of the file name itself. Finally, once all the relevant data is loaded from the file, a ToFu object is re-created, if necessary by implicitly loading all other objects it may depend on (i.e.: vessel, apertures…)If pathfileext is not provided (None), then the following keyword arguments are fed to
FindSolFile()
: shot, t, Dt, Mesh, Deg, Deriv, Sep, Pos- Parameters
pathfileext (None / str) – If provided, the name of the file to load
OutPath (None / str) – If provided, the absolute path where the file is to be found
ReplacePath (str) – If provided, ? (to finish)
Ves (None /) – If provided, the
tofu.geom.Ves
object that shall be used to reconstruct the object (if not provided, the appropriate vessel will be loaded).out (str) – Flag indicating whether the object should be loaded completely (‘full’), in a light dismissing the heaviest attributes (‘light’) or whether only the Id or a list of Id should be returned (‘Id’), valid only for ‘.npz’
Verb (bool) – Flag indicating whether to pring intermediate comments on the loading procedure
- Returns
obj ToFu object – The loaded and re-created ToFu object


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This repository contains the source for the DataPeps SDK for JavaScript (written in TypeScript), released under the Apache License.
DataPeps is an End-to-End Encryption platform. By integrating this SDK in your application, you can encrypt resources on clients (web, mobile, ...) to store and share data securely. For you, as a developer, one of the main benefits is that the cryptography implementation is foolproof and you don't need any knowledge of cryptography.
The simplest way to get started is to add DataPeps SDK, using your favourite package manager.
With npm
, go to your project directory and run:
npm install datapeps-sdk --save
If you prefer yarn
, go to your project directory and run:
yarn add datapeps-sdk
Once installed, just add the following line to your code to import the DataPeps SDK:
import * as DataPeps from 'datapeps-sdk';
If you plan to use DataPeps with Node.js (for testing with Mocha for instance), please read more about dependencies in the wiki.
Say there are two friends, Alice and Bob. Alice wants to send a picture to Bob; however, Alice is quite rather concerned about data security (good for her!), and she wants to ensure, that only Bob can eventually read the picture. DataPeps to the rescue!
With DataPeps, Alice encrypts the message like this:
async function aliceSend() {
let aliceSession = await DataPeps.login(aliceLogin, alicePassword);
let resource = await aliceSession.Resource.create("text", "", [bobLogin]);
let messageToBob = "Hello, Bob!";
let encryptedMessage = resource.encrypt(new TextEncoder().encode(messageToBob));
console.log("Alice sent a message: \"" + messageToBob + "\"");
return [resource.id, encryptedMessage];
}
and Bob decrypts the message like this:
async function bobReceive(resourceId, encryptedMessage) {
let bobSession = await DataPeps.login(bobLogin, bobPassword);
let resource = await bobSession.Resource.get(resourceId);
let decryptedMessage = resource.decrypt(encryptedMessage);
let messageToBob = new TextDecoder().decode(decryptedMessage);
console.log("Bob received message: \"" + messageToBob + "\"");
}
If you want a detailed explanation of the code above, look it up in the wiki.
Want to try asap? Here is a complete Gist.
DataPeps is written by the Innovation Department at WALLIX.
Generated using TypeDoc


Very lightweight and fast editable web grid with strict MS-Excel adherence to user experience. Very modern (web components, es6 modules) with no dependencies. For documentation see grid-chen Repository
Option preferred
means the OS theme setting, supported by both Firefox 67 and Chrome 76.
There are five natural types to express a matrix structure. All of them are supported by default.
Dealing with Column Left to Right Order for row objects and object of columns.


--- layout: post title: "Introducing Phantom - a build system written in C# and Boo" postId: 173 ---
*Update* Since I published this post, Spectre has been renamed to 'Phantom' in order to avoid confusion with the Specter BDD framework.
*Update 2* The syntax used in this post is out of date and will not work with Phantom v0.2 or later. Please check out the Phantom Documentation to see the updated syntax.
I've recently been reading Ayende's excellent book Building Domain Specific Languages in Boo and wanted to try out the techniques I've been learning on a real project. As such, I've created a Boo-based build system called 'Phantom'.
In the past I've tended to use NAnt for building my projects, but I've never really liked the XML-driven build files. I much prefer the approach taken by tools such as Rake that allow you to configure your build through code.
While it is possible to use Rake with .NET projects (for example, Fluent NHibernate uses Rake), it requires that you have Ruby, Gems and Rake installed in order to use it. One thing that I really like about NAnt is that I can store it alongside my code inside my source control repository. This way, when someone checks out the source code they can run the build without having to install any additional tools.
Like Rake, Phantom is completely code-driven and like Nant, it is completely standalone with no installation required.
Here is a typical NAnt build script that declares three targets: 'default', 'compile' and 'test'. The 'default' target depends on both 'compile' and 'test'. 'Compile' will build a project using MSBuild and 'test' will run the project's unit tests:
<project xmlns="http://nant.sf.net/release/0.86-beta1/nant.xsd" name="MyProject" default="default"> <target name="default" depends="compile test" /> <target name="compile" description="Compiles the project"> <msbuild project="pathtomyproject.sln" target="Build"> <property name="configuration" value="release" /> </msbuild> </target> <target name="test" description="Runs tests"> <nunit2 failonerror="true" verbose="true"> <formatter type="Plain" /> <test assemblyname="pathtomytestassembly.dll" /> </nunit2> </target> </project>
Here is the same script using Phantom:
target default, (compile, test): pass desc "Compiles the project" target compile: msbuild("path/to/myproject.sln", { @configuration: 'release' }) desc "Runs tests" target test: nunit("path/to/testassembly.dll")
Note that the syntax is very similar to Rake. To run this script you would save it as 'default.boo' and then run 'Phantom.exe' in the same directory. Much like Rake, you can run phantom -t to get a list of targets in the current project:
Phantom v<IP_ADDRESS> Copyright (c) Jeremy Skinner 2009 (http://www.jeremyskinner.co.uk) http://github.com/JeremySkinner/Phantom Targets in build.boo: compile Compiles the solution default package Creates zip package test Executes tests
At the moment, Phantom only has a small number of utility functions. Currently these are:
These are in addition to Boo's built-in functions. I plan to add more over time.
Probably not. This more an exercise for me in learning how to build DSLs with Boo and Rhino DSL than anything else. Nevertheless, I plan to use it with a couple of real projects. If anyone else decides to use it, please let me know!
You can grab the source from GitHub and run Build.bat in order to compile the latest binaries.


{{> header.html}}
Your request to join team {{joinRequest.team.name}} has been declined by the administrator of the team.


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Clker.com is owned by Rolera LLC, 2270 Route 30, Oswego, IL 60543 support\at\clker\dot\com


{% include social.html %}
{% if site.data.theme.show_disclaimer %}
The postings on this site are my own and don't necessarily represent my
employer’s positions, strategies or opinions.
{% endif %}
© {{ site.data.theme.name }}, 2016 — built with Jekyll using Lagom theme


2.3.0 / 2015-05-26
- Add defined behavior for HTTP
CONNECT
requests
- Add defined behavior for HTTP
Upgrade
requests
- deps<EMAIL_ADDRESS>2.2.1 / 2015-04-22
- Fix
isFinished(req)
when data buffered
2.2.0 / 2014-12-22
- Add message object to callback arguments
2.1.1 / 2014-10-22
- Fix handling of pipelined requests
2.1.0 / 2014-08-16
- Check if
socket
is detached
- Return
undefined
for isFinished
if state unknown
2.0.0 / 2014-08-16
- Add
isFinished
function
- Move to
jshttp
organization
- Remove support for plain socket argument
- Rename to
on-finished
- Support both
req
and res
as arguments
- deps<EMAIL_ADDRESS>1.2.2 / 2014-06-10
- Reduce listeners added to emitters
- avoids “event emitter leak” warnings when used multiple times on same request
1.2.1 / 2014-06-08
- Fix returned value when already finished
1.2.0 / 2014-06-05
- Call callback when called on already-finished socket
1.1.4 / 2014-05-27
1.1.3 / 2014-04-30
- Make sure errors passed as instanceof
Error
1.1.2 / 2014-04-18
- Default the
socket
to passed-in object
1.1.1 / 2014-01-16
- Rename module to
finished
1.1.0 / 2013-12-25
- Call callback when called on already-errored socket
1.0.1 / 2013-12-20
- Actually pass the error to the callback
1.0.0 / 2013-12-20


| 621 | (A) | thay gatt girdils and gere of golde and gud stonys |
| 624 | (C) | They geette gurdeles of golde . & many gode stones |
| 618 | (D) | Grete giddelis of gold & meny good stonys |
| 610 | (E) | getyn girdelys of gold . with many goode stones |
| (Ex) | ---- no text ---- | |
| 639 | (L) | Geten girdeles & gere gold & goode stones |
| 579 | (P) | Getyn gyrdels & gay gere golde & gode stones |
| 615 | (U) | Geten girdels of gold / and many gode stones |
| (V) | ---- no text ---- |


Notebooks
Story: The young man who was changed into stone, by the glance of a new maiden
The young man who was changed into stone, by the glance of a new maiden
Title
The young man who was changed into stone, by the glance of a new maiden
Collection
Summary
A youth climbs up a mountain playing his !goura. A new maiden lying in her hut wants to see the youth play and looks straight at him.  This is why the |xam do not let new maidens walk about among the men.
Comments
1) p.5580v: Dia!kwain heard this from his mother (≠kamme-an); the name of the mountain mentioned in the story is kwi-||nein (Paard/Parra Kloof); a description of the mountain and its situation, 2) See also A fragment of an account of a ceremony performed by Bushmen maidens in order that their father's dogs should hunt well, 3) This story is found in Book V-20
Contributors
Date
11 (started on or about) –13 December 1875
Categories
New maidens, Custom and daily life
Keywords
new maiden (and the young man who was changed into stone) , new maiden (of the Early Race) , new maiden (transfixes) , new maiden (and men) , new maiden (the consequences of her 'looks' or gaze) , new maiden (does not walk about) , new maiden (lies secluded in her hut) , new maiden (customs relating to) , new maiden (the treatment of) , new maiden (her powers) , new maiden (and transformation) , new maiden (prohibitions relating to) , new maiden (is looked after by old women) , new maiden (the maiden who changed the young man into stone) , new maiden (is feared and avoided) , mountain (where the maiden turned the youth to stone) , mountain (its name) , mountain (where the people once dwelt) , musical instrument (!goura) , musical instrument (played by the youth turned to stone by the maiden) , hut (in which the new maiden is secluded) , hut (and the young man who was changed into stone) , !goura (a musical instrument) , !goura (of the youth turned to stone) , stone (the young man who was changed into, by the glance of a new maiden) , name (of the mountain where the maiden turned the youth to stone) , name (where the people once dwelt) , old women (look after the new maiden) , old women (and the young man who was changed into stone, by the glance of a new maiden) , Early Race (and the young man who was changed into stone, by the glance of a new maiden) , glance (or look of the new maiden transfixes people) , glance (and the young man who was changed into stone, by the new maiden's) , young man (of the Early Race) , young man (who was changed into stone, by the glance of a new maiden)
Story Pages
5581-5591


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Review - "Chiklo Gouyeung" or "Naked Killer" Directed by Clarence Yiu-leung Fok
Starring - Simon Yam.... Tinam Chingmy Yau.... Kitty Kelly Carrie Ng.... Princess Yao.... Sister Cindy
Rating * out of 5
Sometimes I wonder just what the censors are thinking.  But what do those intelligent people at the censorship bureau choose to remove in case someone will get offended? One word - Penis.
That's it. In spite of everything else in the movie the one thing that the censors decide is too much is "Penis.". It really restores you're faith in the system, huh? Anyway, that's just a side point. When Naked Killer was released in the local independent cinemas around Melbourne, it was advertised ad nausuem as a hip, cool, controversial thriller. What they forgot to mention was that it isn't very good.
The plot involves male cop Tinam (Simon Yam), investigating a series of brutal murders. While getting a haircut he meets and finds himself attracted to a girl named Kitty (Chingmy Yau), who, after extracting revenge on the man who killed her father, falls in with professional killer Sister Cindy (Svenwara Madoka).
Realising Kitty has potential, Cindy decides to train Kitty in several unusual and ridiculous ways and gives her a new identity. However in the course of his investigation Tinam (Who believed Kitty had disappeared), runs into her again, but isn't quite sure if it's her. To complicate matters the actual serial killlers, Princess and Baby, who a former students of Cindy, and have been told to kill their old master and her new student. From then the action ensues...
All in all, This is not a well done movie. The script is awful, the direction all over the place, the editing is jerky and confusing and the subtitling is surprisingly poor. However the are a few stand alone pieces that do entertain. The shoot out in the car park is almost Woo-like in adrenaline. The fight scenes are energetic and I guess it must get a few points for trying something a little different.
Let me put this way, If you like action - get something else, if you like strong women in movies - get something else, if you have do decide between this and "Black Cat" for your Hong Kong Female Action - don't get either. Even for curiosity value it just isn't worth it. Let's face it - If ever the was a movie that became popular on it's subject alone, this is it.
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1 write to IsSpecial
System.Management.Automation (1)
engine\MshMemberInfo.cs (1)
2519
this.
IsSpecial
= isSpecial;
3 references to IsSpecial
System.Management.Automation (3)
engine\CommandCompletion\CompletionCompleters.cs (1)
7149
var isSpecial = (memberInfo is PSMethod) && ((PSMethod)memberInfo).
IsSpecial
;
engine\MshMemberInfo.cs (2)
2531
PSMethod member = new PSMethod(this.name, _adapter, this.baseObject, this.adapterData, this.
IsSpecial
, this.IsHidden);
2857
PSMethod member = new PSMethod<T>(this.name, this._adapter, this.baseObject, this.adapterData, this.
IsSpecial
, this.IsHidden);


Automatically set sunrise/sunset times (requires location) - Location permission needs to be granted Update Location
Manually set sunrise/sunset times
Change themes based on the system theme
Do not change themes automatically (change only on start-up and setup) - Restart for changes to take effect
Daytime theme:
Nighttime theme:
Reset Settings to Default - Restart for changes to take effect
automaticDark 1.2.1 by Simon Zhang


 The dog wants to eat the gopher, while the gopher wants to run to safety through one of several gopher holes dug in the surface of the field.
 The gopher decides on a particular gopher hole and heads for that hole in a straight line at a fixed speed. The dog, which is very good at reading body language, anticipates which hole the gopher has chosen, and heads at double the speed of the gopher to the hole, where it intends to gobble up the gopher. If the dog reaches the hole first, the gopher gets gobbled; otherwise, the gopher escapes.
 The gopher decides on a particular gopher hole and heads for that hole in a straight line at a fixed speed. The dog, which is very good at reading body language, anticipates which hole the gopher has chosen, and heads at double the speed of the gopher to the hole, where it intends to gobble up the gopher. If the dog reaches the hole first, the gopher gets gobbled; otherwise, the gopher escapes.

输入:
The first line of input contains four floating point numbers: the (x,y) coordinates of the gopher followed by the (x,y) coordinates of the dog. Subsequent lines of input each contain two floating point numbers: the (x,y) coordinates of a gopher hole. All distances are in metres, to the nearest mm.
输出:
Your output should consist of a single line. If the gopher can escape the line should read “The gopher can escape through the hole at (x,y).” identifying the appropriate hole to the nearest mm. Otherwise the output line should read “The gopher cannot escape.” If the gopher may escape through more than one hole, choose the first one in the input. If the gopher and dog reach the hole at the same time, the gopher gets gobbled. There are not more than 1000 gopher holes and all coordinates are between -10000 and +10000.
样例输入:
1.000 1.000 2.000 2.000 1.500 1.500
样例输出:
The gopher cannot escape.
温馨提示:
Sample Input 2
2.000 2.000 1.000 1.000
1.500 1.500
2.500 2.500
2.000 2.000 1.000 1.000
1.500 1.500
2.500 2.500
Output for Sample Input 2
The gopher can escape through the hole at (2.500,2.500).
解题代码:
//* @author<EMAIL_ADDRESS>import java.util.Scanner; public class Main{ public static void main(String[] args) { Scanner in = new Scanner(System.in); double x1 = in.nextDouble(); double y1 = in.nextDouble(); double x2 = in.nextDouble(); double y2 = in.nextDouble(); //double r1 = 0; //double r2 = 0; //double d3 = 0; boolean canEscape = false; while(in.hasNext()) { String s1 = in.next(); in.skip(" "); String s2 = in.nextLine(); //in.nextLine(); double x3 = Double.parseDouble(s1); double y3 = Double.parseDouble(s2); double d1 = (x3 - x1)*(x3 - x1) + (y3 - y1)*(y3 - y1); double d2 = (x3 - x2)*(x3 - x2) + (y3 - y2)*(y3 - y2); if(d2 > 4 * d1) { canEscape = true; System.out.print("The gopher can escape through the hole at ("+s1+','+s2+")."); break; } } if(canEscape == false) System.out.println("The gopher cannot escape."); } }
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很赞的解答
第二个程序编译都通不过， int L[m+1][n+1];这样定义出错，应该换成具体的数字ac
具体实现上有所差别，但思路是一样的
写的好····· Pollard Rh
跟书上的解法不一样，不知道哪个效率要高些
这个方法是错的，不信你试试：
20 5
1 A:9
1 A:9
1 A:9
1 A:6
1 A:4
正确答案应该是19，这个答案是18
为什么暴力求解方法循环到sqrt(n) 就可以了呢
a是根先忽略掉，递归子树。剩下前缀bejkcfghid和后缀jkebfghicd，分拆的原则的是每个子树前缀和后缀的节点个数是一样的，根节点出现在前缀的第一个，后缀的最后一个。根节点b出现后缀的第四个位置，则第一部分为四个节点，前缀bejk，后缀jkeb，剩下的c出现在后缀的倒数第2个，就划分为cfghi和 fghic，第3部分就为c、c
是O(m+n)，不小心打错了
失误失误，转载错了。已经改正，抱歉。
这是往年的笔试题，非在线，主要考察基础，Java会在面试时问到
有限自动机在ACM中是必须掌握的算法，实际上在面试当中几乎不可能让你单独的去实现这个算法，如果有题目要用到有限自动机来降低时间复杂度，那么这种面试题应该属于很难的级别了。
加了星号 (*) 表示跳过此数据不读入. 因为scanf是不读取本行回车的
Thanks i enjoy your write-up about Why We Keep track of | J Squared Consulting


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Digit Reader is an app which can identify handwritten digits by using deep learning. This application is powered by a convolutional neural network which was trained on 60,000 handwritten digits images [MNIST Dataset].


---
layout: post
title: Court intrigues
date: '2013-04-07T15:09:00+02:00'
categories:
- Storyboards
- Atoleiros Battle
picture_url: https://film-Storyboards.tumblr.com/post/47393212036/queen-d-leonor-telles-with-count-andeiro
---
Queen D. Leonor Telles with Count Andeiro: animatic transition from live footage to drawings


Simple Annotated WebSocket
Simple Programmatic WebSocket
Echo sample
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The CoEpi app vision
To fully unleash the power of community epidemiology (CoEpi), we are creating an app that empowers people to leverage their personal social network for better awareness of granular infection risks.
The CoEpi app will have utmost regard for personal privacy and will ask for permission when any sharing of sensitive data is needed. It will only ask individuals to contribute their private data for the good of their community if they become unwell; even when this occurs, the system is designed to permit only the minimum necessary information to be revealed, such that the actual identity of the volunteer participants is maximally concealed.
The CoEpi mobile app will ask permission for everything it does, and will allow individuals to:
simply install the app to get started, without disclosing any personal information.
enable or disable tracking their own location history (only locally on their own device), with the ability to enable or disable such tracking on a scheduled, geofenced, or manual basis to avoid collecting any sensitive information the user would never wish to share.
enable or disable on-demand or background scanning for other compatible app users nearby using Bluetooth Low Energy (BLE), and recording (only on the local device) the history of other devices seen by the app.
enable or disable contextual hygiene reminders, for example to remind them to wash or sanitize hands when arriving at a new location like home or work, and/or after being in proximity of large numbers of people, as indicated by a significant number of other BLE devices being in range for a significant period of time.
produce a report on the device which shows a timeline of personal location history, likely as either a timeline or a map. This report should be able to be emailed or printed on user request. The use case of this information is in its application to individual health care, and in manual contact tracing activity should an individual wish to still contribute to community good after diagnosis but decline to use the networked methodology below.
be alerted when other CoEpi users come into close range for an extended period of time, and if both users consent, allow them to register a close-contact connection between the devices, allowing either user to preferentially share more information with trusted close contacts than with acquaintances.
track their own respiratory or gastrointestinal symptoms. When tracking symptoms, the app will optionally prompt the user for additional updates on symptom progression, providing the user with a comprehensive illness history if the symptoms progress to the point of requiring medical attention.
consent to share any symptoms/results with close contacts, weaker recent contacts, future contacts, and/or public health officials.
be alerted, via push notification or by periodic anonymous polling, when another CoEpi user who has shared symptoms was nearby at any point when they may have been contagious.
invite anyone to begin using the app (and optionally, register as a close contact) via text message, email, QR code, custom shareable URL, etc. Users would be encouraged to invite people in their own social network, as the app will be most useful when the people you’re most likely to be sharing germs with are using it.
Community application of this system will allow visualisation of the distribution of suspected and actual cases of all respiratory and GI illnesses being actively transmitted in the community, most notably COVID-19. In partnership with local public health officials, it could be used to inform community control actions such as travel restrictions and voluntary quarantine. Global application of this or a similar and compatible system would dramatically improve the data that is made available to decision makers.
In the future, if successful this system could be re-used for more effective control of existing transmissible infectious diseases such as influenza and measles, as well as future pandemic diseases.
Symptom reporting and sharing
Once fully integrated with secure and anonymous cloud APIs, the full power of CoEpi will come from the ability to report and share symptoms, and perform automatic real-time contact matching and tracing at scale. Here’s how it would work from a user’s perspective:
When a user enters any symptoms or abnormal results, they will be asked to report the recollected date/time of symptom onset, the current severity of symptoms, from minimal to severe, the type of symptoms, and any additional details required based on the previous answers. They’ll then be prompted to choose whether to share full or partial symptom details for the benefit of others. They can choose to share with close contacts, and/or with weaker recent contacts, future contacts, and/or public health officials.
Close contacts would be already-registered in-person contacts as described above.
Weaker contacts would be any other CoEpi users who have come into BLE range of the affected user’s app while potentially infectious, even if it didn’t result in a close contact registration.
Future contacts would be all CoEpi users who come in range of the affected user during the period when they might remain infectious.
Public health officials (and potentially epidemiological researchers) could choose to receive anonymized symptom reports from CoEpi users who optionally choose to share them. If the individual wishes to participate, the app could additionally request permission to upload approximate locations visited during each time period for public health purposes. This approximation could be done by converting the GPS coordinates to a geohash, for example the 5 character precision with a reported accuracy of +/- 2.4km.
Any reports of severe symptoms will be prompted and advised how to immediately contact local health services for advice on when/how to engage with the healthcare system. Reports of severe symptoms shared with contacts will be provided only on general terms. If a user reports having received an official diagnosis or having required hospitalization, that information will only be shared with public health officials, so they can link the patient’s CoEpi record with their official medical records as needed, and choose whether/how to perform any contact tracing or otherwise alert/contact any CoEpi users with contact matches.
Contact matching and alerting
When someone who was in proximity of a potentially infectious CoEpi user is notified of the fact, they’ll be told about the details of the symptoms reported so far, the date/time/place of their proximity with the potentially affected user, whether or not the person is a registered close contact, and they’ll be provided with guidance as to actions they should take: being on the lookout for symptoms, extra attention to hygiene, etc. For more dangerous symptoms consistent with COVID or influenza, they may also receive recommendations to begin practicing social distancing measures, working from home if possible, etc., and instructions on how to call their healthcare providers for guidance on what to do next. The user will then be able to optionally enable periodic check-ins where the app will ask the user to report any symptoms they’ve noticed, what if anything they’re doing to reduce possible re-transmission, and will prompt the user to consent to share that information with other CoEpi users and/or public health officials if appropriate.
Completely free and open source
The CoEpi app and server code will be completely open-source and free to use.
There will be a main publicly-available server run by the CoEpi community to handle new app registration, symptom and test reporting, etc. This server will endeavor not to retain any PII from individuals. Client IP addresses will not be stored in a database or recorded in log files any longer than necessary. Phone numbers and email addresses will not be linked to the user in any way. There will be no “user accounts” on the server with usernames and passwords: if someone gets a new phone, they’ll need to re-register all their close contacts the next time they come in contact.
Anyone who doesn’t trust the community-run server, or who wants to run their own for any other reason, can run their own server and invite people in their social network to (also) use that one. Anyone can read from multiple servers, and choose one or more of those servers to submit symptoms/tests to. We may choose to implement FHIR integration or similar to allow health systems to run their own server so patients can choose to report symptoms to and receive test results from the hospital’s EHR.
Additional details
Additional details on our current work-in-progress implementations, data models for future functionality, and more can be found in our GitHub repositories, including initial simplified APIs and process flow details.


Home
If you click on any of the links in the aside, you will swiftly be taken to another page of the site or two an external site or a particular resource. Try out each link and then inspect the pages or resources that you are taken to. Try to understand how they work.
Notice that the a tags are responsible for making the hyperlink. The text inside the a tag is what is displayed. The attribute href is what determines where you are sent to when you click the link. Sometimes you are sent to another page on the same website. Sometimes you are sent to other places on the web.


These are my best buds
I used to love toys. Now I live with an anxious dog (Rusty) who eats toys. He is the reason I can't have nice things. Here are the toys I used to love
Here are some of my favorite activities


Properties are settings and options for components that can be edited in the inspector.
Properties can be broadly categorized as references (links to other objects and assets) or values (numbers, checkboxes, colors, etc).
References can be assigned by dragging and dropping an object or asset of the appropriate type to the property in the inspector. For example, the Mesh Filter component needs to refer to a Mesh asset somewhere in the project. When the component is initially created, the reference is unassigned:
…but you can assign a Mesh to it by dropping a Mesh asset onto it:
You can also use the Object Picker to select an object for a reference property. If you click the small circle icon to the right of the property in the inspector, you will see a window like this:
The object picker lets you search for and select objects within the scene or project assets (the information panel at the bottom of the window can be raised and lowered as desired). Choosing an object for the reference property is simply a matter of double clicking it in the picker.
When a reference property is of a component type (such as Transform), you can drop any object onto it; Unity will locate the first component of that type on the object and assign it to the reference. If the object doesn’t have any components of the right type, the assignment will be rejected.
Most values are edited using familiar text boxes, checkboxes and menus, depending on their type (as a convenience, numeric values can also be moved up or down by dragging the mouse over the property name label). However, there are some values of more complex types that have their own specific editors. These are described below.
Color properties will open the Color Picker when clicked.
Unity uses its own color picker but on Mac OS X you can choose to use the system picker from the Preferences (menu: Unity > Preferences and then Use OS X Color Picker from the General panel).
In graphics and animation, it is often useful to be able to blend one colour gradually into another, over space or time. A gradient is a visual representation of a colour progression, which simply shows the main colours (which are called stops) and all the intermediate shades between them:
The upward-pointing arrows along the bottom of the gradient bar denote the stops. You can select a stop by clicking on it; its value will be shown in the Color box which will open the standard colour picker when clicked. A new stop can be created by clicking just underneath the gradient bar. The position of any of the stops can be changed simply by clicking and dragging and a stop can be removed with ctrl/cmd + delete.
The downward-pointing arrows above the gradient bar are also stops but they correspond to the alpha (transparency) of the gradient at that point. By default, there are two stops set to 100% alpha (ie, fully opaque) but any number of stops can be added and edited in much the same way as the colour stops.
A Curve is a line graph that shows the response (on the Y axis) to the varying value of an input (on the X axis).
Curves are used in a variety of different contexts within Unity, especially in animation, and have a number of different options and tools. These are explained on the Editing Curves page of the manual.
When a script exposes an array as a public variable, the inspector will show a value editor that lets you edit both the size of the array and the values or references within it.
When you decrease the Size property, values from the end of the array will be removed. When you increase the size, the current last value will be copied into all the new elements added. This can be useful when setting up an array whose values are mostly the same - you can set the first element and then change the size to copy it to all the other elements.
When you have two or more objects selected, the components they have in common can all be edited together (ie, the values you supply will be copied to all the selected objects).
Where property values are the same for all selected objects, the value will be shown but otherwise, it will be shown as a dash character. Only components that are common to all objects will be visible in the inspector. If any selected object has components that are not present on other objects, the inspector will show a message to say that some components are hidden. The context menu for a property (opened by right-clicking on its name label) also has options to allow you to set its value from any of the selected components.
Note that you can also edit several selected instances of a prefab at once but the usual Select, Revert and Apply buttons will not be available.


lenstronomy.Analysis package¶
Submodules¶
lenstronomy.Analysis.lens_analysis module¶
-
class
lenstronomy.Analysis.lens_analysis.
LensAnalysis
(kwargs_model)[source]¶ Bases:
object
class to compute flux ratio anomalies, inherited from standard MakeImage
-
ellipticity_lens_light
(kwargs_lens_light, center_x=0, center_y=0, model_bool_list=None, deltaPix=None, numPix=None)[source]¶ make sure that the window covers all the light, otherwise the moments may give to low answers.
Parameters: - kwargs_lens_light –
- center_x –
- center_y –
- model_bool_list –
- deltaPix –
- numPix –
Returns:
-
error_map_source
(kwargs_source, x_grid, y_grid, cov_param)[source]¶ variance of the linear source reconstruction in the source plane coordinates, computed by the diagonal elements of the covariance matrix of the source reconstruction as a sum of the errors of the basis set.
Parameters: - kwargs_source – keyword arguments of source model
- x_grid – x-axis of positions to compute error map
- y_grid – y-axis of positions to compute error map
- cov_param – covariance matrix of liner inversion parameters
Returns: diagonal covariance errors at the positions (x_grid, y_grid)
-
flux_components
(kwargs_light, n_grid=400, delta_grid=0.01, deltaPix=0.05, type='lens')[source]¶ computes the total flux in each component of the model
Parameters: - kwargs_light –
- n_grid –
- delta_grid –
Returns:
-
half_light_radius_lens
(kwargs_lens_light, center_x=0, center_y=0, model_bool_list=None, deltaPix=None, numPix=None)[source]¶ computes numerically the half-light-radius of the deflector light and the total photon flux
Parameters: kwargs_lens_light – Returns:
-
half_light_radius_source
(kwargs_source, center_x=0, center_y=0, deltaPix=None, numPix=None)[source]¶ computes numerically the half-light-radius of the deflector light and the total photon flux
Parameters: kwargs_source – Returns:
-
static
light2mass_interpol
(lens_light_model_list, kwargs_lens_light, numPix=100, deltaPix=0.05, subgrid_res=5, center_x=0, center_y=0)[source]¶ takes a lens light model and turns it numerically in a lens model (with all lensmodel quantities computed on a grid). Then provides an interpolated grid for the quantities.
Parameters: - kwargs_lens_light – lens light keyword argument list
- numPix – number of pixels per axis for the return interpolation
- deltaPix – interpolation/pixel size
- center_x – center of the grid
- center_y – center of the grid
- subgrid – subgrid for the numerical integrals
Returns:
-
light2mass_mge
(kwargs_lens_light, model_bool_list=None, elliptical=False, numPix=100, deltaPix=0.05)[source]¶
-
mass_fraction_within_radius
(kwargs_lens, center_x, center_y, theta_E, numPix=100)[source]¶ computes the mean convergence of all the different lens model components within a spherical aperture
Parameters: - kwargs_lens – lens model keyword argument list
- center_x – center of the aperture
- center_y – center of the aperture
- theta_E – radius of aperture
Returns: list of average convergences for all the model components
-
lenstronomy.Analysis.lens_properties module¶
-
class
lenstronomy.Analysis.lens_properties.
LensProp
(z_lens, z_source, kwargs_model, cosmo=None)[source]¶ Bases:
object
this class contains routines to compute time delays, magnification ratios, line of sight velocity dispersions etc for a given lens model
-
angular_distances
(sigma_v_measured, time_delay_measured, kappa_ext, sigma_v_modeled, fermat_pot)[source]¶ Parameters: - sigma_v_measured – velocity dispersion measured [km/s]
- time_delay_measured – time delay measured [d]
- kappa_ext – external convergence estimated []
- sigma_v_modeled – lens model velocity dispersion with default cosmology and without external convergence [km/s]
- fermat_pot – fermat potential of lens model, modulo MSD of kappa_ext [arcsec^2]
Returns: D_d and D_d*D_s/D_ds, units in Mpc physical
-
time_delays
(kwargs_lens, kwargs_ps, kappa_ext=0)[source]¶ predicts the time delays of the image positions
Parameters: - kwargs_lens – lens model parameters
- kwargs_ps – point source parameters
- kappa_ext – external convergence (optional)
Returns: time delays at image positions for the fixed cosmology
-
velocity_dispersion
(kwargs_lens, kwargs_lens_light, lens_light_model_bool_list=None, aniso_param=1, r_eff=None, R_slit=0.81, dR_slit=0.1, psf_fwhm=0.7, num_evaluate=1000)[source]¶ computes the LOS velocity dispersion of the lens within a slit of size R_slit x dR_slit and seeing psf_fwhm. The assumptions are a Hernquist light profile and the spherical power-law lens model at the first position.
Further information can be found in the AnalyticKinematics() class.
Parameters: - kwargs_lens – lens model parameters
- kwargs_lens_light – deflector light parameters
- aniso_param – scaled r_ani with respect to the half light radius
- r_eff – half light radius, if not provided, will be computed from the lens light model
- R_slit – width of the slit
- dR_slit – length of the slit
- psf_fwhm – full width at half maximum of the seeing condition
- num_evaluate – number of spectral rendering of the light distribution that end up on the slit
Returns: velocity dispersion in units [km/s]
-
velocity_dispersion_numerical
(kwargs_lens, kwargs_lens_light, kwargs_anisotropy, kwargs_aperture, psf_fwhm, aperture_type, anisotropy_model, r_eff=None, kwargs_numerics={}, MGE_light=False, MGE_mass=False, lens_model_kinematics_bool=None, light_model_kinematics_bool=None, Hernquist_approx=False)[source]¶ Computes the LOS velocity dispersion of the deflector galaxy with arbitrary combinations of light and mass models. For a detailed description, visit the description of the Galkin() class. Additionaly to executing the Galkin routine, it has an optional Multi-Gaussian-Expansion decomposition of lens and light models that do not have a three-dimensional distribution built in, such as Sersic profiles etc.
The center of all the lens and lens light models that are part of the kinematic estimate must be centered on the same point.
Parameters: - kwargs_lens – lens model parameters
- kwargs_lens_light – lens light parameters
- kwargs_anisotropy – anisotropy parameters (see Galkin module)
- kwargs_aperture – aperture parameters (see Galkin module)
- psf_fwhm – full width at half maximum of the seeing (Gaussian form)
- aperture_type – type of aperture (see Galkin module
- anisotropy_model – stellar anisotropy model (see Galkin module)
- r_eff – a rough estimate of the half light radius of the lens light in case of computing the MGE of the light profile
- kwargs_numerics – keyword arguments that contain numerical options (see Galkin module)
- MGE_light – bool, if true performs the MGE for the light distribution
- MGE_mass – bool, if true performs the MGE for the mass distribution
- lens_model_kinematics_bool – bool list of length of the lens model. Only takes a subset of all the models as part of the kinematics computation (can be used to ignore substructure, shear etc that do not describe the main deflector potential
- light_model_kinematics_bool – bool list of length of the light model. Only takes a subset of all the models as part of the kinematics computation (can be used to ignore light components that do not describe the main deflector
Returns: LOS velocity dispersion [km/s]
-


The ‘Scoob’ Trailer Is Here And People Are Nostalgic And Emotional
by Michelle RennexFor a lot of us, Scooby-Doo was a big part of growing up.
I mean, boasting a whole bunch of different versions and spin-offs since 1969, it’s hard to not have seen the iconic show.
Following the supernatural crime fighting adventures of Scooby, Shaggy, Fred, Daphne and Velma, Scooby-Doo was loved by adults, teens and kids alike.
And yesterday the official trailer for Scoob, the newest animation film in the Scooby-Doo franchise, was released to the delight of eager fans.
Aiming to provide the “epic origin story of the greatest team of heroes in the history of mystery”, Scoob looks like it’s sure to tug on heart strings.
Opening with how the world’s favourite best friend duo came to be, the trailer starts with young versions of Scooby and Shaggy. The video documents how the pair met, the way Scooby got his name, and in a scene that almost made me cry, the moment that Scooby got his collar.

It’s fair to say that the internet is excited for the newest Scooby-Doo adventure for the Mystery Inc gang.
Others were fixated on the fine comedy scattered throughout the two-minute long trailer, particularly the line about Ikea.
The biggest mystery to everyone, however, was the change in voices that we we’re so used to hearing.
This is because the Scooby, who was voiced by Matthew Lillard in the original series, is now played by Frank Welker, the man once responsible for the voice of Fred.
Scoob is set to release in Australian cinemas in June 2020. Until then I guess we just have to keep guessing whose plan those meddling kids foil this time.


@* * Copyright 2018 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.FrontendAppConfig @import uk.gov.hmrc.play.views.html._ @import controllers.routes._ @import models.Decision @import models.Mode @import uk.gov.hmrc.renderer.TemplateRenderer @import components._ @(appConfig: FrontendAppConfig, form: Form[_], mode: Mode, viewModel: viewmodels.BankAccountViewModel)(implicit request: Request[_], messages: Messages, templateRenderer: TemplateRenderer) @header = @{ ptaHeader( formForErrorSummary = Some(form), displayBackLink = true, mainHeadingText = messages("decision.heading", viewModel.bankName), preHeadingText = messages("decision.preHeading") ) } @main_template( title = messages("decision.title", viewModel.bankName), pageHeader = Some(header), appConfig = appConfig, bodyClasses = None ) {