Collect 10 patches and ask your parents to email the address on the back of the patch wrapper for a chance to attend the next SpaceX Rocket Launch!


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FILKOM APPS
DAFTAR HADIR CIVITAS
Dosen
Staff
Made with
in Gazebo
Dosen
Staff
Made with
in Gazebo
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Staff
Dosen
Made with
in Gazebo


Beep-Boop
Instructions:
Numbers that contain a 0 are replaced (all digits) with "Beep!"
Numbers that contain a 1 are replaced (all digits) with "Boop!"
Numbers divisible by 3 are replaced with "I'm sorry, Dave. I'm afraid I can't do that."
Input a number to hear the baby!
Submit!


{% extends "base.html" %} {% block title %}
Sorry The gist you entered could not be found on github's API. Does it work here? https://gist.github.com/{{ gist_id }} Please check that you entered it correctly. Try again


I applied one of the models in Detectron to a mini-drone video with the goal of comparing various ready-made models for the task. Notice how for this particular model there is some fun stuff going on with the shadows classified as extra objects from drone perspective.
Recommend items based on text descriptions of previous selections / Pandas, Plotly, t-SNE, word2vec, spaCy, scikit-learn
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RxPlayer documentation
Overview
You can find here various documentation pages about the RxPlayer.
If you wish to use the RxPlayer as a library, you can directly check the API Documentation.
For those wanting to contribute, we also began documenting the code architecture.
API Documentation
The general API Documentation can be found here:
For a shortcut, here are some other quick links you might want to check:
- Player options
- loadVideo options
- Player Errors
- Player Events
- Text track handling
- Import a minimal version of the Player
Tutorials
We’re under the process of writing tutorials to facilitate the usage of the RxPlayer.
Those are all linked here.
To quickly start using the player, you can directly refer to the quick start tutorial.
Architecture Documentation
An architecture documentation is in progress.
The main page can be found here:
Other pages
You can also find other pages on various subjects related to the RxPlayer here:


public class PackageInstallProcessor extends AbstractInvokeChainProcessor
| Modifier and Type | Field and Description |
|---|---|
static java.util.regex.Pattern |
DEFAULT_PACKAGE_INSTALL_SERVICE_PATTERN
The default package install service regular expression pattern.
|
static java.util.regex.Pattern |
DEFAULT_PACKAGE_UNINSTALL_SERVICE_PATTERN
The default package uninstall service regular expression pattern.
|
protected java.util.regex.Pattern |
installServicePattern
A regular expression pattern used to detect package install services.
|
static java.lang.String |
INTEGRATION_SERVER_PACKAGE_INSTALL_SERVICE
The service used to install packages, which after invocation is when any detected package install services are invoked by this processor.
|
static java.lang.String |
INTEGRATION_SERVER_PACKAGE_NAMESPACE
The service used to install packages, which after invocation is when any detected package install services are invoked by this processor.
|
static java.lang.String |
INTEGRATION_SERVER_PACKAGE_RECOVER_SERVICE
The service used to recover packages, which after invocation is when any detected package install services are invoked by this processor.
|
static java.lang.String |
INTEGRATION_SERVER_PACKAGE_UNINSTALL_SERVICE
The service used to uninstall packages, which before invocation is when any detected package uninstall services are invoked by this processor.
|
protected java.util.regex.Pattern |
uninstallServicePattern
A regular expression pattern used to detect package install services.
|
started
| Constructor and Description |
|---|
PackageInstallProcessor()
Constructs a new PackageInstallProcessor using the default package install and uninstall service patterns.
|
PackageInstallProcessor(java.util.regex.Pattern installServicePattern,
java.util.regex.Pattern uninstallServicePattern)
Constructs a new PackageInstallProcessor using the given package install service pattern.
|
PackageInstallProcessor(java.lang.String installServicePattern,
java.lang.String uninstallServicePattern)
Constructs a new PackageInstallProcessor using the given package install service pattern.
|
| Modifier and Type | Method and Description |
|---|---|
static PackageInstallProcessor |
getInstance()
Returns the singleton instance of this class.
|
void |
process(java.util.Iterator iterator,
com.wm.app.b2b.server.BaseService baseService,
com.wm.data.IData pipeline,
com.wm.app.b2b.server.invoke.ServiceStatus serviceStatus)
Process the invocation chain: if invocation is a package install, run package install services afterwards; if
the invocation is a package uninstall, run package uninstall services before.
|
void |
setInstallServicePattern(java.util.regex.Pattern installServicePattern)
Sets the pattern used to find install services in a package.
|
void |
setInstallServicePattern(java.lang.String installServicePattern)
Sets the pattern used to find install services in a package.
|
void |
setUninstallServicePattern(java.util.regex.Pattern uninstallServicePattern)
Sets the pattern used to find uninstall services in a package.
|
void |
setUninstallServicePattern(java.lang.String uninstallServicePattern)
Sets the pattern used to find uninstall services in a package.
|
isStarted, start, stop
public static final java.lang.String INTEGRATION_SERVER_PACKAGE_NAMESPACE
public static final java.lang.String INTEGRATION_SERVER_PACKAGE_INSTALL_SERVICE
public static final java.lang.String INTEGRATION_SERVER_PACKAGE_RECOVER_SERVICE
public static final java.lang.String INTEGRATION_SERVER_PACKAGE_UNINSTALL_SERVICE
public static final java.util.regex.Pattern DEFAULT_PACKAGE_INSTALL_SERVICE_PATTERN
public static final java.util.regex.Pattern DEFAULT_PACKAGE_UNINSTALL_SERVICE_PATTERN
protected volatile java.util.regex.Pattern installServicePattern
protected volatile java.util.regex.Pattern uninstallServicePattern
public PackageInstallProcessor()
public PackageInstallProcessor(java.lang.String installServicePattern, java.lang.String uninstallServicePattern)
installServicePattern
- A regular expression pattern used to find package install services.uninstallServicePattern
- A regular expression pattern used to find package uninstall services..public PackageInstallProcessor(java.util.regex.Pattern installServicePattern, java.util.regex.Pattern uninstallServicePattern)
installServicePattern
- A regular expression pattern used to find package install services.uninstallServicePattern
- A regular expression pattern used to find package uninstall services..public static PackageInstallProcessor getInstance()
public void setInstallServicePattern(java.lang.String installServicePattern)
installServicePattern
- The pattern used to find install services in a package.public void setInstallServicePattern(java.util.regex.Pattern installServicePattern)
installServicePattern
- The pattern used to find install services in a package.public void setUninstallServicePattern(java.lang.String uninstallServicePattern)
uninstallServicePattern
- The pattern used to find uninstall services in a package.public void setUninstallServicePattern(java.util.regex.Pattern uninstallServicePattern)
uninstallServicePattern
- The pattern used to find uninstall services in a package.public void process(java.util.Iterator iterator, com.wm.app.b2b.server.BaseService baseService, com.wm.data.IData pipeline, com.wm.app.b2b.server.invoke.ServiceStatus serviceStatus) throws com.wm.util.ServerException
process
in interface com.wm.app.b2b.server.invoke.InvokeChainProcessor
process
in class AbstractInvokeChainProcessor
iterator
- Invocation chain.baseService
- The invoked service.pipeline
- The input pipeline for the service.serviceStatus
- The status of the service invocation.com.wm.util.ServerException
- If the service invocation fails.


Keyboard scan matrix¶
Introduction¶
A keyboard matrix is composed of a grid of wires representing rows and columns. These rows and columns are connected to an embedded controller(KSC) which continuously scans the state of all the grid. A circuit in the grid is closed when a key is pressed, and this is eventually sensed by the FW running in the EC. Once the row and column has been determined, the EC maps the grid coordinates to a scan code which is provided to the host as a set of bytes.
Implementation¶
In this realization, state and data for a keyboard matrix are represented in the EC as an application module. This module depends on Keyboard System Controller Host to receive configuration commands and to sent keyboard events back to the host. Keyboard events are treated as if they were PS/2 keyboard events. There are a few configuration commands which are faked by kbchost since we want to let the initialization flow complete successfully. The image below illustrates components involved in the keyboard matrix configuration.
Keyboard events provide row and column data, so there isn’t any scanning performed by the keyboard matrix module since that is performed by the underlying Zephyr driver. The Zephyr driver also tracks which keys in the matrix are still pressed and which ones are being released. This data is relayed to the kscan application module in the form of callbacks. Once the callbacks are received, the application module translates grid coordinates to scan codes and queues them in the same queue used for sending PS/2 scan codes. See the image below for more details.
Note
The keyboard mapping (GTech) provided with the application cannot be connected to the modular card. The TGL board has a scan matrix ribbon connector at the bottom, but it is not mapped to the modular card which means it can’t also be used. More information will be provided later.


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inflorescence
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BOTANICAL
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Shows how to provide complex event mappings (with possibly multiple handlers for events) in a reusable way by writing event maps and hooking them up to the on keyword


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Hi, I'm Miguel, a student Engineering at the Universidad Tecnica del Norte de Guanajuato (UTNG). I am a Java developer, I also have experience in the development of Android, Web and iOS applications, Currently, I am focusing on the Programming with javascript. Apart from coding, I love going to hackathons, attending conferences and traveling. You can find me in these places, but the most reliable way to contact me is to send an email.


Let the world celebrate in your awesomeness!
Never mind, the world is not ready for this pure awesomeness.
Intel is here
Think you can make this more awesome? Click to edit!


English
Nederlands
Text as "string" attribute
Text as vicowa-string content, advantage here is that if there is no javascript support or web component support the text will still show up. Obviously any dynamic behavior like insertion of data will not work in that case.
The following is empty:
test string 1
test string 2 %1$s element
test string 2 %1$s element
test string %2$s %1$s %3$s


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--- title: Data Pre-Processing keywords: fastai sidebar: home_sidebar nb_path: "01_Data_Preprocessing.ipynb" ---
Missing and/or incorrect parameters initialized in run_importance_sampler
are reset in preprocessing_setup
.
preprocessing_setup
also preprocesses data based on the specified parameter configuration. From the parameter
settings in Table 2 of the P-CIT Toolbox Manual, we see that the predictor variable can be z-scored and outliers can
be dropped. We also can generate bootstrap data, scramble the dependent variable, scale the predictor variable between
0 and 1 (this is a mandatory step), and we can perform the analysis on one or more categories while leaving out data
from irrelevant categories. Trials where the predictor variable is set to NaN are filtered out (rows removed) for
purposes of z-scoring, dropping outliers and scaling. These filtered rows are appended to the data matrix following
those pre-processing steps. For,
Simple data analysis (includes both think/no-think and simulated data) the order of pre-processing is:
Bootstrap data analysis the order of pre-processing is:
Scramble data analysis the order of pre-processing is:
The function can be run in isolation from the toolbox pipeline very concisely. For example:
from pcitpy.run_importance_sampler import run_importance_sampler
preprocessing_setup(run_importance_sampler(run_sampler=False))
However, this isn't a typical use case for the function. The importance_sampler
module calls the function directly
at start of execution, making manual execution of preprocessing_setup
redundant.
While preprocessing_setup
is hard to demonstrate in isolation, its helper function scramble_dependent_variable
is
straightforward to illustrate:
scramble_dependent_variable([1, 0, 1, 0, 0, 0, 1], [3, 5, 3, 7, 7, 5, 8])
array([1., 1., 1., 0., 0., 1., 0.])


Class QuaternionUtils
Inheritance
System.Object
QuaternionUtils
Namespace: NomUtils.Math
Assembly: cs.temp.dll.dll
Syntax
public static class QuaternionUtils
Methods
Approximately(Quaternion, Quaternion, Single)
Returns true if the provided Quaternion is within a precision range of the original.
Declaration
public static bool Approximately(this Quaternion q1, in Quaternion q2, float precision)
Parameters
| Type | Name | Description |
|---|---|---|
| Quaternion | q1 | |
| Quaternion | q2 | |
| System.Single | precision |
Returns
| Type | Description |
|---|---|
| System.Boolean |
RotatePointAroundPivot(Vector2, Vector2, Single)
Rotates a Vector2 around a given pivot and angle euler.
Declaration
public static Vector2 RotatePointAroundPivot(in Vector2 point, in Vector2 pivot, float angle)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector2 | point | Point to rotate |
| Vector2 | pivot | Pivot to rotate around |
| System.Single | angle | Angle in degrees to rotate with |
Returns
| Type | Description |
|---|---|
| Vector2 |
RotatePointAroundPivot(Vector3, Vector3, Vector3)
Rotates a Vector3 around a given pivot and angle euler.
Declaration
public static Vector3 RotatePointAroundPivot(in Vector3 point, in Vector3 pivot, in Vector3 angles)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3 | point | Point to rotate |
| Vector3 | pivot | Pivot to rotate around |
| Vector3 | angles | Angles in degrees to rotate with |
Returns
| Type | Description |
|---|---|
| Vector3 |
SmoothDamp(Quaternion, Quaternion, ref Quaternion, Single)
Smoothly interpolates between two quaternions.
Declaration
public static Quaternion SmoothDamp(in Quaternion start, in Quaternion end, ref Quaternion derivative, float t)
Parameters
| Type | Name | Description |
|---|---|---|
| Quaternion | start | |
| Quaternion | end | |
| Quaternion | derivative | |
| System.Single | t |
Returns
| Type | Description |
|---|---|
| Quaternion |


A distributed testing framework for hardware
To set up Leviathan both hardware and software need to be configured. If you are setting up your standalone testbot, then please follow the instructions given below carefully before running tests on the Device Under Test (DUT).
Clone this repository with git clone --recursive
or
Run git clone
and then git submodule update --init --recursive
to install submodules.
Install node and npm in your system. We recommend installing LTS versions from NVM.
Download the image you want to test on your DUT from balena.io/os.
config.json
file in the workspace
directory, following instructions mentioned in the leviathan docs../leviathan/workspace
and rename it to balena.img
Run the tests by navigating to the workspace directory and running
./run-tests.sh
To push a new release to balenaCloud, run npm install
and then push to the balenaCloud application.
npm install
balena push <appname>
To monitior leviathan tests connected to its pipeline for the rigs, you would need to request Jenkins access.
Documentation for Leviathan helpers can be found on https://balena-os.github.io/leviathan
To generate the documentation, run the following command from either the root of the repository or the core
directory.
npm install
npm run docs
If the docs are generated successfully, you will be getting the success line as:
Info: Documentation generated at /path/to/documentation
Head there to find the documentation by double clicking the index.html page
If you're having any problem, please raise an issue on GitHub and the balena team will be happy to help.
The project is licensed under the Apache 2.0 license.
Generated using TypeDoc


console.log('Hello'); (1)
console.log('Bonjour'); (2)
console.log('Bye'); (3)
blue
white
red
|
Tip
|
Pro tip… |
|
Warning
|
Watch out for… |
|
Important
|
Don’t forget… |
console.log('Hello'); (1)
console.log('Bonjour'); (2)
console.log('Bye'); (3)
Display Hello
Display Bonjour
Display Bye
What’s the deal with this converter?
This is the official reveal.js converter for Asciidoctor.
Is it safe to put my credit card number into this presentation?
Probably not.
I need all the Powerpoint features
No you don’t.
| Hard drive |
Permanent storage for operating system and/or user files. |
| RAM |
Temporarily stores information the CPU uses during operation. |
Linux
macOS
Windows
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
Learn how to use images, horizontal lists and duckduckgo.com.
console.log('Hello world')
println("Hello world")
Open a terminal and type:
./forgotten-realms.sh
error: The requested operation returned error: 1954 Forbidden search for defensive operations manual absolutely fatal: operation initiation lost in the dodecahedron of doom would you like to die again? y/n
Protons
Electrons
Neutrons
Documentation is a distillation of many long, squiggly adventures.
I have a dream that my four little children will one day live in a nation where they will not be judged by the color of their skin, but by the content of their character. I have a dream today!
| Name | Category |
|---|---|
|
Firefox |
Browser |
| Artist | Track | Genre |
|---|---|---|
|
The Lumineers |
Ho Hey |
Folk Rock |
Dig
Plant
Hose
The fog comes on little cat feet.
I can’t believe it, we won!
Type the word asciidoc into the search bar.
Word with an explicit id.
Let’s put some emphasis.
Sidebars are used to visually separate auxiliary bits of content that supplement the main text.


--- title: Title keywords: fastai sidebar: home_sidebar ---
In this competition, we don't really have a validation set. The competition organizers where open about the fact that the provided data we can use to evaluate our model comes from a different distribution than the test set!
As a side note - it is really great that the competition organizers opted to take the route of transparency. Not being open about how you sample what might look like as the validation set, or going about it in a 'tricky' way, is a sure route to hurting the competition (or any ML project in general).
So we know we don't have a validation set. We understand where the train set comes from, so that will give us some idea on what we can do to improve the preformance of our model.
Still, the data provided is the closest thing we have to a validation set, at least for now. Let's put our model to the test and see what we can learn.
Also, we could be importing from our library the AudioDataset, metrics, and so forth. But we will not have access to this on Kaggle. Thus the rules of engagement will be that we need to define everything we will need in this notebook.
import pandas as pd
import torch
from fastai2.vision.all import *
import soundfile as sf
mean, std = (-6.132126808166504e-05, 0.04304003225515465)
classes = pd.read_pickle('data/classes.pkl')
get_arch = lambda: nn.Sequential(*[
Lambda(lambda x: x.unsqueeze(1)),
ConvLayer(1, 16, ks=64, stride=2, ndim=1),
ConvLayer(16, 16, ks=8, stride=8, ndim=1),
ConvLayer(16, 32, ks=32, stride=2, ndim=1),
ConvLayer(32, 32, ks=8, stride=8, ndim=1),
ConvLayer(32, 64, ks=16, stride=2, ndim=1),
ConvLayer(64, 128, ks=8, stride=2, ndim=1),
ConvLayer(128, 256, ks=4, stride=2, ndim=1),
ConvLayer(256, 256, ks=4, stride=4, ndim=1),
Flatten(),
LinBnDrop(5120, 512, p=0.25, act=nn.ReLU()),
LinBnDrop(512, 512, p=0.25, act=nn.ReLU()),
LinBnDrop(512, 256, p=0.25, act=nn.ReLU()),
LinBnDrop(256, len(classes)),
nn.Sigmoid()
])
model = get_arch()
model.load_state_dict(torch.load('data/models/first_model.pth'))
model.cuda()
model.eval();
valid_df = pd.read_csv('data/example_test_audio_summary.csv')
test_df = pd.read_csv('data/test.csv')
valid_df.head()
valid_df.filename = valid_df.filename_seconds.apply(lambda fn: '_'.join(fn.split('_')[:-1]))
valid_df.loc[valid_df.filename == 'BLKFR-10-CPL_20190611_093000'].filename = 'BLKFR-10-CPL_20190611_093000.pt540'
valid_df.loc[valid_df.filename == 'ORANGE-7-CAP_20190606_093000'].filename = 'ORANGE-7-CAP_20190606_093000.pt623'
test_df.head()
valid_df.loc[valid_df.birds.isna(), 'birds'] = ''
valid_df.shape
items = [(row.birds.split(), f'data/example_test_audio/{row.filename}.mp3', row.seconds) for idx, row in valid_df.iterrows()]
SAMPLE_RATE = 32_000
from torch.utils.data import Dataset
class AudioDataset(Dataset):
def __init__(self, items, classes, mean=None, std=None):
self.items = items
self.vocab = classes
self.do_norm = (mean and std)
self.mean = mean
self.std = std
def __getitem__(self, idx):
cls, path, offset = self.items[idx]
x, _ = sf.read(path, SAMPLE_RATE*5, start=offset)
if self.do_norm: x = self.normalize(x)
return x.astype(np.float32), self.one_hot_encode(cls)
def normalize(self, x):
return (x - self.mean) / self.std
def one_hot_encode(self, cls):
one_hot = np.zeros((len(self.vocab)))
for bird in cls:
if bird == 'unk': continue
bird_idx = self.vocab.index(bird)
one_hot[bird_idx] = 1
return one_hot
def __len__(self):
return len(self.items)
valid_ds = AudioDataset(items, classes, mean=mean, std=std)
len(valid_ds)
from torch.utils.data import DataLoader
dl = DataLoader(valid_ds, batch_size=128)
for batch in dl:
break
Oh no! Turns out soundfile
is unable to read mp3 files! Switching to librosa... But that means we need to go back and use librosa everywhere now, and also save the train files in an mp3 format. Or even better, figure out how to use the train files without concatenating them together.
import librosa
librosa.load('data/example_test_audio/BLKFR-10-CPL_20190611_093000.pt540.mp3', sr=SAMPLE_RATE)[0]
SAMPLE_RATE = 32_000
from torch.utils.data import Dataset
class AudioDataset(Dataset):
def __init__(self, items, classes, mean=None, std=None):
self.items = items
self.vocab = classes
self.do_norm = (mean and std)
self.mean = mean
self.std = std
def __getitem__(self, idx):
cls, path, offset = self.items[idx]
x, _ = librosa.load(path, sr=SAMPLE_RATE, duration=5, offset=offset)
if self.do_norm: x = self.normalize(x)
return x.astype(np.float32), self.one_hot_encode(cls)
def normalize(self, x):
return (x - self.mean) / self.std
def one_hot_encode(self, cls):
one_hot = np.zeros((len(self.vocab)))
for bird in cls:
if bird in ['unk', 'whhwoo', 'squirrel', 'mouqua', 'hawo']: continue
bird_idx = self.vocab.index(bird)
one_hot[bird_idx] = 1
return one_hot
def __len__(self):
return len(self.items)
valid_ds = AudioDataset(items, classes, mean=mean, std=std)
Unfortunately, the files in test_audio have different sampling rate than 32_000... AFAICT, neither librosa or sondfile can output mp3 files. This means every time we load each examples, librosa will perform resampling...
I guess we can live with this bit of slowness here and hope this will be much faster on kaggle where hopefully we will not need to peform resampling on the fly.
from torch.utils.data import DataLoader
import multiprocessing
import warnings
warnings.filterwarnings("ignore", category=UserWarning)
dl = DataLoader(valid_ds, batch_size=128, num_workers=multiprocessing.cpu_count() // 4)
preds = []
targs = []
for batch in dl:
with torch.no_grad():
preds.append(model(batch[0].cuda()).cpu().detach())
targs.append(batch[1])
preds = torch.cat(preds)
targs = torch.cat(targs)
def preds_to_tp_fp_fn(preds, targs):
positives = (preds >= 0.5)
true_positives = positives[targs == 1]
false_positives = positives[targs != 1]
negatives = ~positives
false_negatives = negatives[targs == 1]
return true_positives.sum(), false_positives.sum(), false_negatives.sum()
def precision(preds, targs):
tp, fp, fn = preds_to_tp_fp_fn(preds, targs)
return (tp.float() / (tp + fp)).item()
def recall(preds, targs):
tp, fp, fn = preds_to_tp_fp_fn(preds, targs)
return (tp.float() / (tp + fn)).item()
def f1(preds, targs, eps=1e-8):
prec = precision(preds, targs)
rec = recall(preds, targs)
return 2 * (prec * rec) / (prec + rec + eps)
recall(preds, targs)
precision(preds, targs)
f1(preds, targs)
preds_to_tp_fp_fn(preds, targs)
This doesn't seem like stellar performance from our model!
We could have some bug in our training code. We could have an issue in how we calculate our metrics. But my guess is that the model cheated during training through how we sampled the validation set. Maybe it was not fitting to the calls at all but was learning some other charactericts of the audio files?
Nonehteless, this is good. Making a pass through the entire pipeline all the way to predictions we have identified quite a few things that were not apparent from start and hopefully we will be able to use this information to iterate on the design.
Time to move the weights to kaggle and create the piece for outputting the predictions there.


{% include header.html %}
Due by {{ page.date | date: "%a, %d %b %Y" }}
Please e-mail your assignments to<EMAIL_ADDRESS>with "LTS2019" in the subject. The submissions will be added to the repository the day after the due date, and no late submissions will be accepted.
{{ content }}


an attribute declaration.
A document type declaration.
An XML node for document type declaration.
an element declaration
validate children and/or attributes of an element exceptions are created but not thrown.
an entity declaration
an ExternalIDs - either PublicID or SystemID
a notation declaration
a parsed entity reference
a parameter entity declaration
a parsed general entity declaration
a public identifier (see http://www.
Scanner for regexps (content models in DTD element declarations) todo: cleanup
a system identifier
an unparsed entity declaration
Parser for regexps (content models in DTD element declarations)
no summary matey


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Bibendum Fusce Vehicula Ullamcorper Fermentum
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Package management
Class ManagementControl
- java.lang.Object
-
- management.ManagementControl
-
- All Implemented Interfaces:
IManagementControl
public class ManagementControl extends java.lang.Object implements IManagementControl
This contrete class is used to control/manage the whole application- Author:
- yannick
-
-
Constructor Summary
Constructors Constructor Description ManagementControl(java.util.logging.Logger logger)
This is the constructor of the manager
-
Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description void
clearScreen()
This method can be used to clear the console screenjava.lang.Boolean
isClearConsoleActive()
This is used to check if the console is clear ablejava.lang.Boolean
isCollisonAvoidanceThreadRunnable()
This will return of the colliosnAvoidance thread should runjava.lang.Boolean
isDatabaseReaderThreadRunnable()
This will return of the databaseReader thread should runjava.lang.Boolean
isDatabaseWriterThreadRunnable()
This will return of the databaseWriter thread should runjava.lang.Boolean
isLidarDataCollectionThreadRunnable()
This will return of the lidarDataCollection thread should runjava.lang.Boolean
isManagemnetThreadRunnable()
This will return of the management thread should runvoid
makeCollisonAvoidanceThreadRunnable()
This will make the collionAvoidance thead runnablevoid
makeCollisonAvoidanceThreadUnrunnable()
This will make the collionAvoidance thread un-runnablevoid
makeDatabaseReaderThreadRunnbale()
This will make the databseReader thread runnablevoid
makeDatabaseReaderThreadUnrunnable()
This will make the databseReader thread un-runnablevoid
makeDatabaseWriterThreadRunnbale()
This will make the databaseWriter thread runnablevoid
makeDatabaseWriterThreadUnrunnable()
This will make the databaseWriter thread un-runnablevoid
makeLidarDataCollectionThreadRunnable()
This will make the lidarDataCollection thread runnablevoid
makeLidarDataCollectionThreadUnrunnable()
This will make the lidarDataCollection thread un-runnablevoid
makeManagementThreadRunnable()
This will make the management thread runnablevoid
makeManegementThreadUnrunnable()
This will make the management thread un-runnablevoid
printAll()
This will print the status of all threads to the consolevoid
readEntriesFormDatabase(IRedisDBInterface redis)
This will read the status of all threads from a redis database to gain external controlvoid
readPropertiesFile()
This is used to read form a properites filevoid
writeEntriesToDatabase(IRedisDBInterface redis)
This will write the status of all thread to a redis database
-
-
-
Method Detail
-
isClearConsoleActive
public java.lang.Boolean isClearConsoleActive()
Description copied from interface:IManagementControl
This is used to check if the console is clear able- Specified by:
isClearConsoleActive
in interfaceIManagementControl
- Returns:
-
isManagemnetThreadRunnable
public java.lang.Boolean isManagemnetThreadRunnable()
Description copied from interface:IManagementControl
This will return of the management thread should run- Specified by:
isManagemnetThreadRunnable
in interfaceIManagementControl
- Returns:
-
makeManagementThreadRunnable
public void makeManagementThreadRunnable()
Description copied from interface:IManagementControl
This will make the management thread runnable- Specified by:
makeManagementThreadRunnable
in interfaceIManagementControl
-
makeManegementThreadUnrunnable
public void makeManegementThreadUnrunnable()
Description copied from interface:IManagementControl
This will make the management thread un-runnable- Specified by:
makeManegementThreadUnrunnable
in interfaceIManagementControl
-
isCollisonAvoidanceThreadRunnable
public java.lang.Boolean isCollisonAvoidanceThreadRunnable()
Description copied from interface:IManagementControl
This will return of the colliosnAvoidance thread should run- Specified by:
isCollisonAvoidanceThreadRunnable
in interfaceIManagementControl
- Returns:
-
makeCollisonAvoidanceThreadRunnable
public void makeCollisonAvoidanceThreadRunnable()
Description copied from interface:IManagementControl
This will make the collionAvoidance thead runnable- Specified by:
makeCollisonAvoidanceThreadRunnable
in interfaceIManagementControl
-
makeCollisonAvoidanceThreadUnrunnable
public void makeCollisonAvoidanceThreadUnrunnable()
Description copied from interface:IManagementControl
This will make the collionAvoidance thread un-runnable- Specified by:
makeCollisonAvoidanceThreadUnrunnable
in interfaceIManagementControl
-
isLidarDataCollectionThreadRunnable
public java.lang.Boolean isLidarDataCollectionThreadRunnable()
Description copied from interface:IManagementControl
This will return of the lidarDataCollection thread should run- Specified by:
isLidarDataCollectionThreadRunnable
in interfaceIManagementControl
- Returns:
-
makeLidarDataCollectionThreadRunnable
public void makeLidarDataCollectionThreadRunnable()
Description copied from interface:IManagementControl
This will make the lidarDataCollection thread runnable- Specified by:
makeLidarDataCollectionThreadRunnable
in interfaceIManagementControl
-
makeLidarDataCollectionThreadUnrunnable
public void makeLidarDataCollectionThreadUnrunnable()
Description copied from interface:IManagementControl
This will make the lidarDataCollection thread un-runnable- Specified by:
makeLidarDataCollectionThreadUnrunnable
in interfaceIManagementControl
-
isDatabaseWriterThreadRunnable
public java.lang.Boolean isDatabaseWriterThreadRunnable()
Description copied from interface:IManagementControl
This will return of the databaseWriter thread should run- Specified by:
isDatabaseWriterThreadRunnable
in interfaceIManagementControl
- Returns:
-
makeDatabaseWriterThreadRunnbale
public void makeDatabaseWriterThreadRunnbale()
Description copied from interface:IManagementControl
This will make the databaseWriter thread runnable- Specified by:
makeDatabaseWriterThreadRunnbale
in interfaceIManagementControl
-
makeDatabaseWriterThreadUnrunnable
public void makeDatabaseWriterThreadUnrunnable()
Description copied from interface:IManagementControl
This will make the databaseWriter thread un-runnable- Specified by:
makeDatabaseWriterThreadUnrunnable
in interfaceIManagementControl
-
isDatabaseReaderThreadRunnable
public java.lang.Boolean isDatabaseReaderThreadRunnable()
Description copied from interface:IManagementControl
This will return of the databaseReader thread should run- Specified by:
isDatabaseReaderThreadRunnable
in interfaceIManagementControl
- Returns:
-
makeDatabaseReaderThreadRunnbale
public void makeDatabaseReaderThreadRunnbale()
Description copied from interface:IManagementControl
This will make the databseReader thread runnable- Specified by:
makeDatabaseReaderThreadRunnbale
in interfaceIManagementControl
-
makeDatabaseReaderThreadUnrunnable
public void makeDatabaseReaderThreadUnrunnable()
Description copied from interface:IManagementControl
This will make the databseReader thread un-runnable- Specified by:
makeDatabaseReaderThreadUnrunnable
in interfaceIManagementControl
-
writeEntriesToDatabase
public void writeEntriesToDatabase(IRedisDBInterface redis)
Description copied from interface:IManagementControl
This will write the status of all thread to a redis database- Specified by:
writeEntriesToDatabase
in interfaceIManagementControl
-
readEntriesFormDatabase
public void readEntriesFormDatabase(IRedisDBInterface redis)
Description copied from interface:IManagementControl
This will read the status of all threads from a redis database to gain external control- Specified by:
readEntriesFormDatabase
in interfaceIManagementControl
-
readPropertiesFile
public void readPropertiesFile()
Description copied from interface:IManagementControl
This is used to read form a properites file- Specified by:
readPropertiesFile
in interfaceIManagementControl
-
printAll
public void printAll()
Description copied from interface:IManagementControl
This will print the status of all threads to the console- Specified by:
printAll
in interfaceIManagementControl
-
clearScreen
public void clearScreen()
Description copied from interface:IManagementControl
This method can be used to clear the console screen- Specified by:
clearScreen
in interfaceIManagementControl
-
-


42.10° N, 75.93° W Beethoven St & Seminary Ave, Binghamton, NY
WHERE TO GO
There is a large, open space on the corner of Laurel and Seminary. It overlooks the upper baseball field and the pool. Take a seat on one of the benches, find a spot in the grass, or walk along the paved trails and watch the sun sink behind the hills.
THE BEST PART
This is a great park for families, students, and anyone who loves a well-loved, peaceful outdoor space. No need to drive here if you do not have a car, and there is plenty of space to settle in and enjoy the view.


Classifier built using Scipy Library of Python for Classifying Gender based on 3 data points provided as follows [ height,weight,shoe size] See Project
codeCrime File System
Developed Project Based on Vb.Net and Sql For Automating Police Station and Fetching Records using Fingerprints and also Cryptographic Message Sending between Police Stations See Project
codeWebsite for HOC Cell of Thakur College of Engineering and Technology
Website for Higher Education,Online Courses and Certifications Cell of TCET enabling Online registration of Seminars, Workshops held at TCET and Forecasting Updates on various Certifications Exams and future Career Options See Project
CERTIFICATIONS
classBlockchain Essentials
Completed Blockchain Essentials Course from IBM DeveloperWorks Mar 2018 – Present See Certificate
classCertificate of Completion: HTML Fundamentals
Completed HTML Fundamentals Course from SOLO Learn Apr 2016 – Present License 1014-516024 See Project
classCertificate of Completion: PHP Course
Completed PHP Fundamentals Course from SOLO Learn Jan 2016 – Present License 1059-516024 See Project
classCertificate of Completion: JAVA Course
Completed JAVA Fundamentals Course from SOLO Learn Jan 2016 – Present License 1068-516024 See Project
PUBLICATION
chrome_reader_modeOnline E-Portal on HOC Cell
Feb 24, 2017 publication:International Organization of Scientific Research Paper Published with ISBN number ISBN 978-0-9972393-4-8 See Published Paper


{# This template receives different input based on state of tf-setup. In addition to form values the following are available: On GET: choices: Value of SECURITY_TWO_FACTOR_ENABLED_METHODS (with possible addition of 'delete' two_factor_required: Value of SECURITY_TWO_FACTOR_REQUIRED primary_method: if a two-factor method has already been set up. On successful POST: chosen_method: which 2FA method was chosen (e.g. sms, authenticator) choices: Value of SECURITY_TWO_FACTOR_ENABLED_METHODS If chosen_method == 'authenticator': authr_qrcode: the image source for the qrcode authr_key: same key as in qrcode - for possible manual entry authr_username: same username as in qrcode authr_issuer: same issuer as in qrcode #} {% extends "security/base.html" %} {% from "security/_macros.html" import render_field_with_errors, render_field, render_field_no_label, render_field_errors %} {% block content %} {% include "security/_messages.html" %}


| 248 | (A) | witħ grete dyn dole ałł that daye for dole of hym one |
| 242 | (C) | with lowde wepynge & noyse . for sorowe of hym oone |
| 243 | (D) | wiþ lowd dremyng and dym for dole of hym alone |
| 243 | (E) | with gret lamentacyon for sorwe of þe kyng . |
| (Ex) | ---- no text ---- | |
| 248 | (L) | with loude dyn & dit for doil of hym one |
| 240 | (P) | with loude dole and with dynn ałł of hym one |
| 240 | (U) | witħ loude lowe dremyng / for drede of him one |
| (V) | ---- no text ---- |


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--- date: 2010-01-31T00:00:00Z published: true title: Automating a portion of the Rails 3 upgrade process url: /2010/01/31/automating-a-portion-of-the-rails-3-upgrade-process/ ---
I’ve been playing with upgrading some apps to Rails 3 (some open-source, some not), and I’ve sort of gotten some of the process down to a science. So what does a developer do when something is down to a process? Automate!
I’ve created a (pretty hacky) gem named rails-upgrade (installable by a simple
gem install rails-upgrade
) to automate some of the more annoying parts of the upgrade from Rails 2.x to Rails 3. So far, it has three parts…
Cool.


X-Plane plug-in setup
When you have the X-Plane plug-in installed according to the X-Plane plug-in
installation, a short setup is needed in order to use it - keys or joystick buttons have to be bound
in order to start, stop, recenter and pause the
tracking. Please note, that the setup of tracking parameters (sensitivities, ...) is done in the Linuxtrack GUI,
X-Plane plug-in doesn't have any means to do that.
Joystick buttons setup
Joystick buttons are setup using Buttons : Adv pane in the Joystick & Equipment window, that is accessible
through Settings / Joystick, Keys & Equipment menu in X-Plane.
Press the desired joystick button, then check the check-box next to the tabs (under the mouse pointer
in the screen-shot above) and the following dialog will appear.
Click the combo-box in the upper part of the dialog and select X System folder.
Select the linuxtrack line in the lower part of the dialog.
Finally select the desired command (ltr_run, ltr_pause or ltr_recenter).
Repeat those steps for other actions (pause, recenter) and when done, just close the window and now you
can control Linuxtrack using the new bindings.
Keyboard bindings setup
Keyboard bindings are setup pretty much in the same way - use Keys pane in the Joystick & Equipment window,
that is accessible through Settings / Joystick, Keys & Equipment menu.
When you create or select a key binding, use the check-box next to the tabs and continue the same way as for
joystick bindings.
You can also press Add new key assignment button in the middle bottom, that will create new key position -
just press the newly created button and then press the key (or key combination) that you want to assign to it.
Then again select the desired binding.
Pilot View interface
Recent Linuxtrack versions contain an interface, allowing Linuxtrack's X-Plane plug-in to communicate with
Pilot View plug-in(version 1.7+) by Sandy Barbour.
To enable the Pilot View interface, open the Pilot View.ini file (located in Resources/plugins/Pilot View
directory in your X-Plane installation folder) in your favorite text editor and add a
line "EnableExternalData = 1" to the CONFIG section, so the result reads like this:
[CONFIG]
EnableExternalData = 1
AutoStart = 0
EngineVibration = 1
...
When done, start X-Plane and start the tracking - now, when Pilot View plug-in is enabled, the tracking should work.
Troubleshooting Pilot View interface
Should you encounter any problems, please follow these steps:
- Verify you have Pilot View 1.7 or higher (see the title-bar of its Config window)
- If you installed Pilot View plug-in before May 19th 2013, please download the fresh version and try
again (don't forget the required modification of the .ini file!)
- If you didn't reinstall the Linuxtrack plug-in, please do so in the ltr_gui (Misc. pane)
- In the plug-ins menu inside X-Plane, open Linuxtrack and Linuxtrack Setup menu -
there should be a line saying: "Pilot View plug-in found, channeling head-tracking data through it!".
Should you encounter any problems getting this to work, please contact me first, so I can determine where
the problem is.


Who knows their local MP?
Hard to contact local representative
Meyasubako
Collecting people's voices to the Tokugawa shogun - Edo era
Messages receieved by your local member
Empowering Australians by providing a public forum for their voice


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Akang Wang
Operations Research, Mathematical Optimization
Hello, I’m Akang Wang. I’m working as a Research Scientist at Shenzhen Research Institute of Big Data (SRIBD). I received my PhD degree in the area of Process Systems Engineering at Carnegie Mellon University in 2020, and my PhD thesis was focused on supply chain modeling and algorithms as well as global optimization. My current research interests include integer programming, global optimization, learning to optimize and grid optimization. I have publications in Journal of Global Optimization, EJOR, ICLR, etc. I won the first place in the primal track of NeurIPS 2021 ML2CO competition and the second place in the 2022 RAS Problem Solving Competition of INFORMS. Besides, I served as reviewers for journals such as IPCO, EJOR, Networks.
In this website, you can know more about my research and experience. Also, please feel free to check out my Google Scholar and LinkedIn.
news
| Jan 20, 2023 | Our paper “A GNN-Guided Predict-and-Search Framework for Mixed-Integer Linear Programming” is accepted by ICLR 2023 Annual Conference. |
| Jan 9, 2023 | Our paper “Estimating the marginal cost to deliver to individual customers” is now accepted by Optimization and Engineering. |
| Oct 16, 2022 | We gave an invited talk entitled “A Hierarchical Decomposition Approach for Railway Disruption Recovery” (link, slides) in INFORMS 2022 Annual Meeting, and our work won the 2nd place in the 2022 RAS Problem Solving Competition. |
| Oct 16, 2022 | We gave a talk entitled “Workload Balancing in Periodic Distribution Scheduling and Routing Optimization” in INFORMS 2022 Annual Meeting. |
| Sep 14, 2022 | I gave an invited talk entitled “An Introduction to Development and Applications of Mixed-Integer Linear Programming Solvers” at weekly seminar series organized by Prof. Chensong Zhang. |
| Apr 19, 2022 | Happy to be invited as a judge for “Election of Top OR Companies in China”. Look forward to it! |
| Dec 9, 2021 | I gave an invited talk entitled “Efficient Primal Heuristics for Mixed-Integer Linear Programs” (video, slides, arXiv) at NeurIPS 2021 Annual Conference. |
| Nov 22, 2021 | We won the 1st place in the primal task of Machine Learning for Combinatorial Optimization NeurIPS 2021 competition. |
| Oct 26, 2021 | We gave a talk entitled “Periodic Vehicle Routing With Trips Spanning Multiple Days” in INFORMS 2021 Annual Meeting. |
| Oct 19, 2021 | I’m very happy to be recognized as “Overseas High-Caliber Personnel (Level C)” by Human Resrouces and Social Security Administration of Shenzhen Municipality, Guangdong Province, China. |
| Aug 14, 2021 | Our paper “Robust vehicle routing under uncertainty via branch-price-and-cut” is now accepted by Optimization and Engineering. |
| Jun 7, 2021 | I join Shenzhen Research Institute of Big Data as a Research Scientist. |
| May 26, 2021 | I gave an invited talk entitled “Mathematical Optimization and Its Application to Ride-Sharing” at DiDi Tech Show 2021. |
| Apr 26, 2021 | Our paper “Mixed-integer linear optimization for full truckload pickup and delivery” is now accepted by Optimization Letters. |
| Mar 23, 2021 | We submitted our paper “Robust Vehicle Routing under Uncertainty via Branch-Price-and-Cut”. |
| Jan 6, 2021 | Our paper “On tackling reverse convex constraints for non-overlapping of unequal circles” is accepted by Journal of Global Optimization. |
| Nov 12, 2020 | We presented our work “A Branch-price-and-cut Approach for Robust Vehicle Routing” in INFORMS 2020 Annual Meeting. |
| Aug 18, 2020 | We submitted our paper “Mixed-integer Linear Optimization for Full Truckload Pickup and Delivery”. |
| Aug 11, 2020 | I join DiDi as an Algorithm Engineer. |
| Jul 3, 2020 | Our work “Vehicle Routing with Endogenous Learning: Application to Offshore Plug and Abandonment Campaign Planning” is now accepted by European Journal of Operational Research. |
| May 28, 2020 | We published our work “A Branch-Price-and-Cut Algorithm for Robust Vehicle Routing under Demand Uncertainty” in Proceedings of the TSL Second Triennial Conference. |
| Apr 22, 2020 | I defended my thesis with the title “Optimization Algorithms for Vehicle Routing and Packing Problems”. |
| Feb 14, 2020 | Our abstract entitled “A Branch-Price-and-Cut Algorithm for Robust Vehicle Routing under Demand Uncertainty” has been accepted by the 2020 TSL conference. |
| Jan 16, 2020 | We submitted our paper “Estimation of Marginal Cost to Serve Individual Customers”. |
| Dec 17, 2019 | We submitted our paper “On Tackling Reverse Convex Constraints for Non-overlapping of Circles”. |
| Dec 6, 2019 | We submitted our paper “Vehicle Routing with Endogenous Learning: Application to Offshore Plug and Abandonment Campaign Planning”. |
| Oct 22, 2019 | I presented my recent work “Estimation of Marginal Cost to Serve Individual Customers” in INFORMS 2019 Annual Meeting. |
| Oct 22, 2019 | I presented a talk “A Branch-and-Cut Algorithm for Continuous-time Inventory Routing” in INFORMS 2019 Annual Meeting. |
| Oct 1, 2019 | I will chair a Transportation Procurement & Vehicle Routing session in INFORMS 2019 Annual Meeting and look forward to these interesting talks. |
| Sep 19, 2019 | As one of the James C. Meade Graduate Fellowship recipients, I had a great opportunity to meet James C. Meade and his family today. James is such a decent, sharp, knowledgable, and interesting person. He shared his thoughts about engineering, education, future generations, etc. A really respectful man! |
| Jun 8, 2019 | During the past two weeks, I visited Air Liquide Delaware Research and Technology Center and had the great opportunity to discuss with and learn from research peers and industry practitioners. |
| May 21, 2019 | I am glad to join the YinzOR 2019 Organizing Committee. Our goal is to provide an opportunity for students who are interested in Operations Research, Management Science, Industrial Engineering, and other related fields to interact with each other. |
| Nov 30, 2018 | I gave a PSE seminar talk with the title of “An Introduction to Intersection Cuts and Their Applications” at Carnegie Mellon University. |
| Nov 7, 2018 | I presented a talk “A Customized Branch-and-Bound Approach for Circle Packing” in INFORMS 2018 Annual Meeting. |
| Sep 18, 2018 | I will chair a Global Optimization session in INFORMS 2018 Annual Meeting and look forward to these interesting talks. |
| Sep 18, 2018 | Our paper “A Scenario Decomposition Algorithm for Strategic Time Window Assignment Vehicle Routing Problems” is available online now. |
| Jun 8, 2018 | We submitted our paper “A Scenario Decomposition Algorithm for Strategic Time Window Assignment Vehicle Routing Problems”. |
| Apr 5, 2018 | I have been selected to receive the H. William and Ruth Hamilton Prengle Graduate Fellowship in Chemical Engineering for 2017-2018. |
| Mar 14, 2018 | Our paper “A customized branch-and-bound approach for irregular shape nesting” is available online now. |
| Nov 27, 2017 | I held my Thesis Proposal presentation with the title “Optimization Algorithms for Vehicle Routing under Uncertainty and Irregular Shape Nesting”. |
| Oct 25, 2017 | I gave a presentation on the application of Global Optimization in Shape Nesting Problems in INFORMS 2017 Annual Meeting. My presentation title was “Irregular Shape Nesting via Branch-and-Bound Using Custom Relaxations”. |
| Oct 24, 2017 | I gave a presentation with the title of “A Branch-Price-and-Cut Approach for Robust Vehicle Routing” in INFORMS 2017 Annual Meeting. |
| Dec 21, 2016 | I have been selected to receive the James C. Meade Graduate Fellowship in Chemical Engineering for 2016-2017. |


nips nips2012 nips2012-16 nips2012-16-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Ozlem Aslan, Dale Schuurmans, Yao-liang Yu
Abstract: Despite the variety of robust regression methods that have been developed, current regression formulations are either NP-hard, or allow unbounded response to even a single leverage point. We present a general formulation for robust regression—Variational M-estimation—that uniﬁes a number of robust regression methods while allowing a tractable approximation strategy. We develop an estimator that requires only polynomial-time, while achieving certain robustness and consistency guarantees. An experimental evaluation demonstrates the effectiveness of the new estimation approach compared to standard methods. 1
[1] T. Bernholt. Robust estimators are hard to compute. Technical Report 52/2005, SFB475, U. Dortmund, 2005.
[2] R. Nunkesser and O. Morell. An evolutionary algorithm for robust regression. Computational Statistics and Data Analysis, 54:3242–3248, 2010.
[3] R. Maronna, R. Martin, and V. Yohai. Robust Statistics: Theory and Methods. Wiley, 2006.
[4] P. Huber and E. Ronchetti. Robust Statistics. Wiley, 2nd edition, 2009.
[5] A. Christmann and I. Steinwart. Consistency and robustness of kernel-based regression in convex risk minimization. Bernoulli, 13(3):799–819, 2007.
[6] A. Christmann, A. Van Messem, and I. Steinwart. On consistency and robustness properties of support vector machines for heavy-tailed distributions. Statistics and Its Interface, 2:311–327, 2009.
[7] P. Rousseeuw and A. Leroy. Robust Regression and Outlier Detection. Wiley, 1987.
[8] M. Black and A. Rangarajan. On the uniﬁcation of line processes, outlier rejection, and robust statistics with applications in early vision. International Journal of Computer Vision, 19(1): 57–91, 1996.
[9] Y. Yu, M. Yang, L. Xu, M. White, and D. Schuurmans. Relaxed clipping: A global training method for robust regression and classiﬁcation. In Advances in Neural Information Processings Systems (NIPS), 2010.
[10] A. Bental, L. El Ghaoui, and A. Nemirovski. Robust Optimization. Princeton Series in Applied Mathematics. Princeton University Press, October 2009.
[11] H. Xu, C. Caramanis, and S. Mannor. Robust regression and Lasso. In Advances in Neural Information Processing Systems (NIPS), volume 21, pages 1801–1808, 2008.
[12] H. Xu, C. Caramanis, and S. Mannor. Robustness and regularization of support vector machines. Journal of Machine Learning Research, 10:1485–1510, 2009.
[13] S. Mukherjee, P. Niyogi, T. Poggio, and R. Rifkin. Learning theory: Stability is sufﬁcient for generalization and necessary and sufﬁcient for consistency of empirical risk minimization. Advances in Computational Mathematics, 25(1-3):161–193, 2006.
[14] G. Kimeldorf and G. Wahba. A correspondence between Bayesian estimation on stochastic processes and smoothing by splines. Annals of Mathematical Statistics, 41(2):495–502, 1970.
[15] I. Steinwart and A. Christmann. Support Vector Machines. Springer, 2008.
[16] Aad W. van der Vaart and Jon A. Wellner. Weak Convergence and Empirical Processes. Springer, 1996.
[17] F. Hampel, E. Ronchetti, P. Rousseeuw, and W. Stahel. Robust Statistics: The Approach Based on Inﬂuence Functions. Wiley, 1986.
[18] D. Donoho and P. Huber. The notion of breakdown point. In A Festschrift for Erich L. Lehmann, pages 157–184. Wadsworth, 1983.
[19] P. Davies and U. Gather. The breakdown point—examples and counterexamples. REVSTAT Statistical Journal, 5(1):1–17, 2007.
[20] Y. Nesterov and A. Nemiroviskii. Interior-point Polynomial Methods in Convex Programming. SIAM, 1994.
[21] Y. Nesterov. Introductory Lectures on Convex Optimization: A Basic Course. Kluwer, 2003.
[22] S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge U. Press, 2004.
[23] J. Peng and Y. Wei. Approximating k-means-type clustering via semideﬁnite programming. SIAM Journal on Optimization, 18(1):186–205, 2007.
[24] P. Rousseeuw and K. Van Driessen. Computing LTS regression for large data sets. Data Mining and Knowledge Discovery, 12(1):29–45, 2006.
[25] R. Horn and C. Johnson. Matrix Analysis. Cambridge, 1985. 9


This is the interface file for the circular buffer. More...
#include <stdint.h>
#include <stddef.h>
Go to the source code of this file.
Data Structures | |
| struct | sCircBuf_t |
| Circular buffer structure used by the application program. More... | |
Enumerations | |
| enum | eStatus_t { E_NOERR = 0, E_BUF_FULL, E_BUF_EMPTY, E_BUF_OVERWRITE } |
| status enum More... | |
Functions | |
| eStatus_t | BufInit (sCircBuf_t *const me) |
| Function for initalizing circular buffer. More... | |
| eStatus_t | BufEnqueue (sCircBuf_t *const me, int8_t data) |
| Function to enqueue data into circular buffer. More... | |
| eStatus_t | BufDequeue (sCircBuf_t *const me, int8_t *data) |
| Function to dequeue data from circular buffer. More... | |
| eStatus_t | BufPeek (sCircBuf_t *const me, int8_t *data) |
| Function which is used to peek into the circular buffer. More... | |
Variables | |
| const size_t | BUF_SIZE = 16 |
This is the interface file for the circular buffer.
| enum eStatus_t |
| eStatus_t BufDequeue | ( | sCircBuf_t *const | me, |
| int8_t * | pData | ||
| ) |
Function to dequeue data from circular buffer.
| me | pointer to sCircBuf_t struct to from data is being dequeued |
| pData | data pointer to be filled by dequeue function |
If buffer is empty update status
dequeue data and set to zero
wrap buffer head
| eStatus_t BufEnqueue | ( | sCircBuf_t *const | me, |
| int8_t | data | ||
| ) |
Function to enqueue data into circular buffer.
| me | pointer to sCircBuf_t struct to which data is being enqueued |
| data | data being enqueued |
If buffer is full indicate in status
wrap buffer tail
enqueue data and update number of elements
set ceiling on number of elements
| eStatus_t BufInit | ( | sCircBuf_t *const | me | ) |
Function for initalizing circular buffer.
| me | pointer to sCircBuf_t struct , to be initalized |
set buffer value to 0
| eStatus_t BufPeek | ( | sCircBuf_t *const | me, |
| int8_t * | data | ||
| ) |
Function which is used to peek into the circular buffer.
| me | pointer to sCircBuf_t struct to from data is being peeked |
| data | pointer to be filled by peek function |


NdArray
N dimensional array package for numeric computing in swift.
The package is inspired by NumPy, the well known python package for numerical computations. This swift package is certainly far away from the maturity of NumPy but implements some key features to enable fast and simple handling of multidimensional data.
Table of Contents
(generated with DocToc)
- Installation
- Multiple Views on Underlying Data
- Sliced and Strided Access
- Element Manipulation
- Reshaping
- Linear Algebra Operations for
Double
andFloat
NdArrays. - Pretty Printing
- Type Concept
- Numerical Backend
- Not Implemented
- Out of Scope
Installation
Swift Package Manager
dependencies: [
.package(url: "https://github.com/dastrobu/NdArrays.git", from: "0.1.2"),
],
Multiple Views on Underlying Data
Two arrays can easily point to the same data and data can be modified through both views. This is significantly different from the Swift internal array object, which has copy on wrie semantics, meaning you cannot pass around pointers to the same data. Whereas this behaviour is very nice for small amounts of data, since it reduces side effects. For numerical computation with huge arrays, it is preferable to let the programmer manage copies. The behaviour of the NdArray is very similar to NumPy’s ndarray object. Here is an example:
let a = NdArray<Double>([9, 9, 0, 9])
let b = NdArray(a)
a[[2]] = 9.0
print(b) // [9.0, 9.0, 9.0, 9.0]
print(a.ownsData) // true
print(b.ownsData) // false
Sliced and Strided Access
Like NumPy’s ndarray, slices and strides can be created.
let a = NdArray<Double>.range(to: 10)
let b = NdArray(a[..., 2])
print(a) // [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]
print(b) // [0.0, 2.0, 4.0, 6.0, 8.0]
print(b.strides) // [2]
b[...].set(0)
print(a) // [0.0, 1.0, 0.0, 3.0, 0.0, 5.0, 0.0, 7.0, 0.0, 9.0]
print(b) // [0.0, 0.0, 0.0, 0.0, 0.0]
This creates an array first, then a strided view on the data, making it easy to set every second element to 0.
 Passing indices individually cannot be implemented, since Swift does not support varargs on subscript.
let a = NdArray<Double>.range(to: 12).reshaped([2, 2, 3])
a[[0, 1, 2]]
a[0, 1, 2] // does not work with Swift
For efficient iteration of all indices consider using e.g. apply
, map
or
reduce
.
let a = NdArray<Double>.ones(4).reshaped([2, 2])
let b = a.map {
$0 * 2
} // map to new array
print(b)
// [[2.0, 2.0],
// [2.0, 2.0]]
a.apply {
$0 * 3
} // in place
print(a)
// [[3.0, 3.0],
// [3.0, 3.0]]
print(a.reduce(0) {
$0 + $1
}) // 12.0
Scaling every second element in a matrix by its row index could be done in the following way
let a = NdArray<Double>.ones([4, 3])
for i in 0..<a.shape[0] {
a[i][..., 2].apply {
$0 * Double(i)
}
}
print(a)
// [[0.0, 1.0, 0.0],
// [1.0, 1.0, 1.0],
// [2.0, 1.0, 2.0],
// [3.0, 1.0, 3.0]]
Alternatively one can use classical loops and convert each row to a vector for efficient element indexing
let a = NdArray<Double>.ones([4, 3])
for i in 0..<a.shape[0] {
let ai = Vector(a[i])
for j in stride(from: 0, to: a.shape[1], by: 2){
ai[j] *= Double(i)
}
}
print(a)
// [[0.0, 1.0, 0.0],
// [1.0, 1.0, 1.0],
// [2.0, 1.0, 2.0],
// [3.0, 1.0, 3.0]]
Reshaping
Like in NumPy, and array can be reshaped to any compatible shape without modifying the data. That means the shape and strides are recomputed to re-interpret the data.
let a = ndarray<double>.range(to: 12)
print(a.reshaped([2, 6]))
// [[ 0.0, 1.0, 2.0, 3.0, 4.0, 5.0],
// [ 6.0, 7.0, 8.0, 9.0, 10.0, 11.0]]
print(a.reshaped([2, 6], order: .f))
// [[ 0.0, 2.0, 4.0, 6.0, 8.0, 10.0],
// [ 1.0, 3.0, 5.0, 7.0, 9.0, 11.0]]
print(a.reshaped([3, 4]))
// [[ 0.0, 1.0, 2.0, 3.0],
// [ 4.0, 5.0, 6.0, 7.0],
// [ 8.0, 9.0, 10.0, 11.0]]
print(a.reshaped([4, 3]))
// [[ 0.0, 1.0, 2.0],
// [ 3.0, 4.0, 5.0],
// [ 6.0, 7.0, 8.0],
// [ 9.0, 10.0, 11.0]]
print(a.reshaped([2, 2, 3]))
// [[[ 0.0, 1.0, 2.0],
// [ 3.0, 4.0, 5.0]],
//
// [[ 6.0, 7.0, 8.0],
// [ 9.0, 10.0, 11.0]]]
A copy will only be made if required to create an array with the specified order.
Linear Algebra Operations for Double
and Float
NdArrays.
Linear algebra support is currently very basic.
Matrix Vector Multiplication
let A = Matrix<Double>.ones([2, 2])
let x = Vector<Double>.ones(2)
print(A * x) // [2.0, 2.0]
Matrix Matrix Multiplication
let A = Matrix<Double>.ones([2, 2])
let x = Matrix<Double>.ones([2, 2])
print(A * x)
// [[2.0, 2.0],
// [2.0, 2.0]]
Matrix Inversion
let A = Matrix<Double>(NdArray.range(to: 4).reshaped([2, 2]))
print(try A.inverted())
// [[-1.5, 0.5],
// [ 1.0, 0.0]]
Solve Linear System of Equations
with single right hand side
let A = Matrix<Double>(NdArray.range(to: 4).reshaped([2, 2]))
let x = Vector<Double>.ones(2)
print(try A.solve(x)) // [-1.0, 1.0]
with multiple right hand sides
let A = Matrix<Double>(NdArray.range(to: 4).reshaped([2, 2]))
let x = Matrix<Double>.ones([2, 2])
print(try A.solve(x))
// [[-1.0, -1.0],
// [ 1.0, 1.0]]
Pretty Printing
Multi dimensional arrays can be printed in a human friendly way.
print(NdArray<Double>.ones([2, 3, 4]))
// [[[1.0, 1.0, 1.0, 1.0],
// [1.0, 1.0, 1.0, 1.0],
// [1.0, 1.0, 1.0, 1.0]],
//
// [[1.0, 1.0, 1.0, 1.0],
// [1.0, 1.0, 1.0, 1.0],
// [1.0, 1.0, 1.0, 1.0]]]
print("this is a 2d array in one line \(NdArray<Double>.zeros([2, 2]), style: .singleLine)")
// this is a 2d array in one line [[0.0, 0.0], [0.0, 0.0]]
print("this is a 2d array in multi line format line \n\(NdArray<Double>.zeros([2, 2]), style: .multiLine)")
// this is a 2d array in multi line format line
// [[0.0, 0.0],
// [0.0, 0.0]]
Type Concept
The idea is to have basic NdArray
type, which keeps a
pointer to data and stores shape and stride information.
Since there can be multiple NdArray
objects referring
to the same data, ownership is tracked explicitly.
If an array owns its data is stored in the ownsData
flag (similar to NumPy’s
ndarray)
When creating a new array from an existing one, no copy is made unless necessary. Here are a few
examples
let A = NdArray<Double>.ones(5)
var B = NdArray(A) // no copy
B = NdArray(copy: A) // copy explicitly required
B = NdArray(A[..., 2]) // no copy, but B will not be contiguous
B = NdArray(A[..., 2], order: .C) // copy, because otherwise new array will not have C ordering
When using slices on an NdArray
it returns a NdArraySlice
object. This slice object is similar to
an array but
keeps track how deeply its sliced.
let A = NdArray<Double>.ones([2, 2, 2])
var B = A[...] // NdArraySlice withe sliced = 1, i.e. one dimension has been sliced
B = A[...][..., 2] // NdArraySlice withe sliced = 2, i.e. one dimension has been sliced
B = A[...][..., 2][..<1] // NdArraySlice withe sliced = 3, i.e. one dimension has been sliced
B = A[...][..., 2][..<1][...] // Assertion failed: Cannot slice array with ndim 3 more than 3 times.
So it is recommended to convert to an NdArray
after
slicing before continuing to work with the data.
let A = NdArray<Double>.ones([2, 2, 2])
var B = NdArray(A[...]) // B has shape [2, 2, 2]
B = NdArray(A[...][..., 2]) // B has shape [2, 1, 2]
B = NdArray(A[...][..., 2][..<1]) // B has shape [2, 1, 1]
When using slices to assign data, no type conversion is required.
let A = NdArray<Double>.ones([2, 2])
let B = NdArray<Double>.zeros(2)
A[...][0] = B[...]
print(A)
// [[0.0, 1.0],
// [0.0, 1.0]]
Subtypes
To be able to define operators for matrix vector multiplication and matrix matrix multiplication, sub
types like
Matrix
and Vector
are defined. Since no data is copied when
creating a matrix or vector from an array, they
can be converted anytime, thereby making sure the shapes match requirements of the sub type.
let a = NdArray<Double>.ones([2, 2])
let b = NdArray<Double>.zeros(2)
let A = Matrix<Double>(a) // matrix from array without copy
let x = Vector<Double>(b) // vector from array without copy
let Ax = A * x; // matrix vector multiplication is defined
let _ = Vector<Double>(a) // Assertion failed: Cannot create vector with shape [2, 2]. Vector must have one dimension.
Furthermore algorithms specific for subtypes like a matrix will be defined as method on the subtype,
e.g. sovle
let A = Matrix<Double>(NdArray.range(to: 4).reshaped([2, 2]))
let x = Vector<Double>.ones(2)
print(try A.solve(x)) // [-1.0, 1.0]
Numerical Backend
Numerical operations are performed using BLAS, see also BLAS cheat sheet for an overview and LAPACK. The functions of these libraries are provided by the Accelerate Framework and are available on most Apple platforms.
Not Implemented
Some features are not implemented yet, but are planned for the near future.
- Trigonometric functions
- Elementwise multiplication of Double and Float arrays. Planned as
multiply(elementwiseBy, divide(elementwiseBy)
employingvDSP_vmulD
Note that this can be done with help ofmap
currently.
Out of Scope
Some features would be nice to have at some time but currently out of scope.
- Complex numbers (currently support for complex numbers is not planned)


Free Code Camp
Survey for
Code Editor
usage amoung campers
Name
Email
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Select Your Favorite Code Editor
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Notepad ++
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Other
Specify if Other
Main features you like about your code editor
Extensions and Plugins
Keyboard shortcuts
Lightweight
Intelligent Code Completion
Your Platforms
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Submit Survey


Academics
Departments
The Institute has following Departments
- Computer Science
- Electronics Engineering
- Electrical Engineering
- Basic Sciences
- Humanities
- Under Grad Programmes
B.Tech programme is being offered in the following disciplines.
- Computer Science and Engineering
- Information Technology
- Electronics and Communication
- Electrical and Electronics Engineering
- Masters Programmes
The Institute offers M.Tech. programmes in
- Computer Science and Engineering.
- Communication Engineering and Signal Processing
- Doctoral Programme
Doctoral programme is offered in following Disciplines
- Computer Science
- Electronics Engineering
- Tele Communication Engineering
- Electrical Engineering
- Mechanical Engineering
- Physics
- Chemistry
- Mathematics
- Business Management


Back
Programming Principles I
Week 12: Basics of file handling: open, close, read, write in C.
Content overview
Pointers
Declaration, initialization
Address of and indirection operators
Call by value vs call by reference
Constant Pointers


{% load static %}
Toggle navigation
Draw Together!
Logout
Dashboard
New Game
Global Stream
Friend Stream
Practice
{{ win }}
All Wins!
{{ total }}
All Records!
{{ loss }}
All Failures!
{{ rate|floatformat:"0" }}%
The Ratio!
{% load static %}


Japan needs extra budget of over ¥10 tril: LDP No. 2 Nikai
TOKYO — Japan needs to compile an extraordinary budget of more than 10 trillion yen ($92 billion) for the current fiscal year through March in a bid to bolster the economy, a senior ruling party official said Tuesday.
"As uncertainty grows about the economic outlook, (the government) cannot remove the public's anxiety without a large-scale supplementary budget," Toshihiro Nikai, secretary general of the Liberal Democratic Party, said at a news conference.
Nikai, who holds the LDP's No. 2 post after Prime Minister Shinzo Abe, stressed the importance of strengthening infrastructure-building and reconstruction efforts in the face of increasingly severe natural disasters hitting the country.
The government is crafting the budget for fiscal 2019 to assist reconstruction in areas devastated by a series of disasters this year, including deadly Typhoon Hagibis in October.
Finance Minister Taro Aso, however, questioned Nikai's suggestion, telling reporters that Nikai needs to show fiscal sources for the proposed budget.
"Where would 10 trillion yen come from?" asked Aso, who doubles as deputy prime minister.
The scale of Nikai's plan compares with the country's 101.46 trillion yen initial budget for fiscal 2019.
Earlier this month, Abe instructed ministers to draw up an economic package to cushion downside risks at home and abroad, including the impact of the Oct 1 increase in the consumption tax to 10 percent from 8 percent.
The government plans to earmark necessary funds for the package in the fiscal 2019 supplementary budget and in the fiscal 2020 budget.
© KYODO


Meet my grandparents
Norma Knutson
Don Knutson
Meadville, Pennsylvania
Hometown of Grandma Knutson
Germany
Home Country of Grandma Knutson's Family
Familyhome
Grandma Knutson'sfamily home in Meadville
Grandma Knutson's Native Country Languages
English& German
They speak English and German.
GreatGrandpaArden
Grandma Knutson'smost important family member
Grandma Knutson's Native Country Food
Sauerkraut
Made out of cabbage and love.
Grandma Knutson Traditions
Church
This is the interior ofPulaski Heights Methodist Church.
The familypassport
Allowed Grandma Knutson'sfamily to come to America
Grandma Knutson Family Fun
Water
Go to the lake whenever possible
A musicalfamily
Grandma Knutson'sfavorite family fact
Austin, Minnesota
Hometown of Grandpa Knutson
Norway
Home Country of Grandpa Knutson's Family
The Cabin
Grandpa Knutson'sfamily retreat in Minnesota
Grandpa Knutson's Native Country Languages
Norwegian& English
They speak Norwegian and English.
LynnGrandpaGrandmaGregg &My Mommy
Grandpa Knutson'smost importantfamily members
Grandpa Knutson's Native Country Food
Lefse
Here is how you make this tasty treat.
Step 1: Make Potato Balls
Step 2: Prepare The Rolling Area
Step 3: Roll out the dough
Grandpa Knutson'straditions
Celebrations
Christmas, Thanksgiving,Easter, Birthdays, etc...
Grandpa Knutson'sfamily fun
Going to the lake
Grandpa Knutson's Favorite Family fact
New Family
Keeps finding new family members because Grandpa was adopted.
The end
Created by Riley Courtney


Order
Here you can download a query from youtube in which you will have to choose your language area and the ranking order result (called order by google).
youtube docs list#order
Part
-
date – Resources are sorted in reverse chronological order based on the date they were created.
-
rating – Resources are sorted from highest to lowest rating.
-
relevance – Resources are sorted based on their relevance to the search query. This is the default value for this parameter.
-
title – Resources are sorted alphabetically by title.
-
videoCount – Channels are sorted in descending order of their number of uploaded videos.
-
viewCount – Resources are sorted from highest to lowest number of views. For live broadcasts, videos are sorted by number of concurrent viewers while the broadcasts are ongoing.
By default Part option here will download only snippet information. But you can only choose to download id from video if you get very high costing query in order to be a little more saver.
youtube docs list#part
Language
Choose your language.
youtube docs list#part


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Still curious? Watch Google’s video about cookies. Or check out allaboutcookies.org and youronlinechoices.eu.
Small pieces of information can make a big difference to how we design our site for you. Imagine if data gathered by cookies showed us that 80% of Torchbox visitors look at our site on their mobile phone. Insights like that can help us focus our energy on making a better mobile version for you.
Our cookies come from four main sources: Google Analytics, Google Optimize, FullStory and Hotjar.
We sometimes connect our site to services like Facebook and Twitter and embed the occasional YouTube video. Like Torchbox, these sites also use cookies, but as you can imagine, we have no control over these third-party cookies.
Take a look at their privacy policies in more detail here:
In a nutshell, we use some third-party cookies to check how well our Facebook, Twitter and LinkedIn advertising campaigns are doing and improve their relevance to you. They allow us to show you ads if you have visited our site before and then track important behaviours that are valuable to us such as whether you fill in a contact form.
Here are some links that explain interest-based advertising and how to opt-out:
Finally, we also use Google Ads. This allows us to show ads that are more relevant to you. We base this on what you are searching for on Google, and webpages that appeared to interest you most when you visited our site in the past. Why? We want you to come back of course.
Here’s a handy section highlighting some of the first-party cookies stored on your device by browsing our site. Take a look at what data they collect and why.
They tell us which page you arrive at, how you navigate between pages and the device you used.
We use this information to make improvements to the experience of individual pages and the journey through the site as a whole. For example, by ensuring you are finding what you are looking for easily.
Used to record a person’s involvement in a website experiment e.g. an A/B test where half of our website users see one webpage, and the other half see an alternative version.
This helps us test that the changes we make to our site are actually making it better.
FullStory records a video of how people use our site. This gives us a clearer picture of the website experience people are having and helps us quickly identify any everyday problems people encounter using our site.
We use Hotjar to track the step-by-step path you take through the website. It also shows us when you exit a section of the site e.g. when you click on a blog post and then leave before reading to the end.
This helps us figure out what content you like, what needs to be tweaked and what we need to get rid of.
Although your data is made anonymous to keep it safe, with all websites there is a risk, however small, that someone will try to maliciously steal information. We believe that cookies pose little threat to your privacy. But ultimately, we understand that you may still want to opt-out and turn off cookies.
To delete or block existing cookies, click the relevant link to the browser you are using and follow the instructions.
It is worth noting that refusing cookies may impact your experience of using our website (which we would love for you to experience at its best). But, it’s entirely up to you.
If after changing your browser settings you do experience difficulties in using our website, please get in touch.
By continuing to use our website, we’ll assume you agree to the cookie policy we have talked about above.


Q142. Linked List Cycle II
Given a linked list, return the node where the cycle begins. If there is no cycle, return null.
There is a cycle in a linked list if there is some node in the list that can be reached again by continuously following the next pointer. Internally, pos is used to denote the index of the node that tail's next pointer is connected to. Note that pos is not passed as a parameter.
Notice that you should not modify the linked list.
Follow up:
Can you solve it using O(1) (i.e. constant) memory?
Example 1:
Input: head = [3,2,0,-4], pos = 1
Output: tail connects to node index 1
Explanation: There is a cycle in the linked list, where tail connects to the second node.
Example 2:
Input: head = [1,2], pos = 0
Output: tail connects to node index 0
Explanation: There is a cycle in the linked list, where tail connects to the first node.
Example 3:
Input: head = [1], pos = -1
Output: no cycle
Explanation: There is no cycle in the linked list.
Constraints:
The number of the nodes in the list is in the range [0, 104].
-105 <= Node.val <= 105
pos is -1 or a valid index in the linked-list.
This question is quite challenging for me. I did not come up with the idea .. It turns out that we need two pointers to do the trick. I feel that it is somewhat clever and fit the question just well.
To solve the question essentially contains two steps:
- Identify if there is loop.
- If so, identify the node which has two incoming edges.
The first step is “Q141. Linked List Cycle”. (If I was given this question, I could not get it, either ..)
The trick is to use two pointers which have different speeds.
Specifically, let’s have two pointers, slow
and fast
, where fast
goes twice as fast as slow
.
Then, we can image that:
- If there is no loop,
fast
will hit None eventually and we can terminate safely. - If there is loop,
fast
andslow
will meet eventually. And suppose the algorithm goes forn
steps when they meet, thenfast
goesn
steps more thanslow
andn
is actually the length of (or the multiple of it) the loop.
OK, here comes to the second part: How to identify the “bad” node (where the loop “start”)?
If the length between the head node and the “bad” node is L
, then we want the fast
node to go extra L
step to hit the “bad” node (since it already goes 2n - L
steps in the loop and need L
extra steps to be the multiple of loop size).
We could implement so by sending slow
to the head node and let slow
and fast
move in the same speed.
By doing so, they will met at the “bad” node since after L
steps, slow
also appears at the “bad” node.
The Python submission.
# Definition for singly-linked list.
# class ListNode:
# def __init__(self, x):
# self.val = x
# self.next = None
class Solution:
def detectCycle(self, head: ListNode) -> ListNode:
slow = head
fast = head
# fast is twice as fast as slow
while fast is not None and fast.next is not None:
fast = fast.next.next
slow = slow.next
if fast is not None and slow == fast:
break
# print(slow.val, fast.val)
if fast is None or fast.next is None:
return None
slow = head
while fast != slow:
fast = fast.next
slow = slow.next
# print(slow.val, fast.val)
return slow


[![Build Status](secure.travis-ci.org/cucumber/cucumber.png)](http://travis-ci.org/cucumber/cucumber) [![Dependency Status](gemnasium.com/cucumber/cucumber.png)](https://gemnasium.com/cucumber/cucumber)
The main website is at cukes.info/ The documentation is at wiki.github.com/cucumber/cucumber/
## Note on Patches/Pull Requests
- Fork the project.
- Make your feature addition or bug fix.
- Add tests for it. This is important so I don’t break it in a future version unintentionally.
- Commit, do not mess with Rakefile, version, or history. (if you want to have your own version, that is fine but bump version in a commit by itself I can ignore when I pull)
- Send me a pull request. Bonus points for topic branches.
## Running tests
gem install bundler bundle install git submodule update --init --recursive rake
## Release Process
Before you even attempt to do a release, make sure you can log into cukes.info and touch a file in /var/www/cucumber/api/ruby (see gem_tasks/yard.rake). You need to be able to do this in order to upload YARD docs as part of the release.
- Bump the version number in `lib/cucumber/platform.rb`.
- Make sure `History.md` is updated with the upcoming version number, and has entries for all fixes.
- No need to add a `History.md` header at this point - this should be done when a new change is made, later.
Now release it
bundle update rake git commit -m "Release X.Y.Z" rake release
## Copyright
Copyright © 2008,2009,2010,2011 Aslak Hellesøy and Contributors. See LICENSE for details.


grub-probe - probe device information for GRUB
grub-probe [OPTION...] [OPTION]... [PATH|DEVICE]
Probe device information for a given path (or device, if the -d option is
given).
- -0
- separate items in output using ASCII NUL characters
- -d, --device
- given argument is a system device, not a path
- -m, --device-map=FILE
- use FILE as the device map [default=/boot/grub/device.map]
- -t, --target=TARGET
- print TARGET available targets: abstraction, arc_hints, baremetal_hints,
bios_hints, compatibility_hint, cryptodisk_uuid, device, disk, drive,
efi_hints, fs, fs_label, fs_uuid, gpt_parttype, hints_string,
ieee1275_hints, msdos_parttype, partmap, partuuid, zero_check
[default=fs]
- -v, --verbose
- print verbose messages.
- -?, --help
- give this help list
- --usage
- give a short usage message
- -V, --version
- print program version
Mandatory or optional arguments to long options are also mandatory
or optional for any corresponding short options.
Report bugs to<EMAIL_ADDRESS>grub-fstest(1)
The full documentation for grub-probe is maintained as a
Texinfo manual. If the info and grub-probe programs are
properly installed at your site, the command
- info grub-probe
should give you access to the complete manual.


This is my first web page. I would like to introduce 2 artists who I like.Below are one of their masterpieces.
Below are the images pictures that they made.
Can you guess who they are?


{% extends "base.html" %} {% block content %}
bikestat pulls in data from Capital Bikeshare's page of trip history data. I'm an avid bikeshare user and an info junkie so I thought might be fun to do something interesting to highlight how cool it is that they're sharing the data.
This site is based on free data, and you can have the code that runs it, too, it's free software. See github.com/dchud/bikestat for the code. bikestat is a django app using PostgreSQL, D3, and bootstrap. If you have suggestions for enhancements or fixes please feel free to fork the code on github, send pull requests, and file tickets.
For any other questions or comments, I'm @dchud on twitter.
{% endblock %}


Symbol Index for "Derived Agent By Agreement"
Symbol Index for diagram
"Derived Agent By Agreement"
DA/volition (Generalization Node)
Derived Agent (Class)
Derived Agent By Agreement (Class)
Derived Agent By Decree (Class)


Image
class Image extends BaseCachedFile implements EventSubscriberInterface
Image management actions. This class handles image processing and caching.
Basically, images are stored outside of the web space (by default in local/media/images), and cached inside the web space (by default in web/local/images).
In the images caches directory, a subdirectory for images categories (eg. product, category, folder, etc.) is automatically created, and the cached image is created here. Plugin may use their own subdirectory as required.
The cached image name contains a hash of the processing options, and the original (normalized) name of the image.
A copy (or symbolic link, by default) of the original image is always created in the cache, so that the full resolution image is always available.
Various image processing options are available :
- resizing, with border, crop, or by keeping image aspect ratio
- rotation, in degrees, positive or negative
- background color, applyed to empty background when creating borders or rotating
- effects. The effects are applied in the specified order. The following effects are available:
- gamma:value : change the image Gamma to the specified value. Example: gamma:0.7
- grayscale or greyscale: switch image to grayscale
- colorize:color : apply a color mask to the image. Exemple: colorize:#ff2244
- negative : transform the image in its negative equivalent
- vflip or vertical_flip : vertical flip
- hflip or horizontal_flip : horizontal flip
If a problem occurs, an ImageException may be thrown.
Constants
| EXACT_RATIO_WITH_BORDERS |
|
| EXACT_RATIO_WITH_CROP |
|
| KEEP_IMAGE_RATIO |
|
Methods
Toggle visibility for an object
Clear the file cache. Is a subdirectory is specified, only this directory is cleared.
Take care of saving a file in the database and file storage
Take care of updating file in the database and file storage
Deleting file in the database and in storage
Process image and write the result in the image cache.
No description
Details
in
BaseAction at line 92
mixed
genericToggleVisibility(
ModelCriteria $query,
ToggleVisibilityEvent $event)
Toggle visibility for an object
in
BaseCachedFile at line 48
__construct(
FileManager $fileManager)
in
BaseCachedFile at line 65
clearCache(
CachedFileEvent $event)
Clear the file cache. Is a subdirectory is specified, only this directory is cleared.
If no directory is specified, the whole cache is cleared. Only files are deleted, directories will remain.
in
BaseCachedFile at line 198
saveFile(
FileCreateOrUpdateEvent $event)
Take care of saving a file in the database and file storage
in
BaseCachedFile at line 238
updateFile(
FileCreateOrUpdateEvent $event)
Take care of updating file in the database and file storage
in
BaseCachedFile at line 261
deleteFile(
FileDeleteEvent $event)
Deleting file in the database and in storage
in
BaseCachedFile at line 266
updatePosition(
UpdateFilePositionEvent $event)
in
BaseCachedFile at line 271
toggleVisibility(
FileToggleVisibilityEvent $event)
at line 90
processImage(
ImageEvent $event)
Process image and write the result in the image cache.
If the image already exists in cache, the cache file is immediately returned, without any processing If the original (full resolution) image is required, create either a symbolic link with the original image in the cache dir, or copy it in the cache dir.
This method updates the cachefilepath and file_url attributes of the event


There is no way to undo this operation." %}


HTML5 & jQuery Grayscale Demo
Tutorial by
Web Designer Wall
(
read article
). Coding by
Darcy Clarke
.
Themify
IconDock
Web Designer Wall
Koi
Phoenix
Peacock
Flow
Web Designer
Sakura


Associate Buyer
Buyer
Carvana
- Daily Acquisition of used vehicle inventory through multiple online auction and purchasing platforms.
- Vehicle appraising and assessment for risk and reconditioning costs. (Repair costs, vehicle history,
and financial sensibility)
- Self started or participated in numerous projects relating to the vehicle inventory diversity, the
appraisal market benchmarking methods, and overall process implementation.
- Managed Nissan, Infiniti, Tesla, and Toyota inventory with some inventory segment changes as company
grew. Now managing Senior level inventory with BMW and Tesla consulting for trade vehicles.
- Inventory management to include but not limited to my overall margin averages for vehicle models and
which vehicles require more margin or which we can be more aggressive on to keep up with demand.
Determining inventory stocking for a Forward Supply so as to never run into a shortage. Making market
aggression adjustments based on seasonality. Growing a well diversified inventory offering to the
market.
- Utilizing SQL Databases to develop usable reporting systems to help more efficiently manage inventory.
Buyer Performance
- Hold company record for most vehicles purchased in a given day at 131 units generating projected
profits of about $160,000.
- Consistent KPI metric goals met or exceeded in all Inventory Groups.
- Reassignment to a Senior Buyer Inventory Group with BMW and Tesla.
April 2017 - April 2018
April 2018 - Present
Product Specialist
Gallery Advisor
Tesla Motors
- Organize and oversee off-site marketing events, delegate drive appointment co-pilots, and co-pilot
drives of my own.
- Maintained a high-end clientele pipeline of over 100+ people at any given time; including vehicle
purchase assistance, guiding customers through the methods of payment, trade-in valuation, and vehicle
acquisition consultations.
- Co-Manager with other Advisors of the vehicle showroom and all product specialists.
Advisor Performance
- First Gallery Advisor in Arizona.
- Revenue generation from May of 2015 to April of 2016. 126 Vehicles creating $13.54 Million in Revenue
with average vehicle price of $107,500.
- Diligent KPI metric attention.
- Test drive satisfaction survey scores from the last 90 Days at 9.80/10.
- Churn (Cancellation) Rate of 4.7%. Lowest in the Region.
November 2013 to March 2015
March 2015 - June 2016


--- layout: default title: Contributing to New York Hacks --- New York Hacks is a community-driven online resource by devs for devs. The goal is to highlight the best resources and events for developers in the New York City area:
gh-pages
branchWe will review all pull requests and make sure they are on-topic and adding value to the developer community in New York.
Same as above, with a few extra steps:
git commit -m "{post title}"
)Please make sure you include a YAML header at the top of the file that defines the title
of your post and specifies the "post" for layout
. Please also include your Github handle for the author
.
--- title: Test layout: post author: github_handle tags: - any - tags - you - like ---
Submitting an event is just like submitting a post. Follow the instructions above and you're 90% of the way there. Consider the content of the post to both be an announcement of your event and the core information that people need to know to find your event.
The only difference is that in order for your event to make it into our calendar you need to define some additional info. All of these variables are **required**:
--- event-name: New York Hack Day event-allday: false event-start: 2013-04-24T18:00:00-08:00 event-end: 2013-04-24T20:30:00-8:00 event-venue: The Alley event-address: 500 7th Ave, 17th Floor, New York, NY event-website: http://nyhackday.whatever.com/ ---


{% extends "bases/bootstrap-with-nav.html" %} {% load staticfiles %} {% load bootstrap3 %} {% block content %}
Please see below a list of all cast ballots for this poll. Voters, you can audit your own ballot by viewing it and then selecting the 'Audit' button.
| # | Voter Alias | Encrypted Ballot Handle | Ballot Selection | View |
|---|---|---|---|---|
| {{ forloop.counter }} | {{ ballot.voter.alias }} | {{ ballot.enc_ballot_handle }} | {{ ballot.selection }} | View |


Before Change
Completes one feedforward step, ensuring net is set to evaluation model returns: network output given input x
"""
self.set_train_eval(train=False)
outs = self(x)
if type(outs) is list:
outs = [o.data for o in outs]
else:
outs = outs.data
return outs
def set_train_eval(self, train=True):
"""Helper function to set model in training or evaluation mode"""
nets = [self.state_proc_model, self.rnn] + self.out_layers
After Change
Completes one feedforward step, ensuring net is set to evaluation model returns: network output given input x
"""
self.eval()
return self(x)
def __str__(self):
"""Overriding so that print() will print the whole network"""
s = self.state_proc_model.__str__() + "\n" + self.rnn_model.__str__()


Saffroncoin
SaffronCoin (SFR) is a crypto-currency that enables instant payments to anyone, anywhere in the world. It uses distributed peer-to-peer technology to operate with no central authority; transactions and coin minting are carried out collectively by the network. It is a Proof-of-Work based coin and uses multiple algorithms to find the next block hence increasing the security of the network.
SaffronCoin is the first multi-algorithm coin to feature two of the most promising algorithms today i.e. X11 and Blake. The other algorithms used are SHA256D, Scrypt and Groestl.


Profile
I am an intermediately experienced developer
Experience
Web Developer
Company | AUG 2016 » Current
- Develop feature for the platform
- Develop feature for the platform
Academics
Company | AUG 2016 » Current
- Class 11 & 12
- Bachelors in BIT
Education
I am currently in my Bachelors Degree and seeking in pursuing other skill and expertise.
Skills
- Programming
- Photo Editing
- Project Management
Awards
National Coding Championship
Champion
2019


For the fig-swirl: Melt butter over medium heat in a saucepan. Add brown sugar and stir to dissolve. Halve all of the figs and toss in the saucepan with water and lemon juice. Cook over medium heat, stirring frequently, until you have a chunky-jammy mixture. Add salt with one or two stirs, set aside and let cool completely.
Ice cream: In a small pot over medium heat, combine milk, and granulated sugar until sugar is completely dissolved and the milk is just barely lukewarm. Whisk in the egg yolks. Set mixture in the fridge and wait until the fig mixture is cooled.
Using an ice cream machine, pour liquids into the frozen hasin and process according to manufacturer instructions, i.e., let spin and thicken for 20 minutes before adding mascarpone, fig jam mixture, and the nuts. Continue to process for - / +10 minutes. Pour semi-frezen mixture into a pyrex dish or glass tupperware. Freeze for at least two hours before serving.


Moving (on)...
Posted: Sat Aug 18, 2007 12:01 pm
So I'm in the middle of moving back into my residence hall for the school year, and I haven't been around to update a lot--but a lot has been done recently. Once I get more than 15 minutes at a time to answer the 27 emails I get per day I'll post a more thorough update of what I expect to happen this semester


Complete the form below to receive an email with the authorization code needed to reset your password.


{{Orphan|date=February 2009}} {{Infobox prepared food | name = Oille | image = | caption = | alternate_name = | country = France | region = | creator = | course = | type = Soup | served = | main_ingredient = | variations = | calories = | other = }}
L'oille is a French potée or soup believed to be the forerunner of pot-au-feu composed of various meats and vegetables.<ref name=larousse>Larousse Gastronomique (1961), Crown Publishers<br>(Translated from the French, Librairie Larousse, Paris (1938))</ref>
It has been suggested by some authorities that the derivation of the word oille is from the Spanish olla podrida but it seems more likely that it is derived from oule, which is used in south-west France to describe an earthenware pot in which the dish is prepared. There are many examples of pot-à-oille (tureens) on display in museums, some of them very ornate indicating that this dish was enjoyed by people from all social classes, ingredients varying greatly.<ref>Association l'eau à la bouche</ref>
There are three kinds of oilles:
An old recipe for this dish contains chicken and pigeons, all stuffed and trussed, beef, veal, onion, parsnips, carrots, turnips, leeks, purslane, orach and chard. All are simmered for five hours. At the end of this time, slices of lightly toasted bread are placed in the bottom of a tureen and this is heated until the bread starts to stick to the bottom. The birds are removed from the soup and placed on the bread and the rest is strained over them. The remaining meat and vegetables are discarded.<ref name=larousse/>
{{reflist}}


Exploring web technology, programming, algorithms and algorithmic music.
Brief description / summary here. paragraph giving overview of site and about page?
Provide a few details about the main features of the project here...
Here are a few details about the main features of the project, which has some details which need to be detailed here
Here are a few details about the main features of the project...


The R logo is © 2016 The R Foundation.
You can distribute the logo under the terms of the Creative Commons Attribution-ShareAlike 4.0 International license (CC-BY-SA 4.0) or (at your option) the GNU General Public License version 2 (GPL‑2).
It is available in raster and vector formats:
The CC-BY-SA 4.0 license allows you to share and adapt the logo for any purpose, including commercial use, provided that you give appropriate credit, provide a link to the license, and indicate if any changes were made. It is also one-way compatible with the GNU General Public License version 3 (GPL‑3). However, CC-BY-SA 4.0 is not compatible with GPL‑2. If you wish to incorporate the logo into works licensed under GPL-2 then you must use the GPL-2 license for the logo.
Note that special considerations apply when using the GPL-2 license for the logo or when incorporating it into works licensed under GPL-3 . In particular, the GPL includes the concept of source, which is defined as “the preferred form of the work for making modifications to it”. We consider the vector version of the R logo in SVG format to be the source by this definition.