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PETITIONER: M.C. MEHTA ETC. ETC. Vs. RESPONDENT: UNION OF INDIA AND OTHERS ETC. ETC. DATE OF JUDGMENT15/05/1992 BENCH: MISRA, RANGNATH (CJ) BENCH: MISRA, RANGNATH (CJ) KANIA, M.H. KULDIP SINGH (J) CITATION: 1991 SCR (1) 866 1991 SCC (2) 353 JT 1991 (1) 620 1991 SCALE (1)427 ACT: Constitution of India, 1950: Article 32-Environmental and air pollution-Control of- Operation of mechanical crushers-Stoppage of-Allotment of sites in the newly set-up crushing zone-Directions issued. HEADNOTE: Writ Petitions were filed before this Court, challenging the action of the owners/proprietors of the stone-crushers whereby stone-dust and smoke was allowed to pass into the air, and due to pollution, lives of the people living in some parts of South Delhi, especially rural, and workers, their families living at the site as well as people living in the villages bordering Haryana were suffering from different diseases and their lives were in danger, and praying for issue of writ of mandamus and directions to the respondents and authorities concerned to take steps to stop pollution, and provide necessary medical facilities to the affected persons. Giving directions, reasons for which to be followed later, this Court, HELD : 1. Environmental changes are the inevitable consequence of industrial development in our country, but, at the same time the quality of environment cannot be permitted to be damaged by polluting the Air, Water and Land to such an extent that it becomes a health-hazard for the residents of the area. The authorities concerned in the Union Territories of Delhi have been wholly re-miss in the performance of their statutory duties and have failed to protect the environment and control air-pollution in the Union Territory of Delhi. Utter disregard to environment has placed Delhi in unenviable position of being the world's third grubbiest, most polluted and unhealthy city as per a study conducted by the World Health Organisation. Needless to say that every citizen has a right to fresh air and to live in pollution-free environment. [359 F-H, 360 A] 2. It is, therefore, directed that (a) the mechanical stone crushers 358 established/operating in Lal Kuan, Anand Parbat, Rajokri, Tughlakabad and in any other area of Union territory of Delhi and also in Suraj Kund, Lakhanpur, Lakkarpur, Kattan, Gurukul, Badkhal, Pallinangla, Saraikhaja, Anangpur and Ballabgarh areas of Haryana should stop operating/functioning with effect from August 15, 1992 and no stone crusher will operate in the above said areas from August 15, 1992 onward; (b) the stone crushers in the Union territory of Delhi/Faridabad-Ballabgarh Complex which do not have valid licences from the Authorities under the Delhi Municipal Corporation Act, 1957/Faridabad Complex Administration (Regulations and Development) Act, 1971 or from any other authority which the law requires, and the stone crushers, in respect of which closure- orders/directions have been issued by the Central Pollution Control Board under Section 31A of Air (Prevention and Control of Pollution) Act, 1981 or by the Central Government under Section 5 of the Environment (Protection Act, 1956, should stop functioning/operating with immediate effect; (c) the authorities concerned should ensure compliance of these order; and (d) the authorities concerned of the State of Haryana should demarcate, and allot the sites to the aforesaid stone crushers in the newly approved 'crushing zone' at village Pali - set up with the object of rehabilitating the existing stone-crushers who are being stopped from functioning as a result of the orders of this Court - by draw of lots or by any other fair and equitable method, and also to provide additional land in or around the "crushing zone" if there is not sufficient land in the said zone to accommodate all the stone crushers affected by this Court's orders within a period of six months. A progress report in this respect should be sent to the Registry of this Court before July 31, 1992. [360 B-C, E-H, 361 A-G] 3. Some Writ Petitions, which were filled by the owners/proprietors of stone-crushers in the Delhi High Court and which have been directed to be transferred to this Court are dismissed. [360 E] JUDGMENT:
ORIGINAL JURISDICTION : Writ Petition (C) No.4677 of 1985.
(Under Article 32 of the Constitution of India). WITH T.C.(C) No. 75-89/91 W.P.(C) No.521/87 T.P.(C) No.245- 50/91.
M.C. Mehta Petitioner-in-person.
359Satish Chander, Hardev Singh, P.N. Duda, B.R.L. Iyengar, P.P. Rao, V.C. Mahajan, S.S. Chadha, R. Mohan, Ms. Rekha Pandey, K.B. Rohtagi, S.K. Dhingra, Shashank Shekhar, Vishnu Mathur, Vijay Pandita, Randhir Jain, N.S. Bisht, Ms. Seema Midha, K.R.R. Pillai, R.S. Suri, R.B. Misra, C.V. Subba Rao, Kailash Vasdev, V.B. Saharya, Ranjit Kumar, R. Sridharan, Ms. Indu Malhotra, P.K. Jain, I.S. Goyal, Dr. A.M. Singhvi, R. Sasiprabhu, Ms. Anil Katiyar and B.Y. Kulkarni for the appearing parties.
The following Order of the Court was delivered: We reserved judgment in these matters on April 24, 1992. We heard learned counsel for the parties at considerable length for several days. We have been taken through the provisions of Delhi Development Act 1957, Master Plan for Delhi published in the Gazette of India dated August 1, 1990, Delhi Development Authority (Zoning) Regulations, 1983, Delhi Municipal Corporation Act 1957, Faridabad Complex Administration (Regulations and Development) Act, 1971, The Development Plan for the Faridabad-Ballabgarh Controlled Areas published in the Haryana Government Gazette dated December 17, 1991, Air (Prevention and Control of Pollution) Act 1981, The Environment (Protection Act) 1986, National Health Policy, 1985, Ancient Monuments Act, 1958, National Capital Region Planning Board Act, 1985 and various other documents including reports of the Experts on Air Pollution and environments. The parties have submitted detailed written arguments which we have taken into consideration. We have given our thoughful consideration to the various arguments advanced by the learned counsel for the parties.
We are conscious that environmental changes are the inevitable consequence of industrial development in our country, but at the same time the quality of environment cannot be permitted to be damaged by polluting the Air, Water and Land to such an extent that it becomes a health- hazard for the residents of the area. We are constrained to record that Delhi Development Authority, Municipal Corporation of Delhi, Central Pollution Control Board and Delhi Pollution Control Committee have been wholly re-miss in the performance of their statutory duties and have failed to protect the environments and control air-pollution in the Union Territory of Delhi. Utter disregard to environment has placed Delhi in an unenviable position of being the world's third grubbiest, most polluted and unhealthy city as per a study conducted by the World Health Organisation. Needless 360 to say that every citizen has a right to fresh air and to live in pollution-free environments.
For the reasons to be recorded and pronounced at a later stage we order and direct as under:-
(1) The mechanical stone crushers established/operating in Lal Kuan, Anand Parbat, Rajokri, Tughlakabad and in any other area of Union territory of Delhi shall stop operating/functioning with effect from August 15, 1992. No stone crusher shall operate in the Union territory of Delhi from August 15, 1992 onward.
(2) The mechanical stone crushers established/operating in Suraj Kund, Lakhanpur, Lakkarpur, Kattan, Gurukul, Badkhal, Pallinangla, Saraikhaja, Anangpur and Ballabgarh areas of Haryana shall stop operating/functioning with effect from August 15, 1992. No stone crusher shall operate in the above said area form August 15, 1992 onward.
(3) The writ petitions filed by the owners/proprietors of stone crushers in Delhi High Court which have been transferred to this Court shall stand dismissed with no order as to costs. (4) The Stone crushers in the Union territory of Delhi/Faridabad-Ballabgarh Complex which do not have valid licences from the Authorities under the Delhi Municipal Corporation Act 1957/Faridabad Complex Administration (Regulations and Development) Act 1971 or from any other authority which the law requires, shall stop functioning and operating with immediate effect.
(5) The stone crushers, in respect of which closure-orders/directions have been issued by the Central Pollution Control Board under Section 31A of Air (Prevention and Control of Pollution) Act, 1981 or by the Central Government under Section 5 of the Environment (Protection) Act 1956, shall stop functioning/operation with immediate effect. (6) The Delhi Development Authority through its Vice Chairman and Commissioner (Planning), the Delhi Municipal Cor-
361poration through its Commissioner, Faridabad Complex Administration through its Chief Administrator, Director Town and Country Planning Department, Haryana Deputy Commissioner, Faridabad, Haryana Urban Development Authority through its Commissioner/Chief Executive, Central Pollution Control Board through its Member Secretary, Central Government under the Environment (Protection Act) 1986 and the Commissioner Police Delhi are directed to ensure the compliance of our above orders.
(7) The officers of the Town and Country Planning Department, Government of Haryana, who were present in Court, informed us that a new "crushing zone" has been approved at village Pali and the lay out Plan has been prepared and is in the process of demarcation by the Haryana Urban Development Authority. The said "crushing zone" has been set up with the object of rehabilitating the existing stone crushers who are being stopped from functioning as a result of our orders. We, therefore, direct the State of Haryana through the Director, Town and Country Planning Department, Haryana, Chandigarh, the Chief Administrator, Faridabad Complex Administration, the Deputy Commissioner, Faridabad and the Haryana Urban Development Authority to demarcate, and allot the sites to the stone crushers mentioned in paras 1,2,4 and 5 above by draw of lots or by any other fair and equitable method. We further direct these authorities to provide additional land in or around the "crushing zone" if there is not sufficient land in the said zone to accommodate all the stone crushers affected by our orders. This exercise shall be completed and plots offered to the stone crushers within a period of six months from today. The Director, Town and Country Planning Department, Haryana, Chandigarh is further directed to send a progress report to the Registry of this Court before July 31, 1992 in this respect.
(8) We make it clear that our orders contained in paras 1,2,4 and 5 above are in absolute terms and are not dependent or consequential to our directions in para 7 above.
362We are not finally disposing of the writ petition at this stage and we keep it pending for the purposes of monitoring the above directions. To be listed on August 5, 1992.
The copies of this order be sent to all the Authorities mentioned in paras 6 and 7 above within two days.
N.P.V. Reasons to follow.
363


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See below for test results. Error details are shown in the console on supporting browsers.
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fun toString(): String
Returns string representation of the Quad including its first, second, third, and fourth values.


public static class Border.Builder
extends java.lang.Object
| Modifier and Type | Method and Description |
|---|---|
Border |
build() |
Border.Builder |
color(YogaEdge edge,
int color)
Specifies a color for a specific edge
|
Border.Builder |
colorRes(YogaEdge edge,
int colorRes)
Specifies a color for a specific edge
|
Border.Builder |
cornerEffect(float radius)
Deprecated.
Please use
radiusPx(int) instead |
Border.Builder |
dashEffect(float[] intervals,
float phase)
Applies a dash effect to the border
|
Border.Builder |
discreteEffect(float segmentLength,
float deviation)
Applies a discrete effect to the border
|
Border.Builder |
pathDashEffect(Path shape,
float advance,
float phase,
PathDashPathEffect.Style style)
Applies a path dash effect to the border
|
Border.Builder |
radiusAttr(int attrId)
Specifies the border radius for all corners
|
Border.Builder |
radiusAttr(int attrId,
int defaultResId)
Specifies the border radius for all corners
|
Border.Builder |
radiusAttr(int corner,
int attrId,
int defaultResId)
Specifies the border radius for the given corner
|
Border.Builder |
radiusDip(float radius)
Specifies the border radius for all corners
|
Border.Builder |
radiusDip(int corner,
float radius)
Specifies the border radius for the given corner
|
Border.Builder |
radiusPx(int radius)
Specifies the border radius for all corners
|
Border.Builder |
radiusPx(int corner,
int radius)
Specifies the border radius for the given corner
|
Border.Builder |
radiusRes(int radiusRes)
Specifies the border radius for all corners
|
Border.Builder |
radiusRes(int corner,
int res)
Specifies the border radius for the given corner
|
Border.Builder |
widthAttr(YogaEdge edge,
int attrId)
Specifies a width for a specific edge
|
Border.Builder |
widthAttr(YogaEdge edge,
int attrId,
int defaultResId)
Specifies a width for a specific edge
|
Border.Builder |
widthDip(YogaEdge edge,
float width)
Specifies a width for a specific edge
|
Border.Builder |
widthPx(YogaEdge edge,
int width)
Specifies a width for a specific edge
|
Border.Builder |
widthRes(YogaEdge edge,
int widthRes)
Specifies a width for a specific edge
|
public Border.Builder widthPx(YogaEdge edge, int width)
Note: Having a border effect with varying widths per edge is currently not supported
edge
- The YogaEdge
that will have its width modifiedwidth
- The desired width in raw pixelspublic Border.Builder widthDip(YogaEdge edge, float width)
Note: Having a border effect with varying widths per edge is currently not supported
edge
- The YogaEdge
that will have its width modifiedwidth
- The desired width in density independent pixelspublic Border.Builder widthRes(YogaEdge edge, int widthRes)
Note: Having a border effect with varying widths per edge is currently not supported
edge
- The YogaEdge
that will have its width modifiedwidthRes
- The desired width resource to resolvepublic Border.Builder widthAttr(YogaEdge edge, int attrId)
Note: Having a border effect with varying widths per edge is currently not supported
edge
- The YogaEdge
that will have its width modifiedattrId
- The attribute to resolve a width value frompublic Border.Builder widthAttr(YogaEdge edge, int attrId, int defaultResId)
Note: Having a border effect with varying widths per edge is currently not supported
edge
- The YogaEdge
that will have its width modifiedattrId
- The attribute to resolve a width value fromdefaultResId
- Default resource value to utilize if the attribute is not setpublic Border.Builder radiusPx(int radius)
radius
- The desired border radius for all cornerspublic Border.Builder radiusDip(float radius)
radius
- The desired border radius for all cornerspublic Border.Builder radiusRes(int radiusRes)
radiusRes
- The resource id to retrieve the border radius value frompublic Border.Builder radiusAttr(int attrId)
attrId
- The attribute id to retrieve the border radius value frompublic Border.Builder radiusAttr(int attrId, int defaultResId)
attrId
- The attribute id to retrieve the border radius value fromdefaultResId
- Default resource to utilize if the attribute is not setpublic Border.Builder radiusPx(int corner, int radius)
corner
- The Border.Corner
to specify the radius ofradius
- The desired radiuspublic Border.Builder radiusDip(int corner, float radius)
corner
- The Border.Corner
to specify the radius ofradius
- The desired radiuspublic Border.Builder radiusRes(int corner, int res)
corner
- The Border.Corner
to specify the radius ofres
- The desired dimension resource to use for the radiuspublic Border.Builder radiusAttr(int corner, int attrId, int defaultResId)
corner
- The Border.Corner
to specify the radius ofattrId
- The attribute ID to retrieve the radius fromdefaultResId
- Default resource ID to use if the attribute is not setpublic Border.Builder color(YogaEdge edge, int color)
edge
- The YogaEdge
that will have its color modifiedcolor
- The raw color value to usepublic Border.Builder colorRes(YogaEdge edge, int colorRes)
edge
- The YogaEdge
that will have its color modifiedcolorRes
- The color resource to usepublic Border.Builder dashEffect(float[] intervals, float phase)
Specifying two effects will compose them where the first specified effect acts as the outer effect and the second acts as the inner path effect, e.g. outer(inner(path))
intervals
- Must be even-sized >= 2. Even indices specify "on" intervals and odd indices
specify "off" intervalsphase
- Offset into the given intervals@Deprecated public Border.Builder cornerEffect(float radius)
radiusPx(int)
insteadradius
- The amount to round sharp angles when drawing the borderpublic Border.Builder discreteEffect(float segmentLength, float deviation)
Specifying two effects will compose them where the first specified effect acts as the outer effect and the second acts as the inner path effect, e.g. outer(inner(path))
segmentLength
- Length of line segmentsdeviation
- Maximum amount of deviation. Utilized value is random in the range
[-deviation, deviation]public Border.Builder pathDashEffect(Path shape, float advance, float phase, PathDashPathEffect.Style style)
Specifying two effects will compose them where the first specified effect acts as the outer effect and the second acts as the inner path effect, e.g. outer(inner(path))
shape
- The path to stamp alongadvance
- The spacing between each stampphase
- Amount to offset before the first stampstyle
- How to transform the shape at each positionpublic Border build()


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Identify the member disks with ssaraid -H -lssa0 -n pdisk [n] -u -a use=member
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I'm a sophomore currently pursuing a B.S. in Computer Science and a B.A. in Math at Tufts University's School of Engineering.
When it comes to programming, most of my experience comes from my courses in school. I've learned a lot about building programs in C++ and websites and servers using HTML and Javascript. I spend a lot of my free time learning new languages and playing around with unfamiliar technologies. I'll often build something (e.g. this website) just to learn new skills.
When I'm not in front of the computer, I enjoy traveling with my family, watching The Office reruns, reading Calvin and Hobbes, or playing a casual game of poker with my friends.


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Blueprints provides a set of interfaces for the property graph data model. An example instance is diagrammed above. In order to make a data management system “Blueprints-enabled,” the Blueprints interfaces must be implemented. However, note that there are various Graph
interfaces, each with different types of functionality. For example, if an application only needs a Graph
, then a TransactionalGraph
implementation is not required of the underlying graph.
The following diagram identifies the names of the different components of a Graph
. In general, these are the basic components of a property graph.
- Graph: An object that contains vertices and edges.
- Element: An object that can have any number of key/value pairs associated with it (i.e. properties)
- Vertex: An object that has incoming and outgoing edges.
- Edge: An object that has a tail and head vertex.
A property graph has these elements:
- a set of vertices
- each vertex has a unique identifier.
- each vertex has a set of outgoing edges.
- each vertex has a set of incoming edges.
- each vertex has a collection of properties defined by a map from key to value.
- a set of edges
- each edge has a unique identifier.
- each edge has an outgoing tail vertex.
- each edge has an incoming head vertex.
- each edge has a label that denotes the type of relationship between its two vertices.
- each edge has a collection of properties defined by a map from key to value.


WMAR Marist College Radio
2016-2017
Co-Host
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- Weekly night show lasting about 2hrs
Eagle Scout
2014
Project
- Designed, managed and led a 145 man hour service project to landscape and
improve an area outside of my high school
- The project included leading 30
volunteers to trim overgrown shrubs and trees, mulch, and plant new plants
- The project also included renovating two benches and building a new bench


This notebook contains an excerpt from the Python Data Science Handbook by Jake VanderPlas; the content is available on GitHub.
The text is released under the CC-BY-NC-ND license, and code is released under the MIT license. If you find this content useful, please consider supporting the work by buying the book!
< In-Depth: Manifold Learning | Contents | In Depth: Gaussian Mixture Models >
In Depth: k-Means Clustering¶
In the previous few sections, we have explored one category of unsupervised machine learning models: dimensionality reduction. Here we will move on to another class of unsupervised machine learning models: clustering algorithms. Clustering algorithms seek to learn, from the properties of the data, an optimal division or discrete labeling of groups of points.
Many clustering algorithms are available in Scikit-Learn and elsewhere, but perhaps the simplest to understand is an algorithm known as k-means clustering, which is implemented in sklearn.cluster.KMeans
.
We begin with the standard imports:
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns; sns.set() # for plot styling
import numpy as np
Introducing k-Means¶
The k-means algorithm searches for a pre-determined number of clusters within an unlabeled multidimensional dataset. It accomplishes this using a simple conception of what the optimal clustering looks like:
The “cluster center” is the arithmetic mean of all the points belonging to the cluster.
Each point is closer to its own cluster center than to other cluster centers.
Those two assumptions are the basis of the k-means model. We will soon dive into exactly how the algorithm reaches this solution, but for now let’s take a look at a simple dataset and see the k-means result.
First, let’s generate a two-dimensional dataset containing four distinct blobs. To emphasize that this is an unsupervised algorithm, we will leave the labels out of the visualization
from sklearn.datasets.samples_generator import make_blobs
X, y_true = make_blobs(n_samples=300, centers=4,
cluster_std=0.60, random_state=0)
plt.scatter(X[:, 0], X[:, 1], s=50);
By eye, it is relatively easy to pick out the four clusters. The k-means algorithm does this automatically, and in Scikit-Learn uses the typical estimator API:
from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=4)
kmeans.fit(X)
y_kmeans = kmeans.predict(X)
Let’s visualize the results by plotting the data colored by these labels. We will also plot the cluster centers as determined by the k-means estimator:
plt.scatter(X[:, 0], X[:, 1], c=y_kmeans, s=50, cmap='viridis')
centers = kmeans.cluster_centers_
plt.scatter(centers[:, 0], centers[:, 1], c='black', s=200, alpha=0.5);
The good news is that the k-means algorithm (at least in this simple case) assigns the points to clusters very similarly to how we might assign them by eye. But you might wonder how this algorithm finds these clusters so quickly! After all, the number of possible combinations of cluster assignments is exponential in the number of data points—an exhaustive search would be very, very costly. Fortunately for us, such an exhaustive search is not necessary: instead, the typical approach to k-means involves an intuitive iterative approach known as expectation–maximization.
k-Means Algorithm: Expectation–Maximization¶
Expectation–maximization (E–M) is a powerful algorithm that comes up in a variety of contexts within data science. k-means is a particularly simple and easy-to-understand application of the algorithm, and we will walk through it briefly here. In short, the expectation–maximization approach here consists of the following procedure:
Guess some cluster centers
Repeat until converged
E-Step: assign points to the nearest cluster center
M-Step: set the cluster centers to the mean
Here the “E-step” or “Expectation step” is so-named because it involves updating our expectation of which cluster each point belongs to. The “M-step” or “Maximization step” is so-named because it involves maximizing some fitness function that defines the location of the cluster centers—in this case, that maximization is accomplished by taking a simple mean of the data in each cluster.
The literature about this algorithm is vast, but can be summarized as follows: under typical circumstances, each repetition of the E-step and M-step will always result in a better estimate of the cluster characteristics.
We can visualize the algorithm as shown in the following figure. For the particular initialization shown here, the clusters converge in just three iterations. For an interactive version of this figure, refer to the code in the Appendix.
The k-Means algorithm is simple enough that we can write it in a few lines of code. The following is a very basic implementation:
from sklearn.metrics import pairwise_distances_argmin
def find_clusters(X, n_clusters, rseed=2):
# 1. Randomly choose clusters
rng = np.random.RandomState(rseed)
i = rng.permutation(X.shape[0])[:n_clusters]
centers = X[i]
while True:
# 2a. Assign labels based on closest center
labels = pairwise_distances_argmin(X, centers)
# 2b. Find new centers from means of points
new_centers = np.array([X[labels == i].mean(0)
for i in range(n_clusters)])
# 2c. Check for convergence
if np.all(centers == new_centers):
break
centers = new_centers
return centers, labels
centers, labels = find_clusters(X, 4)
plt.scatter(X[:, 0], X[:, 1], c=labels,
s=50, cmap='viridis');
Most well-tested implementations will do a bit more than this under the hood, but the preceding function gives the gist of the expectation–maximization approach.
Caveats of expectation–maximization¶
There are a few issues to be aware of when using the expectation–maximization algorithm.
The globally optimal result may not be achieved¶
First, although the E–M procedure is guaranteed to improve the result in each step, there is no assurance that it will lead to the global best solution. For example, if we use a different random seed in our simple procedure, the particular starting guesses lead to poor results:
centers, labels = find_clusters(X, 4, rseed=0)
plt.scatter(X[:, 0], X[:, 1], c=labels,
s=50, cmap='viridis');
Here the E–M approach has converged, but has not converged to a globally optimal configuration. For this reason, it is common for the algorithm to be run for multiple starting guesses, as indeed Scikit-Learn does by default (set by the n_init
parameter, which defaults to 10).
The number of clusters must be selected beforehand¶
Another common challenge with k-means is that you must tell it how many clusters you expect: it cannot learn the number of clusters from the data. For example, if we ask the algorithm to identify six clusters, it will happily proceed and find the best six clusters:
labels = KMeans(6, random_state=0).fit_predict(X)
plt.scatter(X[:, 0], X[:, 1], c=labels,
s=50, cmap='viridis');
Whether the result is meaningful is a question that is difficult to answer definitively; one approach that is rather intuitive, but that we won’t discuss further here, is called silhouette analysis.
Alternatively, you might use a more complicated clustering algorithm which has a better quantitative measure of the fitness per number of clusters (e.g., Gaussian mixture models; see In Depth: Gaussian Mixture Models) or which can choose a suitable number of clusters (e.g., DBSCAN, mean-shift, or affinity propagation, all available in the sklearn.cluster
submodule)
k-means is limited to linear cluster boundaries¶
The fundamental model assumptions of k-means (points will be closer to their own cluster center than to others) means that the algorithm will often be ineffective if the clusters have complicated geometries.
In particular, the boundaries between k-means clusters will always be linear, which means that it will fail for more complicated boundaries. Consider the following data, along with the cluster labels found by the typical k-means approach:
from sklearn.datasets import make_moons
X, y = make_moons(200, noise=.05, random_state=0)
labels = KMeans(2, random_state=0).fit_predict(X)
plt.scatter(X[:, 0], X[:, 1], c=labels,
s=50, cmap='viridis');
This situation is reminiscent of the discussion in In-Depth: Support Vector Machines, where we used a kernel transformation to project the data into a higher dimension where a linear separation is possible. We might imagine using the same trick to allow k-means to discover non-linear boundaries.
One version of this kernelized k-means is implemented in Scikit-Learn within the SpectralClustering
estimator.
It uses the graph of nearest neighbors to compute a higher-dimensional representation of the data, and then assigns labels using a k-means algorithm:
from sklearn.cluster import SpectralClustering
model = SpectralClustering(n_clusters=2, affinity='nearest_neighbors',
assign_labels='kmeans')
labels = model.fit_predict(X)
plt.scatter(X[:, 0], X[:, 1], c=labels,
s=50, cmap='viridis');
/usr/local/lib/python3.6/dist-packages/sklearn/manifold/spectral_embedding_.py:235: UserWarning: Graph is not fully connected, spectral embedding may not work as expected. warnings.warn("Graph is not fully connected, spectral embedding"
We see that with this kernel transform approach, the kernelized k-means is able to find the more complicated nonlinear boundaries between clusters.
k-means can be slow for large numbers of samples¶
Because each iteration of k-means must access every point in the dataset, the algorithm can be relatively slow as the number of samples grows.
You might wonder if this requirement to use all data at each iteration can be relaxed; for example, you might just use a subset of the data to update the cluster centers at each step.
This is the idea behind batch-based k-means algorithms, one form of which is implemented in sklearn.cluster.MiniBatchKMeans
.
The interface for this is the same as for standard KMeans
; we will see an example of its use as we continue our discussion.
Examples¶
Being careful about these limitations of the algorithm, we can use k-means to our advantage in a wide variety of situations. We’ll now take a look at a couple examples.
Example 1: k-means on digits¶
To start, let’s take a look at applying k-means on the same simple digits data that we saw in In-Depth: Decision Trees and Random Forests and In Depth: Principal Component Analysis. Here we will attempt to use k-means to try to identify similar digits without using the original label information; this might be similar to a first step in extracting meaning from a new dataset about which you don’t have any a priori label information.
We will start by loading the digits and then finding the KMeans
clusters.
Recall that the digits consist of 1,797 samples with 64 features, where each of the 64 features is the brightness of one pixel in an 8×8 image:
from sklearn.datasets import load_digits
digits = load_digits()
digits.data.shape
(1797, 64)
The clustering can be performed as we did before:
kmeans = KMeans(n_clusters=10, random_state=0)
clusters = kmeans.fit_predict(digits.data)
kmeans.cluster_centers_.shape
(10, 64)
The result is 10 clusters in 64 dimensions. Notice that the cluster centers themselves are 64-dimensional points, and can themselves be interpreted as the “typical” digit within the cluster. Let’s see what these cluster centers look like:
fig, ax = plt.subplots(2, 5, figsize=(8, 3))
centers = kmeans.cluster_centers_.reshape(10, 8, 8)
for axi, center in zip(ax.flat, centers):
axi.set(xticks=[], yticks=[])
axi.imshow(center, interpolation='nearest', cmap=plt.cm.binary)
We see that even without the labels, KMeans
is able to find clusters whose centers are recognizable digits, with perhaps the exception of 1 and 8.
Because k-means knows nothing about the identity of the cluster, the 0–9 labels may be permuted. We can fix this by matching each learned cluster label with the true labels found in them:
from scipy.stats import mode
labels = np.zeros_like(clusters)
for i in range(10):
mask = (clusters == i)
labels[mask] = mode(digits.target[mask])[0]
Now we can check how accurate our unsupervised clustering was in finding similar digits within the data:
from sklearn.metrics import accuracy_score
accuracy_score(digits.target, labels)
0.7935447968836951
With just a simple k-means algorithm, we discovered the correct grouping for 80% of the input digits! Let’s check the confusion matrix for this:
from sklearn.metrics import confusion_matrix
mat = confusion_matrix(digits.target, labels)
sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,
xticklabels=digits.target_names,
yticklabels=digits.target_names)
plt.xlabel('true label')
plt.ylabel('predicted label');
As we might expect from the cluster centers we visualized before, the main point of confusion is between the eights and ones. But this still shows that using k-means, we can essentially build a digit classifier without reference to any known labels!
Just for fun, let’s try to push this even farther. We can use the t-distributed stochastic neighbor embedding (t-SNE) algorithm (mentioned in In-Depth: Manifold Learning) to pre-process the data before performing k-means. t-SNE is a nonlinear embedding algorithm that is particularly adept at preserving points within clusters. Let’s see how it does:
from sklearn.manifold import TSNE
# Project the data: this step will take several seconds
tsne = TSNE(n_components=2, init='random', random_state=0)
digits_proj = tsne.fit_transform(digits.data)
# Compute the clusters
kmeans = KMeans(n_clusters=10, random_state=0)
clusters = kmeans.fit_predict(digits_proj)
# Permute the labels
labels = np.zeros_like(clusters)
for i in range(10):
mask = (clusters == i)
labels[mask] = mode(digits.target[mask])[0]
# Compute the accuracy
accuracy_score(digits.target, labels)
0.9326655537006121
That’s nearly 92% classification accuracy without using the labels. This is the power of unsupervised learning when used carefully: it can extract information from the dataset that it might be difficult to do by hand or by eye.
Example 2: k-means for color compression¶
One interesting application of clustering is in color compression within images. For example, imagine you have an image with millions of colors. In most images, a large number of the colors will be unused, and many of the pixels in the image will have similar or even identical colors.
For example, consider the image shown in the following figure, which is from the Scikit-Learn datasets
module (for this to work, you’ll have to have the pillow
Python package installed).
# Note: this requires the ``pillow`` package to be installed
from sklearn.datasets import load_sample_image
china = load_sample_image("china.jpg")
ax = plt.axes(xticks=[], yticks=[])
ax.imshow(china);
The image itself is stored in a three-dimensional array of size (height, width, RGB)
, containing red/blue/green contributions as integers from 0 to 255:
china.shape
(427, 640, 3)
One way we can view this set of pixels is as a cloud of points in a three-dimensional color space.
We will reshape the data to [n_samples x n_features]
, and rescale the colors so that they lie between 0 and 1:
data = china / 255.0 # use 0...1 scale
data = data.reshape(427 * 640, 3)
data.shape
(273280, 3)
We can visualize these pixels in this color space, using a subset of 10,000 pixels for efficiency:
def plot_pixels(data, title, colors=None, N=10000):
if colors is None:
colors = data
# choose a random subset
rng = np.random.RandomState(0)
i = rng.permutation(data.shape[0])[:N]
colors = colors[i]
R, G, B = data[i].T
fig, ax = plt.subplots(1, 2, figsize=(16, 6))
ax[0].scatter(R, G, color=colors, marker='.')
ax[0].set(xlabel='Red', ylabel='Green', xlim=(0, 1), ylim=(0, 1))
ax[1].scatter(R, B, color=colors, marker='.')
ax[1].set(xlabel='Red', ylabel='Blue', xlim=(0, 1), ylim=(0, 1))
fig.suptitle(title, size=20);
plot_pixels(data, title='Input color space: 16 million possible colors')
Now let’s reduce these 16 million colors to just 16 colors, using a k-means clustering across the pixel space. Because we are dealing with a very large dataset, we will use the mini batch k-means, which operates on subsets of the data to compute the result much more quickly than the standard k-means algorithm:
import warnings; warnings.simplefilter('ignore') # Fix NumPy issues.
from sklearn.cluster import MiniBatchKMeans
kmeans = MiniBatchKMeans(16)
kmeans.fit(data)
new_colors = kmeans.cluster_centers_[kmeans.predict(data)]
plot_pixels(data, colors=new_colors,
title="Reduced color space: 16 colors")
The result is a re-coloring of the original pixels, where each pixel is assigned the color of its closest cluster center. Plotting these new colors in the image space rather than the pixel space shows us the effect of this:
china_recolored = new_colors.reshape(china.shape)
fig, ax = plt.subplots(1, 2, figsize=(16, 6),
subplot_kw=dict(xticks=[], yticks=[]))
fig.subplots_adjust(wspace=0.05)
ax[0].imshow(china)
ax[0].set_title('Original Image', size=16)
ax[1].imshow(china_recolored)
ax[1].set_title('16-color Image', size=16);
Some detail is certainly lost in the rightmost panel, but the overall image is still easily recognizable. This image on the right achieves a compression factor of around 1 million! While this is an interesting application of k-means, there are certainly better way to compress information in images. But the example shows the power of thinking outside of the box with unsupervised methods like k-means.
< In-Depth: Manifold Learning | Contents | In Depth: Gaussian Mixture Models >


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China says plague outbreak risk minimal after two new cases in Beijing
SHANGHAI (Reuters) - Chinese health officials say the risk of an outbreak of pneumonic plague is minimal after two new cases were confirmed this week in Beijing, the official China Daily said on Thursday.
Two patients from Inner Mongolia were quarantined after being diagnosed with the highly infectious and often fatal disease, health authorities in the Chaoyang district of the capital said on Tuesday.
State news agency Xinhua said one of the two was in critical condition while the other was stable. Infected in Inner Mongolia, they were sent by ambulance to a medical institution in Beijing.
The Chinese Center for Disease Control and Prevention said all those at risk of exposure to plague had been traced and treated, and hospitals had stepped up monitoring of those with similar symptoms.
Pneumonic plague is a lung disease spread mostly by rats, with death rates of close to 100% in humans if left untreated, Beijing health authorities said in a notice.
Outbreaks in China have been rare, but large parts of the northwestern city of Yumen were sealed off in 2014 after a 38-year-old resident died of bubonic plague, known as “Black Death” in the Middle Ages and caused by the same bacterium as the pneumonic variant.
Rodent populations have risen in Inner Mongolia after persistent droughts, worsened by climate change. An area the size of the Netherlands was hit by a “rat plague” last summer, causing damages of 600 million yuan ($86 million), Xinhua said.


Education
Udacity NanoDegree
Udacity
Sep 2016 – Dec 2016 Duration: 4 months
- Used Bootstrap to create responsive layouts that displays clean UI on all devices
- Learned latest technologies in web development to create stunning user experiences
- Used Jasmine (JavaScript Testing) to test and maintain functionality of websites
- Learned and used high-demand frameworks like jQuery and Angular as well as using AJAX, API, and JSON to develop web applications
Masters of Technology
SRM University
Jul 2014 – May 2016 Duration: 2 years
- Major: Communication Systems
- Designed and Fabricated an Antenna to improve the ARBW (axial ratio bandwidth) and to obtain reconfigurable property by the use of metasurface.
- Metasurface is two dimension version of a meta-material that are used for polarization converter, scatter, etc
- Published a paper in Indian Journel of Science and Technology
Bachelors of Technology
Manakula Vinayagar Institute of Technology
Jul 2010 – May 2014 Duration: 4 years
- Major: Electronics and Communication Engineering
- Designed a tool to Classify Hyperspectral images
- Participated in National Conference of Computer Science and presented a paper regarding healing of Node failure
- Presented an idea of translating telephone conversation in PC-Expo conducted by ICT Academy
Pre-Degree
St. Joseph of Cluny
Jun 2009 – May 2010 Duration: 1 years
- Major: Biology Group
- Learnt Extracuricular Activites like March Past, ThrowBall, Yoga


Package net.sf.jasperreports.engine
Interface JRSection
-
- All Superinterfaces:
java.lang.Cloneable
,JRCloneable
- All Known Implementing Classes:
JRBaseSection
,JRDesignSection
,JRFillSection
public interface JRSection extends JRCloneable
Implementations of this interface represent various sections in the report template.Report Sections
JasperReports works with templates that are structured into multiple sections, like any traditional reporting tool. A report can contain the following sections: background, title, summary, page header, page footer, last page footer, column header and column footer. For each group defined in the report, there is a corresponding group header section and group footer section. At report-filling time, the engine iterates through the virtual records of the supplied report data source and renders each report section when appropriate, depending on each section's defined behavior. For instance, the detail section is rendered for each record in the data source. When page breaks occur, the page header and page footer sections are rendered as needed. Sections are made of one or more bands. Bands are portions of the report template that have a specified height and width and can contain report elements like lines, rectangles, images, and text fields. These bands are filled repeatedly at report-generating time and make up the final document. The entire structure of the report template is based on the following sections:<title>
,<pageHeader>
,<columnHeader>
,<groupHeader>
,<detail>
,<groupFooter>
,<columnFooter>
,<pageFooter>
,<lastPageFooter>
,<summary>
, <background> and <noData>. All report sections are optional, but of course all useful templates must have at least one such section.Title
This is the first section of the report. It is generated only once during the report-filling process and represents the beginning of the resulting document. The title section precedes even the page header section. To print the page header before the title section, put the elements on the page header at the beginning of the title section as well. One can suppress the actual page header on the first page using the<printWhenExpression>
, based on thePAGE_NUMBER
report variable. The title section can be followed by a page break if theisTitleNewPage
attribute at report level is set to true.Page Header
This section appears at the top of each page in the generated document.Column Header
This section appears at the top of each column in the generated document.Detail
For each record in the data source, the engine tries to generate this section. The detail section can be made of multiple bands.Column Footer
This section appears at the bottom of each column in the generated document. It never stretches downward to acquire the content of its containing text fields. Its rendering position is controlled by theisFloatColumnFooter
flag declared at report template level.Page Footer
This section appears at the bottom of each page in the generated document. Just like the column footer section, the page footer never stretches downwards to acquire the content of its containing text fields and always retains the declared fixed height.Summary
This section is generated only once per report and appears at the end of the generated document, but is not necessarily the last section generated. This is because in some cases the column footer and/or page footer of the last page follows it. One can have the summary section start a new page of its own by setting theisSummaryNewPage
attribute to true at report template level. Even if this attribute remains false, the summary section always starts a new page if it does not fit on the remaining space of the last page, or if the report has more than one column and it has already started a second column on the last page.Last Page Footer
If present, this section replaces the normal page footer section, but only on the last occurrence of the page footer, which might not be the last page if the summary is present and it overflows on multiple pages or it is rendered alone on its own last page. So it behaves more like the last page footer than the footer of the last page.Background
This is a special section that is rendered on all pages and its content placed underneath all other report sections. Normal report sections are rendered one after the other, but the background section does not interfere with the other report sections and can be used to achieve watermark effects or to create the same background for all pages.No Data
This is another special section that is generated only once per report and, under certain conditions, its content will replace all the ordinary report's content. Sometimes when the report data source is empty, is very useful to generate an equivalent content, a notice, maybe, or an image, in order to replace all the empty zone. Especially in the case of subreports, more useful is to replace all the subreport's content with an equivalent one. If the<noData>
section is defined in the report template, and if the data source is empty, then the<noData>
section will be the only one taken into account at fill time, and its content will produce the report output.Group Header
This section marks the start of a new group in the resulting document. It is inserted in the document every time the value of the group expression changes during the iteration through the data source. The group header section is a multi-band section.Group Footer
Every time a report group changes, the engine adds the corresponding group footer section before starting the new group or when the report ends. The group footer section is also a multi-band section.- Author:
- Teodor Danciu<EMAIL_ADDRESS>- See Also:
JRBand
-
-
Method Summary
All Methods Instance Methods Abstract Methods Modifier and Type Method Description JRBand[]
getBands()
Returns the bands within the current section.JRPart[]
getParts()
Returns the parts within the current section.-
Methods inherited from interface net.sf.jasperreports.engine.JRCloneable
clone
-
-


UTC +1. Summer time UTC +2 April to September.
Voltage: 240 V
Frequency: 50 Hz
The Gibraltar pound is (suppose to be) equal to the pound sterling, (which can also be used).
ATMs dispense either Gib pounds (NatWest) or pound sterling (Barclays). None dispense euros.
Euros are accepted in many shops.
No VAT is charged on goods here.
International Dialing Code for Gibraltar is +350.
Mobile phone - Call your cell phone provider to make sure that they offer phone coverage in Gibraltar. Ask to be upgraded to international roaming.
Consider renting a cell phone or SIM card to use with your personal cell phone if your cellular company does not offer coverage in Gibraltar. rangeRoamer provides short-term cell phone service for international travelers. Buy, Rent, or use your own cell phone.
Foreign Embassies in Gibraltar
United States Embassy, London, United Kingdom (also responsible for Gibraltar): +44 (0)20 7499 9000.
Canadian High Commission, London, United Kingdom (also responsible for Gibraltar): +44 (0)207 258 6600.
Australian High Commission, London, United Kingdom (also responsible for Gibraltar): +44 (0)20 7379 4334.
South African High Commission, London, United Kingdom (also responsible for Gibraltar): +44 (0)20 7451 7299.
Irish Embassy, London, United Kingdom (also responsible for Gibraltar): +44 (0)20 7235 2171.
New Zealand High Commission, London, United Kingdom (also responsible for Gibraltar): +44 (0)20 7930 8422.
Police/ambulance - Dial 199 / 112 (mobiles)
Fire - Dial 190
Royal Gibraltar Police - Dial 72500
Imray and Adlard Coles Pilot Books are available at a discounted price for Noonsite.com users via World Cruising Club
Mediterranean Almanac 2015/16
By Rod and Lucinda Heikell (9th Edition)
Publisher Imray (December 2014)
2016 supplement available from Imray
ISBN -<PHONE_NUMBER>986
Price - £29.50
See this Noonsite review.
North Africa: Morocco, Algeria, Tunisia, Malta and Libya
An RCC Pilotage Publication by Graham Hutt
Published by Imray (revised 4th Edn, Nov 2010)
ISBN:<PHONE_NUMBER>817
Straits Sailing Handbook 2014
by Pillars of Hercules Sailing
Published by Pillars of Hercules
Stock code RB0541-2
An essential cruising companion when sailing through the Straits of Gibraltar, providing vital local knowledge to visitors. Information such as port formalities, customs regulations, marine services and shore based facilities is included, as well as surface flow charts, tactics and tide tables for Gibraltar, Lisbon and Cadiz.
Yacht Scene Sailors Guide - Gibraltar
By D. M Sloma
Published by Yacht Scene Publications
See www.yachtscene.gi
The Cruising Almanac 2015
By Cruising Association and Imray
Publisher Imray (2015 Edition)
ISBN<PHONE_NUMBER>771
Covers North West Europe from Shetland and southern Norway to Gibraltar and West Ireland to the Baltic. Fully revised and additional plans. Available as Imray Nautical Apps for iPad users. Free updates approximately every two months
Noonsite welcomes information and updates especially regarding clearance, customs and immigration procedures from cruisers visiting this country.
Please E-mail noonsite with any new information, updates or corrections. Even just a short email confirming that the current data is accurate would be most helpful.


publication date: 12.10.2020
The model Mariah Idrissi shot to fame in a campaign for the high-street chain H&M in 2015 – in which she wore a printed headscarf (which looks very similar to a traditional keffiyeh) and a loose salmon-pink coat over a monochrome ensemble. She points out that many successful modest influencers have started dressing in an increasingly westernised way in recent years, which to her, gives the mainstream fashion industry the impression that these successful, high-profile women reflect the way all “fashionable” Muslim women may want to dress. Read more...


| Tag Summary |
| choose |
Simple conditional tag that establishes a context for
mutually exclusive conditional operations, marked by
<when> and <otherwise>
|
| out |
Like <%= ... >, but for XPath expressions.
|
| if |
XML conditional tag, which evalutes its body if the
supplied XPath expression evalutes to 'true' as a boolean
|
| forEach |
XML iteration tag.
|
| otherwise |
Subtag of <choose> that follows <when> tags
and runs only if all of the prior conditions evaluated to
'false'
|
| param |
Adds a parameter to a containing 'transform' tag's Transformer
|
| parse |
Parses XML content from 'source' attribute or 'body'
|
| set |
Saves the result of an XPath expression evaluation in a 'scope'
|
| transform |
Conducts a transformation given a source XML document
and an XSLT stylesheet
|
| when |
Subtag of <choose> that includes its body if its
expression evalutes to 'true'
|


- Comments
- New Article
- Blog
Article Table
| Id | Title | Date Created | Manipulate |
|---|---|---|---|
| ed234sfff345 | Kick off grid systems | 20-09-19 | |
| ed234sfff345 | Kick off grid systems | 20-09-19 |
| Id | Title | Date Created | Manipulate |
|---|---|---|---|
| ed234sfff345 | Kick off grid systems | 20-09-19 | |
| ed234sfff345 | Kick off grid systems | 20-09-19 |


SSH is not part of the game. SSH is just how you access the gameserver. The account you will log into is `agent` and the password is `password`.
For the best experience, connect to<EMAIL_ADDRESS>using a SSH client. If you're on Windows, try PuTTY, Linux try gnome-terminal, OSX try Terminal. This is not a requirement, I have provided a web-based terminal emulator, all you have to do to use it is visit http://ld36.xtoast.com. The web-based terminal emulator is fine for playing the game with a few caveats. There is some input lag, images may be difficult to see because of the bright theme, and copy-pasting the hex content takes some getting used to.
If you have difficulties copy-pasting, try the following. Do not use Ctrl+C or Ctrl+V. Highlight the text you wish to copy. Right-click using your mouse, then from the context menu, click copy or paste.
I recommend keeping a notepad with all the information you find. There is a pile of data to go through, and it will help if you compile all the data into one place. Think like a detective, and find out everything there is to know about each of the in-game characters. Poke around, exploring any phone numbers, user accounts, or e-mail inboxes you can find.
As this is a ludemdare game made in under 48 hours, I built in little to no security. If you know Linux, you could easily exit the game inside your SSH session, then browse all the game's source code and assets, leading you to the answers to the mystery. That's an easy way to win, but it's not the challenging or intended way to play. The intended way to play is to only use the SSH connection to access the DECTILE remote access client, or if you know Linux, `./game.sh`.
Using the DECTILE remote access client, you will find clues that lead you to websites and other away-from-keyboard information. In this way, Agents of DECTILE bleeds out into the "real world."


API
GET - /move/{pin}/{pos}
- pin - 0 to 7, the pin it's plugged in on the SSC.
- pos - 0 to 255. 0 Full CCW, 127 - stopped, 255 Full CW.
GET - /nav/{left_motor}/{right_motor}
- left_motor - 0 to 255. 0 Full CCW, 127 - stopped, 255 Full CW.
- right_motor - 0 to 255. 0 Full CCW, 127 - stopped, 255 Full CW.


--- layout: page title: Tech Bio ---
I love learning new things! So one day I decided to learn how to code and now I'm a Full Stack Engineer specializing in Javascript. Particularly NodeJs and React.
I've worked for two startups so far in my career and really love working in a small team with a big mission.
NearSt is a startup dedicated to getting people back to the high street. They aim to make it faster and easier for shoppers to buy from high street stores than order online.
I worked for NearSt as a full stack engineer from Nov 2016 until Jan 2018.
During this time I:
LOVESPACE is the UKs largest by-the-box storage startup aiming to disrupt the self-storage industry.
I worked for LOVESPACE as an engineer in 2015.
During this time I:
Also in 2015, I cofounded The News Explained with Tom McGivan. TNE is a collection of articles to help the general public understand the complex subjects of the modern world. This combined my love for current affairs (I studied Philosophy, Politics and Economics at University) with Web Design and Development.
This project really shows off my web design skills. But it also required some programming in PHP and creation of a wordpress theme.
Tzundoku was an idea I had at university to create a website where users could create lists of their favourite things.
For example: "Top 10 Matt Damon Films" or "Top 10 Barbers in London".
The code for this project can be found on github here
Project Anmo is a fun project I developed with a friend to learn Ruby on Rails. It started as a simple todo list. But then we added an algorithm in C (for performance) to "auto generate" the list.
The code for this project is also on github here.
Contracted to build a website for Manx Biodiversity in 2015.
julyandavey.com is built by me using Ruby and a static site generator called jekyll.


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Traveling to Charlotte
Airport: Charlotte Douglas International Airport (CLT)
Charlotte is American Airlines' largest hub on the East coast, so it's usually easy to find American flights from almost any city to Charlotte. CLT is also served by JetBlue, Southwest, United, Delta, and other airlines.
Getting Around
The hotels below and the wedding venue are all within a 30 minute drive from the Charlotte airport. Because it's economical and convenient for the area, we recommend you rent a car, but please let us know if you need alternate transportation arrangements.
Other convenient transportation options include the LYNX Light Rail (Hotels in Pineville below are closest to a light rail stop), which serves the South End and Uptown areas, as well as Charlotte's bus system, CATS.
Where to Stay
We've booked blocks of rooms at four different hotels. Two of them are very close to the wedding venue itself, and two are somewhere between the venue and the rehearsal dinner location. If you plan to attend both, one of the in-between hotels may be more convenient.
If you book one of these, mention the Harward & Morris wedding for the best rate.
| Hotel | Location (Map) | Phone | Notes |
|---|---|---|---|
|
TownePlace Suites (block booking link) |
2135 Tabor Drive, Rock Hill, SC |<PHONE_NUMBER> | Close to venue |
|
Hampton Inn (block booking link) |
111 Tabor Drive, Rock Hill, SC |<PHONE_NUMBER> | Close to venue |
|
Comfort Suites (call/ask for Morris/Harward block) |
10415 Centrum Parkway, Pineville, NC |<PHONE_NUMBER> | Between venue and rehearsal |
|
Springhill Suites (block booking link) |
12325 Johnston Road, Charlotte, NC |<PHONE_NUMBER> | Between venue and rehearsal |
Other Hotels
The following hotels are also conveniently located and have good rates and reviews, although we were not able to reserve blocks of rooms. You can still book them if you prefer:
- Four Points by Sheraton Pineville, NC (20% off if you mention Harward/Morris wedding)
- Homewood Suites Ballantyne Charlotte, NC
- Aloft Hotel Ballantyne Charlotte, NC
- Hilton Garden Inn Rock Hill, SC


ZIP: 302 Title: Standardized Memo Field Format Owners: Jack Grigg<EMAIL_ADDRESS>Original-Authors: Jay Graber Status: Draft Category: Standards / RPC / Wallet Created: 2017-02-08 Discussions-To: <https://github.com/zcash/zips/issues/366> Pull-Request: <https://github.com/zcash/zips/pull/105>
This ZIP describes a proposed specification for a standardized format for clients who wish to transmit or receive content within the encrypted memo field of shielded transactions.
A well-defined standard for formatting content within the encrypted memo field will help expand its use within the Zcash ecosystem by providing a commonly recognized format for memo values carrying different types of data. Users and third-party services benefit from standardized formatting rules that define the type and length of the data contained within.
Section 5.5 of the Zcash protocol specification 1 defines three cases for the encoding of a memo field:
0xF6
followed by 511 0x00
bytes, indicating "no memo"; or0xF5
or greater (which is therefore not a valid UTF-8 string), as specified in ZIP 302.This ZIP refines the specification of the third case.
The following specification constrains a party, called the "reader", that interprets the contents of a memo. It does not define consensus requirements.
The new protocol specification is an improvement over the current memo field content specification that was in the protocol spec up to version 2020.1.0, which stated:
The usage of the memo field is by agreement between the sender and recipient of the note. The memo field SHOULD be encoded either as:
- a UTF-8 human-readable string [Unicode], padded by appending zero bytes; or
- an arbitrary sequence of 512 bytes starting with a byte value of 0xF5 or greater, which is therefore not a valid UTF-8 string.
In the former case, wallet software is expected to strip any trailing zero bytes and then display the resulting UTF-8 string to the recipient user, where applicable. Incorrect UTF-8-encoded byte sequences should be displayed as replacement characters (U+FFFD).
In the latter case, the contents of the memo field SHOULD NOT be displayed. A start byte of 0xF5 is reserved for use by automated software by private agreement. A start byte of 0xF6 or greater is reserved for use in future Zcash protocol extensions.
See issue #1849 for further discussion.
Encrypted memo field contents sent without the standardized format proposed here will be interpreted according to the specification set out in older versions of the protocol spec.
| 1 | Zcash Protocol Specification, Version 2021.1.19 |
|---|
| 2 | UTF-8, a transformation format of ISO 10646 |
|---|
| 3 | Variable length integer. Bitcoin Wiki |
|---|


32
Elihu Rebukes Job's Three Friends
1 So these three men ceased to answer Job, because he was righteous in his own eyes.
2 Then Elihu the son of Barachel the Buzite, of the family of Ram, burned with anger. He burned with anger at Job because he justified himself rather than God.
3 He burned with anger also at Job's three friends because they had found no answer, although they had declared Job to be in the wrong.
4 Now Elihu had waited to speak to Job because they were older than he.
5 And when Elihu saw that there was no answer in the mouth of these three men, he burned with anger.
6 And Elihu the son of Barachel the Buzite answered and said:
“I am young in years,
and you are aged;
therefore I was timid and afraid
to declare my opinion to you.
7 I said, ‘Let days speak,
and many years teach wisdom.’
8 But it is the spirit in man,
the breath of the Almighty, that makes him understand.
9 It is not the old who are wise,
nor the aged who understand what is right.
10 Therefore I say, ‘Listen to me;
let me also declare my opinion.’
11 “Behold, I waited for your words,
I listened for your wise sayings,
while you searched out what to say.
12 I gave you my attention,
and, behold, there was none among you who refuted Job
or who answered his words.
13 Beware lest you say, ‘We have found wisdom;
God may vanquish him, not a man.’
14 He has not directed his words against me,
and I will not answer him with your speeches.
15 “They are dismayed; they answer no more;
they have not a word to say.
16 And shall I wait, because they do not speak,
because they stand there, and answer no more?
17 I also will answer with my share;
I also will declare my opinion.
18 For I am full of words;
the spirit within me constrains me.
19 Behold, my belly is like wine that has no vent;
like new wineskins ready to burst.
20 I must speak, that I may find relief;
I must open my lips and answer.
21 I will not show partiality to any man
or use flattery toward any person.
22 For I do not know how to flatter,
else my Maker would soon take me away.


--- layout: none title: "ACS 2019 | Nanocar Builder" permalink: /presentations/acs-2019/ date: 2019-04-03 categories: presentations ---
Kinesin
RNA Polymerase
Macroscopic Machines
Molecular Machines
Shirai, Yasuhiro, et al. Nano Letters 5.11 (2005): 2330-2334.
Kudernac, Tibor, et al. Nature 479.7372 (2011): 208.
Chiang, Pinn-Tsong, et al. ACS Nano 6.1 (2011): 592-597.
Body Parts - Wheels / Chassis
Nanocar Assembly
Wheels
Chassis
Coming soon


utmps
Software
skarnet.org
What has changed in utmps
in <IP_ADDRESS>
- skalibs
dependency bumped to <IP_ADDRESS>.
- s6 run-time
dependency bumped to <IP_ADDRESS>.
in <IP_ADDRESS>
- skalibs
dependency bumped to <IP_ADDRESS>.
- s6 run-time
dependency bumped to <IP_ADDRESS>.
in <IP_ADDRESS>
- skalibs
dependency bumped to <IP_ADDRESS>.
- s6 run-time
dependency bumped to <IP_ADDRESS>.
in <IP_ADDRESS>
- skalibs
dependency bumped to <IP_ADDRESS>.
- s6 run-time
dependency bumped to <IP_ADDRESS>.
in <IP_ADDRESS>
- WTMP_FILE is now /var/log/wtmp, to ease integration
with distributions. (Unlike utmp, wtmp is supposed to be persistent.)
in <IP_ADDRESS>
- skalibs
dependency bumped to <IP_ADDRESS>.
- s6 run-time
dependency bumped to <IP_ADDRESS>.
in <IP_ADDRESS>
- skalibs
dependency bumped to <IP_ADDRESS>.
- s6 run-time
dependency bumped to <IP_ADDRESS>.
- The utmpx.h header file is
not installed by default anymore. You now need to explicitly give the
--enable-libc-includes configure option in order to install it. The
point of this change is to avoid overwriting libc headers accidentally.
in <IP_ADDRESS>
- skalibs
dependency bumped to <IP_ADDRESS>.
- s6 run-time
dependency bumped to <IP_ADDRESS>.
in <IP_ADDRESS>
- skalibs
dependency bumped to <IP_ADDRESS>.
- s6 run-time
dependency bumped to <IP_ADDRESS>.
in <IP_ADDRESS>
- skalibs
dependency bumped to <IP_ADDRESS>.
- s6 run-time
dependency bumped to <IP_ADDRESS>.
in <IP_ADDRESS>
in <IP_ADDRESS>
- skalibs
dependency bumped to <IP_ADDRESS>.
- s6 run-time
dependency bumped to <IP_ADDRESS>.
- Everything now builds as PIC by default no matter
the toolchain's settings. Use the --disable-all-pic configure
option to build executables and static libraries as non-PIC.
in <IP_ADDRESS>
- Members of the utmp group (if utmp is the name
of the group the utmps-utmpd daemon runs as)
can now use pututxline() to write to the utmp database.
in <IP_ADDRESS>
- skalibs
dependency bumped to <IP_ADDRESS>.
- Optional nsss support added.
in <IP_ADDRESS>
in <IP_ADDRESS>
in <IP_ADDRESS>
Initial release.


{% load static %}
Document
News
(current)
Spare parts
Machines
Issues
Statistics
{% block content %} {% endblock %}


Installation¶
Getting Airflow¶
The easiest way to install the latest stable version of Airflow is with pip
:
pip install airflow
You can also install Airflow with support for extra features like s3
or postgres
:
pip install "airflow[s3, postgres]"
Extra Packages¶
The airflow
PyPI basic package only installs what’s needed to get started.
Subpackages can be installed depending on what will be useful in your
environment. For instance, if you don’t need connectivity with Postgres,
you won’t have to go through the trouble of installing the postgres-devel
yum package, or whatever equivalent applies on the distribution you are using.
Behind the scenes, Airflow does conditional imports of operators that require these extra dependencies.
Here’s the list of the subpackages and what they enable:
| subpackage | install command | enables |
|---|---|---|
| all | pip install airflow[all] |
All Airflow features known to man |
| all_dbs | pip install airflow[all_dbs] |
All databases integrations |
| async | pip install airflow[async] |
Async worker classes for gunicorn |
| devel | pip install airflow[devel] |
Minimum dev tools requirements |
| devel_hadoop | pip install airflow[devel_hadoop] |
Airflow + dependencies on the Hadoop stack |
| celery | pip install airflow[celery] |
CeleryExecutor |
| crypto | pip install airflow[crypto] |
Encrypt connection passwords in metadata db |
| druid | pip install airflow[druid] |
Druid.io related operators & hooks |
| gcp_api | pip install airflow[gcp_api] |
Google Cloud Platform hooks and operators
(using google-api-python-client ) |
| jdbc | pip install airflow[jdbc] |
JDBC hooks and operators |
| hdfs | pip install airflow[hdfs] |
HDFS hooks and operators |
| hive | pip install airflow[hive] |
All Hive related operators |
| kerberos | pip install airflow[kerberos] |
kerberos integration for kerberized hadoop |
| ldap | pip install airflow[ldap] |
ldap authentication for users |
| mssql | pip install airflow[mssql] |
Microsoft SQL operators and hook, support as an Airflow backend |
| mysql | pip install airflow[mysql] |
MySQL operators and hook, support as an Airflow backend |
| password | pip install airflow[password] |
Password Authentication for users |
| postgres | pip install airflow[postgres] |
Postgres operators and hook, support as an Airflow backend |
| qds | pip install airflow[qds] |
Enable QDS (qubole data services) support |
| rabbitmq | pip install airflow[rabbitmq] |
Rabbitmq support as a Celery backend |
| s3 | pip install airflow[s3] |
S3KeySensor , S3PrefixSensor |
| samba | pip install airflow[samba] |
Hive2SambaOperator |
| slack | pip install airflow[slack] |
SlackAPIPostOperator |
| vertica | pip install airflow[vertica] |
Vertica hook support as an Airflow backend |
| cloudant | pip install airflow[cloudant] |
Cloudant hook |


automatic relaunch of iTunes
-
kkay
Hello,
The problem I'm having with Synergy is that after I quit the iTunes application, it is automatically relaunched after a few seconds. If I turn off Synergy and try to quit iTunes, the auto relaunch does not happen. So I'm pretty sure it's Synergy, not iTunes; besides, this bug started to occur right after I upgraded for 3.1. I am a purchaed user of the program and using a few hot keys to play/pause and skip tracks. Currently I'm using an intel-based mac book.
Thanks,
-
endless
I am also experiencing this issue.
MacBook C2D OS X 10.4.8 iTunes 7.0.2
-
Greg Hurrell
Are you running any other program that in any way communicates with or complements iTunes?
-
endless
No, I am not. The only other thing running even remotely related to iTunes or Synergy is Growl.
-
Greg Hurrell
To isolate the cause of the problem I'd recommend following the basic troubleshooting steps outlined here:
-
kkay
Nope. I've tried pretty much everything from the troubleshooting, but nothing's working.
Now I'm more than certain that my issue is caused by the lastest version of Synergy. Do you have a link I can download the previous version? I had had absolutely no problem with the product until I downloaded the 3.1. This bug is starting to get very annoying. Not to mention it took a very long time to finally get a response from somebody. This IS a great support.
-
Greg Hurrell
So I take it you've been able to reproduce the problem after trashing your preferences? Trying from another user account etc? If so, it really is extremely surprising.
The previous version is no longer on the server but if you contact me privately with your email address I can email you a copy. I really don't think it should make a difference though.
In terms of getting a response to support queries, you can always open a support ticket (in the navigation bar above, "Support" -> "Support tickets"). The usual response time for such tickets is within a few hours, almost always within 24 hours, and in extreme cases (for example, if I am away for the weekend) at most a couple of days. I do read these forums but this thread must have slipped under my radar.
-
tinsun
I'm not sure if this is related, but I've noticed the same thing: iTunes is relaunching a minute after closing it, but it only happens one time. I hadn't begun to look into it when I stumbled upon this post, looking for something else. At any rate, I noticed these lines in the console log almost exactly one minute after closing iTunes, and seeing it start again:
2007-03-05 16:30:04.138 Synergy[5951] Audioscrobbler: Submission timer fired; will double-check that current track position matches expected value 2007-03-05 16:30:13.400 Synergy[5951] Audioscrobbler: Position does not match (user must have skipped); will not submit to Audioscrobbler
I'll see if that occurs next time it happens as well.
Reply
This topic is now closed.


Calculate the probability of a collision. Formula Used: \(1 - \frac{t!}{(t-n)! (t^n)}\) where \(t\) is the table size and \(n\) is the number of records inserted.


Pistachio Pinwheels, Boston Cream delights, Chocolate confetti, Marshmallow Mayhems, French twists, Bulls Horns


Nigerians React To Viral Queen’s College ‘Eyelashes’ Video
by Emmanuel EgobiambuThere were mixed reactions over the viral video of a mother of a Queen’s College student attacking security guards of the school over the alleged abuse of her daughter.
Concise News understands that the guards refused her daughter into the school because she fixed artificial eyelashes and was wearing nail polish.
In the viral video, the yet-to-be-identified woman also held the shirt of one of the security guards for turning back her daughter and calling her inappropriate names.
The development has sparked off diverse reactions from Nigerians who took to Twitter apportioning blames.
Some of them claimed that the school was wrong to have handled the situation like that, saying the student is still young.
According to them, what the student needed was to be disciplined by the school authority.
However, there were those that backed the action of the Queen’s College, saying the school is only protecting its standard and inculcating discipline.
They noted that the mother of the student should not have confronted the security guards who were merely doing their job.
Here are some of the reactions to the viral video below:


meta(name="viewport" content="width=device-width, initial-scale=1")
My Handhelds page includes specific information about the handhelds (graphing calculators, etc.) we have been using. For general technology tools, refer to the table below. Click the images in the table below to visit the technology's Tech-In-Action page. Click the technology's name to visit its home page. "Tech-In-Action" pages include descriptions, pictures, or demonstrations.
| Classroom Tools | |||
|---|---|---|---|
| Device | Description | Applications in Content Area | Goodies |
| An interactive graph plotting application. Create and import data, view in tabular form, and create dynamic plots and charts that change based on data and parameters. | statistics; data analysis | comprehensive resources | |
| A famous Microsoft product for spreadsheets and databases. | spreadsheets; data analysis; trends and patterns; functions; graphs | ||
| Uses sonar to measure distances, velocities, and accelerations of objects. | kinematics; polynomial functions; rate of change | ||
| A robust dynamic geometry application. Create geometric and graphical figures dependent on changing parameters, generalize phenomena and prove conjectures. | geometry, function analysis | ||
| Uses wireless networking to connect students' handhelds to the classroom computer. Used for collecting data, polls, and student work. Improves engagement, understanding, performance. | all content areas are applicable | accompanying tutorials | |
| An interactive whiteboard with the power level of a computer. | all content areas are applicable | ||
| Personal Computer Tools | |||
| A simple and basic (yet free) Website editor of the "What-You-See-Is-What-You-Get" variety. A good start for novice Web designers and developers. | no content applications | ||
| A free image editor. | no content applications | ||
| A screen capturer and screen recorder useful for demonstration purposes. | no content applications |


I am Arnanta Chatterjee presently pursuing M.Sc in Computer Science From St. Xavier's College(Autonomous),Kolkata.
I am an aspiring Full Stack Web Developer and have made a few projects using various frameworks. I am doing a few projects
in Data Science domain also. I try to make apps using React Native framework and I just love writing.
But man is not made for defeat. A man can be destroyed but not defeated. -Ernest Hemingway
You can know more about me by clicking the button below
Using non-linear SVM book genre is predicted from its summary.
A car rental service where anyone can book a car in their city on hourly basis and even for days.


Most vegetable and almost all fruits contain a small amount of alcohol in them.
It is expensive because of the age of this cognac which has been over 100 years. Secondly, it is due to the bottle which is coated with 24-carat gold and sterling platinum and is studded with an outrageous 6,500 diamonds.
Alcohol is not exclusively a terrestrial matter. Astronomers found out there is a lot alcohol in space as well.
Drinking red wine in moderation is believed to be good for the heart. Red wine contains resveratrol, a substance which helps control cholesterol, prevent blood vessel damage, and stop blood clots.
Stress relief is another side effect of drinking alcohol. This is caused by an increase in the uptake of another neurotransmitter, called GABA.


4 references to BypassGroupPolicyCaching
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engine\runtime\CompiledScriptBlock.cs (1)
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‘End of an era’ as iconic Cork mural falls foul of alcohol law
You could say it was ‘down in one’, because one day is all it took for Heineken to blot out the pint of stout that dominates an iconic mural in Macroom, Co Cork.
The act was not a case of Dutch courage, but a response to new laws banning alcohol advertising within 200m of a school, creche, or local authority playground.
Failure to do so would have exposed Heineken to hefty fines or even a spell in jail as the new laws kicked in at midnight last night.
The 20-year-old Murphy’s mural, painted on the gable end of a private home on Castle St, was visible to anyone crossing the bridge, including pupils of schools such as De La Salle College and McEgan College.

It was painted by two well-known local artists, Denis O’Reardon and Cormac Shiel.
“Anyone travelling from the Killarney side would see it on their way over the bridge. Only the other day I saw busloads of tourists running back from the square to take pictures,” said local councillor Martin Coughlan.
“It has been a landmark. It’s just unfortunate that it had to go.”
A spokesperson for Heineken confirmed that they inspected this site, and all of their advertising sites, in the context of the new legislation.
“This site is within 200m of a school and we had no other choice but to remove it,” said the Heineken spokesperson.
The mural was painted in 1999 following an arrangement between the Flynn family, who own the house, and brewing company Murphy’s. It was maintained by Heineken after it took over the brewery.
James Flynn, who lives in the house, said it was the end of an era.
“Unfortunately there is nothing we can do,” he said.
However, he said: “If anyone wants to discuss taking the advertising space they are more than welcome to contact us — the rates are negotiable.”
While the pint glass element of the mural has been painted over, it is understood that workers are due back next week to complete the job.
Mr Coughlan is hoping the artists who painted the mural may be invited to once again use the gable end as a canvas.
He says Denis and Cormac recently completed four other murals in the town that have been a hit with townsfolk and tourists alike.


--- title: Who you gonna call? R processes! author: Irene Steves date: '2019-06-15' slug: callr categories: [] tags: [] cover: /callr-cover.png ---
Aside from the workshops and talks at #rstudioconf back in January, I picked up many useful nuggets from the stream of conference tweets. I was particularly struck by this one:
{{% tweet "1086253815916126209" %}}Not feeling the amazement? Okay, let me explain…
Loyal readers may remember my previous blogpost, in which I described starting a new R process to run a plumber API for testing. To do so, I opened up a new RStudio terminal with rstudioapi::terminalCreate()
, then sent it commands with terminalSend()
. My commands sometimes “got lost” if I sent them too soon, so I forced my function to “nap” and try again until the commands were fully complete. A bit of a convoluted workaround, but it worked.
The callr
package, on the other hand, deals with R processes in a much more sophisticated way. The authors describe it this way:
Call R from R: It is sometimes useful to perform a computation in a separate R process, without affecting the current R process. This package does exactly that.
I was able to catch Gábor Csárdi, the creator and maintainer of the callr
package, at the #TidyverseDevDay for a live demo.
Gábor showed me r()
, which evaluates an expression in another R session, and then returns the output. The first argument is a function, and the second argument is a list of the arguments/parameters to pass into the function.
library(callr)
r(function(i) 1 + i, list(i = 5))
## [1] 6
Try running the code again and observe how it freezes up your R session for a moment before returning the answer 6
. It sends the call, then waits for the response and returns it to you.
The r_bg()
(background) function takes the same arguments, but once it sends your expression to the new R session, it stops immediately. You must retrieve the output yourself by using $get_result()
on the R process object.
my_r_process <- r_bg(function(i) 1 + i, list(i = 5))
my_r_process$wait()
my_r_process$get_result()
## [1] 6
If you try to get_result()
right away, you get an error (though you may not notice if you run these lines interactively one-by-one).
my_r_process <- r_bg(function(i) 1 + i, list(i = 5))
my_r_process$get_result()
## Error in rp_get_result(self, private): Still alive
Because of this behavior, r_bg()
is much faster than r()
. However, if you try to retrieve the output to soon, you may run into errors.
library(tictoc)
tic(); r(function(i) 1 + i, list(i = 5)); toc()
## [1] 6
## 0.244 sec elapsed
tic(); r_bg(function(i) 1 + i, list(i = 5)); toc()
## PROCESS 'R', running, pid 82950.
## 0.021 sec elapsed
For code that takes a while to run, the “notorious” r_bg()
is a clear winner–it keeps your console free to use while working hard on your behalf in the background.
You can browse through the available functions if you type callr::
(hit <TAB>
if nothing shows up automatically). You may notice that some of the options are not functions at all, but rather R6 class generators.
class(callr::r_process)
## [1] "R6ClassGenerator"
What in the world is R6?1 Unlike regular R objects, R6 objects are mutable. That is, you can change them without having to reassign the changed version to the old variable name. This sort of functionality is particularly well-suited to processes.
In pseudo-code, it goes something like this:
# "normal" R - variables don't change unless they are reassigned
my_num <- 7
my_num <- multiply_three(my_num)
my_num # becomes 21 because it has been re-assigned
versus
# the world of R6
my_num <- MyR6Class$new(7)
my_num$multiply_three()
my_num # magically becomes 21
Those of you familiar with Python or other programming languages may already have some intuition for mutable objects.
Among the R6 classes in the package are r_process
and r_session
. An r_process
starts in the background, evaluates an R function call, and then quits.
rp <- r_process$new(r_process_options(func = function(i) i + 1,
args = list(i = 2)))
rp$wait() # if we call get_result too soon, we get an error
rp$get_result()
## [1] 3
rp
## PROCESS 'R', finished.
On the other hand, an r_session
continues to run in the background until it is explicitly closed.
rs <- r_session$new()
rs$run(function(i) i + 1, args = list(i = 2))
## [1] 3
rs$run(function() paste(stringr::fruit[1:3], collapse = " and "))
## [1] "apple and apricot and avocado"
rs
## R SESSION, alive, idle, pid 82966.
R is a single-threaded language, meaning that it can only handle one thing at a time. We have ways of speeding things up with vectorization and parallelization, but callr
provides us with even more flexibility. We can partition code into separate processes ourselves. Next time, instead of source()
ing your script or–as in my previous case–opening up a new RStudio terminal, consider if it’s time to call up an R process.
For a full explanation of R6, take a look at this chapter in Advanced R.↩


Thoughts and considerations on Convertible Note financings
December 14, 2014
In the last 5-10 years, Convertible Notes (aka Convertible Debt) emerged as a popular new financing option for early stage start-ups. There were a few main factors, real or perceived, driving their adoption:
- Avoid price/valuation discussions
- Minimize legal fees and overhead
- Overall simplicity and speed to close
The biggest, and most controversial, is probably around pricing. For the uninitiated, Convertible Note pricing typically has two components:
- A discount percentage on the valuation in the next financing round, typically in the range of 10-30%. The idea is that, since an earlier investor puts in money at a riskier time for the business, they deserve a significantly lower price than the investors who come in later on.
- A valuation cap, i.e. the maximum price the convertible note holder will acquire equity at in the subsequent round. So if an investor puts in $1M with a $10M cap, and the company raises additional equity at a $20M pre-money valuation a year later, then the convertible note holder effectively gets equity at a 50% discount to the new investors. This protects the convertible note investor above and beyond the discount percentage, since a 20% discount to a $20M valuation would still mean the debt investor converts at a price of $16M, rather than the maximum cap of $10M.
Notes priced on these terms are called
Discounted Convertible Notes with a Cap
There are a number of problems with convertible notes that can cause unexpected issues both for founders and for investors, unfortunately. The first one should be the elephant in the room: valuation. While it is understandable for entrepreneurs and investors to want to avoid this hard conversation at such an early stage -- when there is so little hard evidence on when to make a decision -- it can easily come back to bite them later on. Much better to have the difficult conversations up front, before you enter a formal business relationship that is likely to last many years, than after. Furthermore, caps don't actually free entrepreneurs from many of the negatives associated with setting a price. One of the big issues founders face is with "down rounds", and unfortunately, raising money at a price lower than your valuation cap can often be a red flag to those outside your company, and an unexpected *perceived* dilutive event to existing equity holders.
There absolutely is merit to giving early investors a discount to subsequent rounds, but it can be achieved through other mechanisms besides discounts and caps. To offer an investor a 20% discount, you could just issue her one "free" share for every four purchased. Five for the price of four. In practice this free share would be structured as a nominally-priced warrant, without any of the preferred stock rights. But the economic effect is the same.
Convertible notes also have a perception of being "cheaper" in terms of legal fees, because most of the docs are boiler-plate, and do not require all the legwork of a formal equity round. However thanks to the smart work of lawyers like Ted Wang, the terms and structure of seed-stage equity rounds are becoming more standardized, and require less customization as well. Less customization = fewer billable hours, and many seed equity rounds can now be completed for just ~$5k, leveraging a wealth of preexisting documents.
There are countless other considerations here which I don't have time to address in this post -- quality of the investors, control provisions, effective liquidation preferences, board rights, signaling issues, etc. But thanks largely to YCombinator and other accelerators, convertible notes are likely here to stay, at least for a while. It is important for entrepreneurs and investors alike to research their various funding options thoroughly, and understand their key characteristics.


It uses utility classes for typography and spacing to space content out within the larger container.
Learn more


Loads of data is generated on a daily basis on social platforms in the form of congestion notifications, slow moving traffic updates, traffic images, and news ticker data among others. These valuable pieces of information can be of great value to traffic administrators and commuters in proactive monitoring, decision making and effective analysis of the traffic condition in that particular area. With effective and proactive traffic administration and navigation as the primary focus, this project trying to tackle these problems by introducing a real-time system that takes the advantages of the social media data and combines with existing Geographical Information System like Google maps for real-time detection and diagnosis of traffic incidents.
The primary objective of this project is to triangulate road traffic incidents using social media data. Once traffic incidents geo-location data extracted, a spatial data layer built over Google map to display all of these incidents.
Major component are:
I tried to look for the existing freely available social media posts database for this project but I had no luck. So, I decided to go ahead to gather social media data by my own ( in my case only Twitter - for now). The data collector program was developed using Python( it was integrated into the Windows services ) to gathered tweets for a period of six months. For more visit git page here.
The dataset contained tweets related to politics, personal communications, product promotions, traffic related incidents and much more. To filter out the tweets linked with traffic-related incidents, machine learning based SVM classifier was used. It consist of three major parts :
Tweets contains substantial noise in it, like URLs, emoticons. In order to remove these unwanted entities from the text, regular expression (regex) used.
To convert a tweet into a feature vector, previously i had used the Bag of Words (BOW) method. The BOW method generates a term frequency model, which is basically a list of numbers produced on the basis of how many times a term is appearing in the text but problem BOW ignore the semantics of the words i.e. BOW not able to capture the relation between words. It treats every word as an individual identity. To overcome this, skip ngram based word2vec model used for feature extraction.
Different supervised classifier algorithms tried over the extracted data and found that the classifiers with complex decision had low accuracy. As an example Neural Network (with two layers) - 54%, Support Vector Machine (SWM) with Gaussian kernel - 94%, SVM with polynomial (of degree two) kernel - 93 %, Logistic Regression - 94 %. for more result click here - detail data. . The main cause of this was over-fitting as when the data was plotted, I figured out that some classifiers were overfitting over training data.
To convert a tweet into a feature vector, previously i had used the Bag of Words (BOW) method. The BOW method generates a term frequency model, which is basically a list of numbers produced on the basis of how many times a term is appearing in the text but problem BOW ignore the semantics of the words i.e. BOW not able to capture the relation between words. It treats every word as an individual identity. To overcome this, skip ngram based word2vec model used for feature extraction.
Deep learning Word2Vec method for feature extraction.
Created text classifier models using Neural Network, Support Vector Machine (with Gaussian and polynomial kernel) and accuracy of 93 %.
Geo parsing location information using Name Entity recognition for buildings and gazetteer for toponym detection.
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saurabhkumar
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saurabhkumar.spsu@gmail
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saurabh-kumar-22179227


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v0.4 documentation
The underlying framework upon which Calloway resides is Django. It is written in Python and promotes adding functionality in pluggable “Applications” that can be reused in other projects. This isolation of functionality is what Calloway uses to combine other open source projects together to create a cohesive infrastructure.
Calloway is based on four principles:
Integration without dependency Django pluggable apps should do one thing and only one thing and do it well. For example, a blog app, should not include tagging, categories, authors, and other things that might augment the core functionality of the blog. It should manage blogs and entries and make it easy to write them and deliver them.
This allows you to build the core infrastructure like building blocks, adding in the functionality that you need in the way you need it.
User interface for content managers is important The Django admin has spoiled us developers. While it is great in many respects, where it is lacking is often overlooked by developers. The content managers, the ones who keep your site up-to-date with the stuff users want, suffer.
We want to promote and provide methods to improve the user experience for content managers, and promote applications that do so as well.
Easy customization through overrides and fallbacks The methods that Django uses for handling settings and templates nails this idea. Templates are easily overridden one at a time, and it is easy to provide template to fallback on if no other is there.
Simple enough for a designer to do it This is not meant to be derogatory, but a workflow advantage. A designer that is comfortable with the command line should be able to bootstrap a project, pick the building blocks of functionality and start designing the front end.
We’ve been frustrated attempting to get supposed “pluggable” applications to work together; even our own. We realized that most of the time, it is because the application was written with some assumptions that are either not communicated, or don’t work in a different situation.
Calloway aims to be assumption free. But it probably isn’t right now. That’s because you haven’t tried it in your situation to discover an assumption not yet realized and fixed.
Lists of application “bundles” that are known to work together. Some of them we’ve written, most are really good third-party applications from the community. The application bundles are merely shortcuts and not required.
You can see the list of application bundles at Application bundles in settings.
Dependency management. With the applications that it knows about, it can generate a requirements file for pip to make installation of the packages easy.
Project templating. The Django startproject command is nice for beginners, but is missing many things that you probably need and want. We provide an example project template, but encourage you to customize it to your heart’s desire.
A fundamental Calloway principle is progressive enhancement. By providing good defaults, you only override what you need. You have access to individual parts, as well as a combined whole.


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