@* * Copyright 2015 Yahoo Inc. Licensed under the Apache License, Version 2.0 * See accompanying LICENSE file. *@ @(crumbs: IndexedSeq[models.navigation.BreadCrumbs.BreadCrumbRendered]) @import models.navigation.BreadCrumbs._ @renderLink(name: String, link: String) = {


Click here to go to the corresponding page for the latest version of DIALS
Source code for dxtbx.model.beam
from __future__ import absolute_import, division
#!/usr/bin/env python
# beam.py
# Copyright (C) 2011 Diamond Light Source, Graeme Winter
#
# This code is distributed under the BSD license, a copy of which is
# included in the root directory of this package.
#
# A model for the beam for the "updated experimental model" project documented
# in internal ticket #1555. This is not designed to be used outside of the
# XSweep classes.
import math
import pycbf
from dxtbx_model_ext import Beam
import libtbx.phil
beam_phil_scope = libtbx.phil.parse(
"""
beam
.expert_level = 1
.short_caption = "Beam overrides"
{
wavelength = None
.type = float
.help = "Override the beam wavelength"
direction = None
.type = floats(size=3)
.help = "Override the sample to source direction"
.short_caption = "Sample to source direction"
polarization_normal = None
.type = floats(size=3)
.help = "Override the polarization normal"
.short_caption = "Polarization normal"
polarization_fraction = None
.type = float(value_min=0.0, value_max=1.0)
.help = "Override the polarization fraction"
.short_caption = "Polarization fraction"
}
"""
)
[docs]class BeamFactory:
"""A factory class for beam objects, which encapsulate standard beam
models. In cases where a full cbf description is available this
will be used, otherwise simplified descriptions can be applied."""
def __init__(self):
pass
[docs] @staticmethod
def from_phil(params, reference=None):
"""
Convert the phil parameters into a beam model
"""
# Check the input
if reference is None:
beam = Beam()
else:
beam = reference
# Set the parameters
if params.beam.wavelength is not None:
beam.set_wavelength(params.beam.wavelength)
elif reference is None:
raise RuntimeError("No wavelength set")
if params.beam.direction is not None:
beam.set_direction(params.beam.direction)
elif reference is None:
raise RuntimeError("No beam direction set")
if params.beam.polarization_normal is not None:
beam.set_polarization_normal(params.beam.polarization_normal)
if params.beam.polarization_fraction is not None:
beam.set_polarization_fraction(params.beam.polarization_fraction)
# Return the model
return beam
[docs] @staticmethod
def from_dict(d, t=None):
"""Convert the dictionary to a beam model
Params:
d The dictionary of parameters
t The template dictionary to use
Returns:
The beam model
"""
from dxtbx.model import Beam
# If None, return None
if d == None:
if t == None:
return None
else:
return from_dict(t, None)
elif t != None:
d = dict(t.items() + d.items())
# Create the model from the dictionary
return Beam.from_dict(d)
[docs] @staticmethod
def make_beam(
sample_to_source=None,
wavelength=None,
s0=None,
unit_s0=None,
divergence=None,
sigma_divergence=None,
):
if divergence is None or sigma_divergence is None:
divergence = 0.0
sigma_divergence = 0.0
if sample_to_source:
assert wavelength
return Beam(
tuple(map(float, sample_to_source)),
float(wavelength),
float(divergence),
float(sigma_divergence),
)
elif unit_s0:
assert wavelength
return Beam(
tuple(map(lambda x: -x, map(float, unit_s0))),
float(wavelength),
float(divergence),
float(sigma_divergence),
)
else:
assert s0
return Beam(tuple(map(float, s0)))
[docs] @staticmethod
def make_polarized_beam(
sample_to_source=None,
wavelength=None,
s0=None,
unit_s0=None,
polarization=None,
polarization_fraction=None,
divergence=None,
sigma_divergence=None,
flux=None,
transmission=None,
):
assert polarization
assert polarization_fraction >= 0.0
assert polarization_fraction <= 1.0
if divergence == None or sigma_divergence == None:
divergence = 0.0
sigma_divergence = 0.0
if flux is None:
flux = 0
if transmission is None:
transmission = 1.0
if sample_to_source:
assert wavelength
return Beam(
tuple(map(float, sample_to_source)),
float(wavelength),
float(divergence),
float(sigma_divergence),
tuple(map(float, polarization)),
float(polarization_fraction),
float(flux),
float(transmission),
)
elif unit_s0:
assert wavelength
return Beam(
tuple(map(lambda x: -x, map(float, unit_s0))),
float(wavelength),
float(divergence),
float(sigma_divergence),
tuple(map(float, polarization)),
float(polarization_fraction),
float(flux),
float(transmission),
)
else:
assert s0
return Beam(
tuple(map(float, s0)),
float(divergence),
float(sigma_divergence),
tuple(map(float, polarization)),
float(polarization_fraction),
float(flux),
float(transmission),
)
[docs] @staticmethod
def simple(wavelength):
"""Construct a beam object on the principle that the beam is aligned
with the +z axis, as is quite normal. Also assume the beam has
polarization fraction 0.999 and is polarized in the x-z plane."""
return BeamFactory.make_beam(
sample_to_source=(0.0, 0.0, 1.0), wavelength=wavelength
)
[docs] @staticmethod
def simple_directional(sample_to_source, wavelength):
"""Construct a beam with direction and wavelength."""
return BeamFactory.make_beam(
sample_to_source=sample_to_source, wavelength=wavelength
)
[docs] @staticmethod
def complex(
sample_to_source, polarization_fraction, polarization_plane_normal, wavelength
):
"""Full access to the constructor for cases where we do know everything
that we need..."""
return BeamFactory.make_polarized_beam(
sample_to_source=sample_to_source,
wavelength=wavelength,
polarization=polarization_plane_normal,
polarization_fraction=polarization_fraction,
)
[docs] @staticmethod
def imgCIF(cif_file):
"""Initialize a detector model from an imgCIF file. N.B. the
definition of the polarization plane is not completely helpful
in this - it is the angle between the polarization plane and the
+Y laboratory frame vector."""
d2r = math.pi / 180.0
cbf_handle = pycbf.cbf_handle_struct()
cbf_handle.read_file(cif_file, pycbf.MSG_DIGEST)
cbf_handle.find_category("axis")
# find record with equipment = source
cbf_handle.find_column("equipment")
cbf_handle.find_row("source")
# then get the vector and offset from this
direction = []
for j in range(3):
cbf_handle.find_column("vector[%d]" % (j + 1))
direction.append(cbf_handle.get_doublevalue())
# and the wavelength
wavelength = cbf_handle.get_wavelength()
# and information about the polarization - FIXME this should probably
# be a rotation about the beam not about the Z axis.
try:
polar_fraction, polar_angle = cbf_handle.get_polarization()
except Exception:
polar_fraction = 0.999
polar_angle = 0.0
polar_plane_normal = (
math.sin(polar_angle * d2r),
math.cos(polar_angle * d2r),
0.0,
)
return BeamFactory.make_polarized_beam(
sample_to_source=direction,
wavelength=wavelength,
polarization=polar_plane_normal,
polarization_fraction=polar_fraction,
)
[docs] @staticmethod
def imgCIF_H(cbf_handle):
"""Initialize a detector model from an imgCIF file. N.B. the
definition of the polarization plane is not completely helpful
in this - it is the angle between the polarization plane and the
+Y laboratory frame vector. This example works from a cbf_handle,
which is already configured."""
d2r = math.pi / 180.0
cbf_handle.find_category("axis")
# find record with equipment = source
cbf_handle.find_column("equipment")
cbf_handle.find_row("source")
# then get the vector and offset from this
direction = []
for j in range(3):
cbf_handle.find_column("vector[%d]" % (j + 1))
direction.append(cbf_handle.get_doublevalue())
# and the wavelength
wavelength = cbf_handle.get_wavelength()
# and information about the polarization - FIXME this should probably
# be a rotation about the beam not about the Z axis.
try:
polar_fraction, polar_angle = cbf_handle.get_polarization()
except Exception:
polar_fraction = 0.999
polar_angle = 0.0
polar_plane_normal = (
math.sin(polar_angle * d2r),
math.cos(polar_angle * d2r),
0.0,
)
return BeamFactory.make_polarized_beam(
sample_to_source=direction,
wavelength=wavelength,
polarization=polar_plane_normal,
polarization_fraction=polar_fraction,
)


CrowdHuman_train01.zip [Baidu Drive] [Google Drive]
CrowdHuman_train02.zip [Baidu Drive] [Google Drive]
CrowdHuman_train03.zip [Baidu Drive] [Google Drive]
CrowdHuman_val.zip [Baidu Drive] [Google Drive]
annotation_train.odgt [Baidu Dirve] [Google Drive]
annotation_val.odgt [Baidu Drive] [Google Drive]
CrowdHuman_test.zip
[Baidu Drive] Fetch Code: cr7k
[Google Drive]
We support annotation_train.odgt
and annotation_val.odgt
which contains the annotations of our dataset.
odgt
is a file format that each line of it is a JSON, this JSON contains the whole annotations for the relative image. We prefer using this format since it is reader-friendly.
JSON{
"ID" : image_filename,
"gtboxes" : [gtbox],
}
gtbox{
"tag" : "person" or "mask",
"vbox": [x, y, w, h],
"fbox": [x, y, w, h],
"hbox": [x, y, w, h],
"extra" : extra,
"head_attr" : head_attr,
}
extra{
"ignore": 0 or 1,
"box_id": int,
"occ": int,
}
head_attr{
"ignore": 0 or 1,
"unsure": int,
"occ": int,
}
Keys
in extra
and head_attr
are optional, it means some of them may not existextra/head_attr
contains attributes for person/head
tag
is mask
means that this box is crowd/reflection/something like person/...
and need to be ignore
(the ignore
in extra
is 1
)vbox, fbox, hbox
means visible box, full box, head box
respectivelyTerms of use: by downloading the image data you agree to the following terms:


{{grant.client.client_name}} is requesting: {{ grant.request.scope }}
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Introduction
What?
Doxecute is a document based database with basic SQL support such as INSERT, UPDATE, DELETE, and SELECT statement. No more complicated JavaScript filters, expressions, callback functions.
It is schemaless. Because it is a document based database, you can insert or update your document using JSON format without even issuing CREATE TABLE statement. Instead of a table, you have a collection where you put all kinds of JSON documents you want to store.
Doxecute support SQL from version 1.0. JavaScript will be supported from version 2.0.
Why?
Popular document based databases support JavaScript as a query language. However, we database guys are familiar with SQL. You can run ad-hoc queries simply by issuing a SQL statement. The SELECT statement supports WHERE, INNER JOIN, GROUP BY, and ORDER BY clause.
Doxecute separates different kinds of JSON documents into different storage to reduce reading unnecessary data from disk while running your query on a specific kind of JSON document.
How?
Doxecute is carefully designed to support transaction feature on a document based database. It automatically categorizes JSON documents based on the similarity between documents to store different kinds of JSON documents on different storage. It results in reduced amount of I/O for running a query on a specific kind of JSON documents.
Tutorial?
To check out the tutorial on deploying Doxecute on Amazon EC2 node and manipulating JSON documents with SQL, click here.
When?
The first closed alpha testing starts on 15th Aug. To join the alpha testing, click here.


Having a mother diagnosed with chronic pain has given me consistent access to pills for years. The first time she offered me Tramadol, I was rather skeptical as the literature described it as a non-narcotic. To me this sounded about as fun as popping Ibuprofen. But since I had a sore back, I tried them anyway.
First of all, I don't think they're very effective as a painkiller. A prescription-strength anti-inflammatory alleviates my pain just as well. A doctor once told me that morphine doesn't actually alter perception of pain, it just makes you feel so good you forget you hurt. Same concept with tramadol.. Twenty minutes to an hour after oral ingestion, I just get happy. Like a switch is activated in my brain, I can pinpoint the exact moment it hits me. One second, normal, next second 'Damn I'm happy to be alive.' That's where the parallel with opiates ends, however, in my experience.
The label warns about drowsiness, but I can't take it after about two pm, if I want to sleep that night. It also causes other stimulant-like side effects like loss of appetite and dehydration. Much like meth, it becomes easy to 'zone in' on monotonous physical tasks like household cleaning , filing paperwork or even sex. The most trivial things are infused with enthusiasm. One must be careful, it's easy to start talking a mile a minute, or go on a marathon organizing/cleaning binge. At higher doses, physical effects include pinpoint pupils and profuse sweating. Twice I got heart palpitations.
I would describe the high as the bliss of Demerol minus the head-fog, and the energy/stamina of meth. There's no mental high to speak of, but reasoning and comprehension seem to be decreased. Combining it with marijuana gives me the mental high that's lacking, and counteracts the lethargy pot can induce. Best of both worlds. If I take it with Xanax, the energy of Tramadol balances the drowsiness of Xanax, isolating the calming effects of the sedative. Taking it with meth doubles the energy and concentration, but I can access the creativity and intellect that Tramadol restricts. If I wake up with a line or two, chasing it with a few Tramadol in the afternoon gives it legs, and I avoid the comedown. Iím careful with alcohol. A day in la-la land usually leaves me with an empty stomach, and it's easy to overestimate my alcohol tolerance and go into blackout territory.
In closing, I think Tramadol is the Pharmacy's best kept secret. It has the potential to be the new 'mommy's little helper'.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


General Linear Models
A general linear model describes a dependent variable in terms of a linear combination of independent variables and an error term.
Simple Linear Regression
A line can be described by the equation \(y = mx + b\), where \(b\) is the \(y\)-intercept and \(m\) is the slope. Simple linear regression uses this idea to describe the relationship between two variables—one dependent, the \(y\), and one independent, the \(x\). Dependent variables are also sometimes referred to as "response variables," "regressands," or "targets." The corresponding terms for independent variables are "predictor variables," "regressors," or "features."
The objective is to specify a model that explains \(y\) in terms of \(x\) in the population of interest. A simple model can be specified as follows.
\[y = \beta_0 + \beta_1 x + \epsilon\]
In this specification—called the scalar form—\(\beta_0\) represents the \(y\)-intercept and \(\beta_1\) represents the slope. The \(\epsilon\), which is called the error term, represents other factors that affect \(y\). If the error term is excluded, the implication is that the relationship is exact or deterministic. On the other hand, if we include the error term, we imply that the relationship also contains a stochastic component. For most, if not all, applications of regression, the relationship between the dependent and independent variables includes both deterministic and stochastic parts.
The actual \(\beta\) values in the population are unknown. However, they can be estimated using data—preferably a random sample from the population. We use this data to find the line that "best" fits it. Because each observation in our sample has its own values, we use subscripts to denote them.
\[y_i = \beta_0 + \beta_1 x_i + \epsilon_i\]
Notice that the \(y\), \(x\), and \(\epsilon\) terms have subscripts, but the \(\beta\)s do not. This implies that there is a single \(y\)-intercept and slope—the one from the population equation that represents the "true" line.
Multiple Regression
Often, outcomes for our variable of interest, \(y\), depend on two or more predictors. This is referred to as a multiple linear regression model, which, for the case of two variables, can be written as:
\[y_i = \beta_0 + \beta_1 x_{1i} + \beta_2 x_{2i} + \epsilon_i\]
As you might imagine, the scalar form gets unwieldy as the number of independent variables increases. To deal with this, we introduce an alternative way to express our model.
Matrix Form
While the scalar form allows us to be explicit about the regressors included in the model, it is often more efficient to write the equation in matrix form. To motivate the reasons why, consider the case where, for our equation above, \(i = 1, 2, \ldots,n\).
\[y_1 = \beta_0 + \beta_1 x_{11} + \beta_2 x_{21} + \epsilon_1\] \[y_2 = \beta_0 + \beta_1 x_{12} + \beta_2 x_{22} + \epsilon_2\] \[\vdots\] \[y_n = \beta_0 + \beta_1 x_{1n} + \beta_2 x_{2n} + \epsilon_n\]
Our system of \(n\) equations can, instead, be represented in terms of matrix addition. Note that we've included a vector of ones of length \(n\), which we'll call \(x_0\), that we multiply with \(\beta_0\).
\begin{split} \left[\begin{matrix}y_1\\ \vdots\\ y_n\end{matrix}\right] = \beta_0 \left[\begin{matrix}1 \\ \vdots\\ 1\end{matrix}\right] + \beta_1 \left[\begin{matrix}x_{11}\\ \vdots\\ x_{1n}\end{matrix}\right] + \beta_2 \left[\begin{matrix}x_{21}\\ \vdots\\ x_{2n}\end{matrix}\right] + \left[\begin{matrix}\epsilon_1\\ \vdots\\ \epsilon_n\end{matrix}\right] \end{split}
This can be restated in terms of matrix multiplication.
\begin{split} \left[\begin{matrix}y_1\\ \vdots\\ y_n\end{matrix}\right] = \left[ \begin{matrix} 1 & x_{11} & x_{21}\\ \vdots & \vdots & \vdots\\ 1 & x_{1n} & x_{2n} \end{matrix} \right] \left[\begin{matrix}\beta_0\\ \beta_1\\ \beta_2\end{matrix}\right] + \left[\begin{matrix}\epsilon_1\\ \vdots\\ \epsilon_n\end{matrix}\right] \end{split}
Here, we've simply combined the \(x_0\), \(x_1\), and \(x_2\) into a matrix, which we refer to as \(X\), called the design matrix, and combined the \(\beta\)s into a column vector, which we refer to as \(\vec{\beta}\), called the parameter vector. In this example, \(X\) is an \(n \times p\) matrix and the column vector \(\vec{\beta}\) can be thought of as a \(p \times 1\) matrix, where \(p = 3\). Because the number of columns in \(X\) equals the number of rows in \(\vec{\beta}\), they can be multiplied, resulting in an \(n \times 1\) column vector. This is the deterministic portion of our model, which, when added to the stochastic component, \(\vec{\epsilon}\), results in \(\vec{y}\).
The matrix form of our model can be rewritten compactly using vector notation.
\[\vec{y} = X\vec{\beta} + \vec{\epsilon}\]
This specification can handle an arbitrary number of parameters. For each parameter, \(p\), add a column to \(X\) and an element to \(\vec{\beta}\).Solving
There are several ways to solve for the "best" line. To illustrate this, we'll go back to the simple linear regression model. Let's suppose we'd like to create a regression line for the data shown in the following plot, created using the code below. (Note: the complete set of commands used to make this plot can be found here.)
import numpy as np import matplotlib.pyplot as plt np.random.seed(1868) x = np.random.normal(0, 1, 20) y = np.array([np.random.normal(v, 0.5) for v in x]) plt.scatter(x, y, s=35, alpha=0.85, color='#328E82')
The "best" line is usually thought of as the one that minimizes the vertical distance between it and the data points. Typically, the distance that is minimized is the sum of the squared residuals. This is the ordinary least squares estimation. Residuals are the difference between each \(y_i\) and its estimated (or fitted) value, \(\hat{y}_i\). In vector notation, these can be written as \(\vec{y}\) and \(\hat{\vec{y}}\), respectively. The idea is to find values of \(\beta_0\) and \(\beta_1\) that minimize: \[\sum_{i=1}^n (y_i - \hat{y}_i)^2\]
The terms "errors" and "residuals" are often used as synonyms, unfortunately. In reality, they are quite distinct. Errors are a population-level concept and are unobservable. Residuals, on the other hand, are sample-specific and are computed from the data when we estimate the \(\beta\)s.
Recall that we can specify the model for the data above as \(\vec{y} = X\vec{\beta} + \vec{\epsilon}\). From this, we can use matrix algebra to estimate the "best" line.
\[\hat{\vec{\beta}} = (X^T X)^{-1} X^T \vec{y}\]
Using NumPy, we can estimate the parameter vector as follows.
import numpy.linalg as npl ones = np.ones(len(x)) X = np.column_stack((ones, x)) b = npl.inv(X.T.dot(X)).dot(X.T).dot(y)
Here, \(b\) is the estimated \(\hat{\vec{\beta}}\) with values [-0.08396926, 1.10769145]. These represent \(\hat{\beta}_0\) and \(\hat{\beta}_1\), respectively. This results in the following regression line, in which is the sum of the squared distances between the data points and the line have been minimized.
For this approach to work, \(X^T X\) must be invertible or nonsingular. This means that the columns in \(X\) must be linearly independent. If this isn't the case—that is, if \(X^T X\) is not invertible—then there will be an infinite number of solutions for \(\hat{\vec{\beta}}\).
The General Linear Model
"The term general linear model refers to a linear model of form \(\vec{y} = X\vec{\beta} + \vec{\epsilon}\)" (Poline and Brett). It's "general" in the sense that the data in \(X\) can be continuous, discrete, or even binary (dummy). The errors, \(\vec{\epsilon}\), are assumed to be independent and identically distributed with a zero mean and \(\sigma^2\) variance. General linear models need not only describe a single \(y\). That is, they can also cover multivariate models.
Final Thoughts
We've seen how to construct simple and multiple linear regression models using matrix notation and how to estimate the parameters using NumPy. These models are useful and are used often for describing the relationships between variables.
The following sources were invaluable for my
understanding of these concepts.


Waldorf Blofeld
Multi Patch Editor
This editor will work with the Waldorf Blofeld, the Waldorf Blofeld with SL Option, and the Waldorf Blofeld Keyboard (which automatically comes with the SL option).
Communicating with Edisyn Set your Blofeld to send Sysex only, not Ctl nor Ctl+Sysex. Also make sure that Ctrl Receive is on.
Play Each Part has a button called Play. This sets the status to "play" (versus "mute").
Show Each Part has a button called Show. This shows the given sound (bank and number) as a separate Blofeld Single editor panel.
Gotchas Multimode on the Blofeld is a mess.
-
The Blofeld's Multimode sysex is entirely undocumented, and this patch editor is the result of reverse engineering. There may be errors: please report them.
- Edisyn cannot tell the Blofeld to switch to a given multi patch number. As a result, *Send Patch* and *Send Patch To...* are disabled. To send a patch, set the patch location manually on your synthesizer, then use *Send Current Patch*. To get a patch, manually switch to the desired patch number, then call *Request Current Patch* (or dump the patch manually to Edisyn).
- The Blofeld doesn't send or receive sysex for updating individual Multimode parameters, so you won't see changes on the Blofeld (or Edisyn) when tweaking a parameter on the other.
-
If you save a multimode patch on the Blofeld, it corrupts the first letter of the name of every patch! This is not permanent (just reboot your Blofeld), but it's reported to Edisyn when Edisyn tries to load the patch from temporary memory again, so you'll see a corrupted patch name on receiving a patch.
- (On purpose) you can't mutate or merge the Play, Local, MIDI, or USB options as they turn on or off the ability to test playing a note at all.
- Effects work strangely in Multimode. Part 1's Effect 2 settings are applied to all parts regardless of their own settings. Also, Parts 1 through 4 can have their own independent settings for Effect 1, but Parts 5 through 16 do not have Effect 1 at all (it's just turned off). In either case, you set the mix level of the Effect in question for each Part in its own program patch, not in the multimode patch.
| By
| Sean Luke
|
| Date
| June 2017
|


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You can buy a items in the shop, some of which are automatically handled by the system and others require to be manually handled by authorized staff members.


Job: IT Support Analyst and Consultant
SUMMARY
The IT Support Analyst and Consultant will be working directly with our clients and provide technical support for existing systems and technical advice for future oriented plans.
ESSENTIAL DUTIES AND RESPONSIBILITIES
- Provide customer service support on-site or via phone, email for technical and non-technical users
- Gather and document system requirements
- Understand and troubleshoot technical problems
- Desktop installation and support
- Escalate issues to higher support engineers
REQUIRED EXPERIENCE AND QUALIFICATIONS
- Diploma in Information Technology related field
- Excellent communication, interpersonal, and problem-solving skills
- Knowledge with remote network and PC support
- Understanding of Microsoft and Linux based operating systems, anti-virus/anti-spyware, backup & recovery methods, and PC & basic network hardware required.


FCS Teaching Topics: Giving Better Feedback Directly on Students' Project
Work to Reduce Your Workload
Recommendations
Not everything on an assignment must have written feedback. Choose the most important
points that will make a significant improvement in overall attainment and
provide feedback on that.
Be specific with feedback, it must be accurate and valid to the task.
Formative feedback must shape learning and summative feedback must
explain the outcome.
Formative assessment is ongoing, it takes place during the learning process whereas
summative assessment takes place at the end of a unit or course (ie., projects, tests, exams).
Typically, marks are given for summative assessments only. However, it is important to ensure that
feedback given for both types of assessment is appropriate and meaningful to the learner. The
feedback given on formative assessments should help the learner to improve their work/knowledge of
course material in time to achieve better attainment in summative assessments. The goal
is to improve students' learning which, ideally, should result in better attainment
for the student.
Suggested below are some principles of feedback for consideration that are designed to
enhance student learning and attainment
Principles of Feedback Designed to Enhance Assessment Practices
(Nicol, 2008) - edited:
- Clarify what good performance is (goals, criteria, standards).
- Encourage time and effort on challenging learning tasks.
- Be specific in feedback as to what needs to be improved for a better result
- Provide opportunities to act on feedback (to improve between current and desired
performance).
- Encourage interaction and dialogue around learning (peer and teacher-student).
- Encourage the positive motivational beliefs and self-esteem
What can you do to implement best practice of feedback in your class?
- Ensure students know what they are supposed to do i.e., what it is that they are being
assessed on. Rubrics must be clear! Students can better understand the assessment criteria
by implementing a learning task using it. For example, show learners an example project
(exemplar) and assess it together in class using the rubric. Engaging with the rubric will
develop an understanding of the criteria and the various levels of possible achievement per
criterion.
- Engage students more fully in the assessment process. Carless (2007) cites a range of
activities, such as the use of exemplars, peer feedback, peer assessment and
self-evaluation skills designed to help students use feedback to ‘feedforward’
into summative work.
- Incorporate the above (#2) type of activity within a ‘Community of
Practice’ in order to develop a mutual understanding of tacit and explicit
knowledge between the student and tutor in the assessment process O’Donovan et al. (2008)
Incorporate #2 with the submitted assessment. It is crucial for the tutor/instructor
to see that the student has gone through the process towards improvement in their
learning.
- Curtail summative feedback to ‘a few concise points for further
development'. Whilst reducing summative feedback to minor points and/or a grade
is controversial, it highlights the need to gain an effective balance between different
forms of feedback.
(Nicol & Macfalane-Dick, 2006)
Drop by for a chat at The Academic Loop 239
Cell Number:<PHONE_NUMBER>
I'm looking forward to working with you - Stephanie Bernier
Suggested article (link):
Examining
Principles of Formative and Summative Feedback
References
Carless, D. (2007). Learning‐oriented assessment: conceptual bases and practical
implications. Innovations in Education and Teaching International, 44(1), 57–66. doi:
10.1080/14703290601081332
Heron, G. (2010). Examining Principles of Formative and Summative Feedback. British
Journal of Social Work, 41(2), 276–295. doi: 10.1093/bjsw/bcq049
Nicol, D. J., & Macfarlane‐Dick, D. (2006). Formative assessment and self‐regulated
learning: a model and seven principles of good feedback practice. Studies in Higher Education,
31(2), 199–218. doi: 10.1080/03075070600572090
O'Donovan, B., Price, M., & Rust, C. (2008). Developing student understanding of
assessment standards: a nested hierarchy of approaches. Teaching in Higher Education, 13(2),
205–217. doi: 10.1080/13562510801923344


Black Ninja
About Me
I am a Junior Full-Stack Web Developer. I still have so much to learn. My goal is to become either a professional Software Development Engineer (SDE) or a business owner and run my own web development business.
I am a Junior Full-Stack Web Developer. I still have so much to learn. My goal is to become either a professional Software Development Engineer (SDE) or a business owner and run my own web development business.


Customer information
Thi is where you privide necessary information
Name
{{ customerName }}
{{ customerEmail }}
Address
{{ customerStreet }} {{ customerHouse }}
Payment method
{{ customerPayment }}
Gender
{{ customerGender }}
Burger selection
{{ chosenBurgers }}


I was born 31 July 1980, the only child of James and Lily Potter (née Evans), both members of the original Order of the Phoenix. I married Ginny Weasley, with whom I have three children: James Sirius, Albus Severus, and Lily Luna.
I learned that I was a wizard on my eleventh birthday. I began attending Hogwarts in 1991 and was Sorted into Gryffindor House. While at school I became best friends with Ron Weasley and Hermione Granger. I later became the youngest Quidditch Seeker in over a century and eventually the captain of my house's team, winning two Quidditch Cups.
After the war, I became an Auror and helped reform and revolutionise the Ministry of Magic. In 2007 I was promoted to Head of the Auror Office (I was 26), and would occasionally deliver Defence Against the Dark Arts lectures at Hogwarts. I later went on to be Head of the Department of Magical Law Enforcement by the summer of 2020.


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| PREV NEXT | FRAMES NO FRAMES |
| Packages that use Branch | |
| org.dom4j | Defines the XML Document Object Model in Java interfaces together with some helper classes. |
| org.dom4j.bean | An implementation of the dom4j API which allows JavaBeans to be used to store and retrieve attribute values from Element. |
| org.dom4j.datatype | An implementation of the dom4j API which supports the XML Schema Data Types specification. |
| org.dom4j.dom | An implementation of the dom4j API which also supports the W3C object model. |
| org.dom4j.io | Provides input and output via SAX and DOM together with writing dom4j objects to streams as XML text. |
| org.dom4j.swing | A collection of adapters to allow easy integration with dom4j XML documents and Swing such as TreeModels and TableModels. |
| org.dom4j.tree | Contains the default implementations of the dom4j Document Object Model together with some helpful base classes for those wishing to implement their own document object model. |
| org.dom4j.util | A collection of utility classes for the dom4j API. |
| Uses of Branch in org.dom4j |
| Subinterfaces of Branch in org.dom4j | |
interface |
Document
Document defines an XML Document.
|
interface |
Element
Element interface defines an XML element. |
| Methods in org.dom4j with parameters of type Branch | |
static Element |
DocumentHelper.makeElement(Branch source,
String path)
makeElement a helper method which navigates from the given Document or Element node to some Element using the path expression, creating any necessary elements along the way. |
void |
Branch.appendContent(Branch branch)
Appends the content of the given branch to this branch instance. |
| Constructors in org.dom4j with parameters of type Branch | |
IllegalAddException(Branch parent,
Node node,
String reason)
|
| Uses of Branch in org.dom4j.bean |
| Classes in org.dom4j.bean that implement Branch | |
class |
BeanElement
BeanElement uses a Java Bean to store its attributes.
|
| Uses of Branch in org.dom4j.datatype |
| Classes in org.dom4j.datatype that implement Branch | |
class |
DatatypeElement
DatatypeElement represents an Element which supports the XML Schema Data Types
specification.
|
| Uses of Branch in org.dom4j.dom |
| Classes in org.dom4j.dom that implement Branch | |
class |
DOMDocument
DOMDocument implements an XML document which supports the W3C
DOM API.
|
class |
DOMElement
DOMElement implements an XML element which supports the W3C
DOM API.
|
| Methods in org.dom4j.dom with parameters of type Branch | |
static void |
DOMNodeHelper.appendElementsByTagName(List list,
Branch parent,
String name)
|
static void |
DOMNodeHelper.appendElementsByTagNameNS(List list,
Branch parent,
String namespace,
String localName)
|
| Uses of Branch in org.dom4j.io |
| Methods in org.dom4j.io with parameters of type Branch | |
void |
STAXEventWriter.writeChildNodes(Branch branch)
Writes each child node within the provided Branch instance. |
protected void |
SAXWriter.writeContent(Branch branch,
NamespaceStack namespaceStack)
|
protected void |
DOMReader.readTree(Node node,
Branch current)
|
protected void |
DOMReader.readElement(Node node,
Branch current)
|
| Uses of Branch in org.dom4j.swing |
| Methods in org.dom4j.swing that return Branch | |
protected Branch |
BranchTreeNode.getXmlBranch()
|
| Constructors in org.dom4j.swing with parameters of type Branch | |
BranchTreeNode(Branch xmlNode)
|
|
BranchTreeNode(TreeNode parent,
Branch xmlNode)
|
| Uses of Branch in org.dom4j.tree |
| Classes in org.dom4j.tree that implement Branch | |
class |
AbstractBranch
AbstractBranch is an abstract base class for tree implementors
to use for implementation inheritence.
|
class |
AbstractDocument
AbstractDocument is an abstract base class for tree
implementors to use for implementation inheritence.
|
class |
AbstractElement
AbstractElement is an abstract base class for tree
implementors to use for implementation inheritence.
|
class |
BaseElement
BaseElement is a useful base class for implemementation
inheritence of an XML element.
|
class |
DefaultDocument
DefaultDocument is the default DOM4J default implementation of
an XML document.
|
class |
DefaultElement
DefaultElement is the default DOM4J default implementation of
an XML element.
|
| Methods in org.dom4j.tree with parameters of type Branch | |
void |
AbstractBranch.appendContent(Branch branch)
|
| Uses of Branch in org.dom4j.util |
| Classes in org.dom4j.util that implement Branch | |
class |
IndexedElement
IndexedElement is an implementation of Element which
maintains an index of the attributes and elements it contains to optimise
lookups via name.
|
class |
NonLazyElement
NonLazyElement is the default DOM4J default implementation of
an XML element.
|
class |
UserDataElement
UserDataElement support the adornment of a user data object on
an Element or Attribute instance such that the methods
UserDataElement.setData(Object) will get and set the values of a user data object.
|
| Methods in org.dom4j.util with parameters of type Branch | |
int |
NodeComparator.compareContent(Branch b1,
Branch b2)
|
|
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| PREV NEXT | FRAMES NO FRAMES |


AccountCreateOperation |
Class used to encapsulate operations related to the OperationType.ACCOUNT_CREATE_OPERATION class AccountCreateOperation : BaseOperation |
AccountCreateOperationBuilder |
Represents builder for AccountCreateOperation. Checks required data. class AccountCreateOperationBuilder : Builder<AccountCreateOperation> |
AccountUpdateOperation |
Represents blockchain operation for updating an existing account. class AccountUpdateOperation : BaseOperation |
AccountUpdateOperationBuilder |
Represents builder for AccountUpdateOperation. Checks required data. class AccountUpdateOperationBuilder : Builder<AccountUpdateOperation> |
BlockRewardOperation |
Virtual operation that indicates payout of block reward class BlockRewardOperation : BaseOperation |
ContractCallOperation |
Represents blockchain operation for calling contract w2ith changing state class ContractCallOperation : ContractOperation |
ContractCallOperationBuilder |
Represents builder for ContractCreateOperation. Checks required data. class ContractCallOperationBuilder : Builder<ContractCallOperation> |
ContractCreateOperation |
Represents blockchain operation for creating new smart contract class ContractCreateOperation : ContractOperation |
ContractCreateOperationBuilder |
Represents builder for ContractCreateOperation. Checks required data. class ContractCreateOperationBuilder : Builder<ContractCreateOperation> |
ContractOperation |
Base implementation of contract operation abstract class ContractOperation : BaseOperation |
ContractTransferOperation |
Represents blockchain operation for transferring amount from contract. class ContractTransferOperation : BaseOperation |
CreateAssetOperation |
Encapsulates asset creation information class CreateAssetOperation : BaseOperation |
GenerateBitcoinAddressOperation |
Generates address for botcoin network transfers for required target account account class GenerateBitcoinAddressOperation : BaseOperation |
GenerateEthereumAddressOperation |
Generates address for ethereum network transfers for required target account account class GenerateEthereumAddressOperation : BaseOperation |
IssueAssetOperation |
Encapsulates asset issue operation information class IssueAssetOperation : BaseOperation |
IssueAssetOperationBuilder |
Builder class for asset issue operation IssueAssetOperation class IssueAssetOperationBuilder : Builder<IssueAssetOperation> |
OperationType |
Represents all blockchain operation types enum class OperationType |
OperationTypeToClassConverter |
Maps operation id to required result class type class OperationTypeToClassConverter : Converter<Int, Class<*>?> |
SidechainBurnSocketOperation |
Encapsulates ethereum asset withdraw information class SidechainBurnSocketOperation : BaseOperation |
SidechainERC20BurnSocketOperation |
Encapsulates erc20 token erc20Token burning information in ECHO network class SidechainERC20BurnSocketOperation : BaseOperation |
SidechainERC20DepositSocketOperation |
Encapsulates erc20 token erc20TokenAddress deposit information class SidechainERC20DepositSocketOperation : BaseOperation |
SidechainERC20IssueSocketOperation |
Encapsulates erc20 token erc20Token issue information in ECHO network class SidechainERC20IssueSocketOperation : BaseOperation |
SidechainERC20RegisterTokenOperation |
Registers ethereum ERC20 token by ethAddress in ECHO network class SidechainERC20RegisterTokenOperation : BaseOperation |
SidechainIssueSocketOperation |
Encapsulates ethereum asset issue information class SidechainIssueSocketOperation : BaseOperation |
TransferOperation |
Encapsulates transfer information class TransferOperation : BaseOperation |
TransferOperationBuilder |
Builder class for transfer operation TransferOperation class TransferOperationBuilder : Builder<TransferOperation> |
WithdrawBitcoinOperation |
Transfers bitcoin asset from echo blockchain to bitcoin address class WithdrawBitcoinOperation : BaseOperation |
WithdrawERC20Operation |
Transfers value amount ethereum token erc20Token to ethereum address ethAddress class WithdrawERC20Operation : BaseOperation |
WithdrawEthereumOperation |
Transfers ethereum asset from echo blockchain to ethereum address class WithdrawEthereumOperation : BaseOperation |


Get insights that help your business grow.
Discover the benefits of data analytics and make better decisionsregarding revenue, customer experience, and overall efficiency.
Discover the benefits of data analytics and make better decisionsregarding revenue, customer experience, and overall efficiency.


{% extends 'base.html' %}
{% load i18n %}
{% block title %}{% endblock %}
{% block page_header %}
{% include "horizon/common/_domain_page_header.html" with title="Manage Endpoints" %}
{% endblock page_header %}
{% block main %}
Services
{% for service in services %}
{{ service.name.capitalize }}
{% endfor %}
Description
Select a service to see its description
{% for form in endpoints_forms %}
{% include form.template with form=form %}
{% if form.service_enabled %}
{% include "endpoints_management/endpoints_management/_disable_service_modal.html" with service_name=form.service.name %}
{% endif %}
{% endfor %}
{% include "endpoints_management/endpoints_management/_endpoints_script.html" %}
{% endblock %}


The Bluebeam Rally Team is one of the newest and most talked about competitors on the Swedish Rally Circuit.
Read MoreThe Bluebeam Rally Team is one of the newest and most talked about competitors on the Swedish Rally Circuit. Formed in 2010, Bluebeam Software has partnered with rally veterans Björn Wulff, Christian Gren, Anders Nystedt, Jonas Wennberg, Kenneth Lodeklint and Jan Österman to help push sales of its PDF solutions for design...
Read MoreThe Bluebeam Rally Team is one of the newest and most talked about competitors on the Swedish Rally Circuit.
Read MoreThe Bluebeam Rally Team is one of the newest and most talked about competitors on the Swedish Rally Circuit. Formed in 2010, Bluebeam Software has partnered with rally veterans Björn Wulff, Christian Gren, Anders Nystedt, Jonas Wennberg, Kenneth Lodeklint and Jan Österman to help push sales of its PDF solutions for design...
Read More


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3
Discrete Fourier transformation (DFT)
3.1
Deﬁnition of DFT
3.2
Periodic property of Discrete Fourier transformation
3.3
Frequency resolution and bandwidth
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{% extends "account/base.html" %} {% load i18n %} {% load account %} {% block head_title %}{% trans "Password Reset" %}{% endblock %} {% block content %}
We have sent you an e-mail. Please contact us if you do not receive it within a few minutes.
You will be redirected to Sign In page in 5 seconds.


{% extends "base.html" %} {% block content %} {% if request.user.is_authenticated %}
You have successfully completed authentication.


Jeep Gladiator And Wrangler 'Three O Five' Editions Arrive In Miami
The duo looks ready to hit the dunes of Ocean Drive.
by Gabriel VegaJeep pays homage to South Florida with the reveal of two new special editions of the Gladiator and Wrangler at the 2019 Miami Auto Show. Named ‘Three O Five’ – after Miami’s infamous ‘305’ area code – the two special edition models are based on the Sport S trim level and are available with a generous endowment of optional Jeep Performance Parts by Mopar.
Gallery: Jeep launches Gladiator and Wrangler "Three O Five" Editions in Miami
On the outside, Jeep equipped both the Wrangler and the Gladiator Three O Five with kit such as a winch and BF Goodrich off-road tires. Arguably, the most impressive piece of equipment are the two Jeeps’ unusual doors.
The Three O Five editions are equipped with metal tube frame doors painted black to match the interior of the vehicles. Like any other Wrangler and Gladiator, the doors Three O Fives’ doors are removable – complementing Miami’s laid-back, beach vibes. Given the doors lack any real protection from the elements, it’s hard to see how the climate control system will not be on at capacity most of the time, but, then again, as any South Floridian can attest, driving around with your A/C blasting is as much a part of living in the area as catching sun at the beach.
Inside, both vehicles include quilted leather seats decorated with various, model-specific small details, such as a Willys Jeep grille embroidered on the backrests. Meanwhile, audiophiles will find a 7.0-inch touchscreen infotainment system with Apple CarPlay and Android compatibility.
The fun details continue on the outside, too. The hood on both vehicles is decorated with a sticker that shows a very stylized depiction of the evolution of Jeep’s seven-slot grille – from CJ to Wrangler. These small details look great and add a lot of visual flair to the Jeeps’ familiar shapes.
All Wrangler Three O Fives will be equipped with the Sky One Touch roof; the first time this feature will be available in the Sport S trim. The Gladiator Three O Five, however, features the removable roof of the standard model. LED headlights and a Miami-themed graphics package are included with both models.
Powering the Wrangler Three O Five is a turbocharged 2.0-liter four-cylinder engine that produces 270 horsepower and 295 pound-feet of torque. Meanwhile, the Gladiator Three O Five features FCA’s 3.6-liter ‘Pentastar’ V6 engine that produces 285 hp and 260 lb-ft of torque.
 The only two color options available are Punk’n Orange and Bright White with contrasting colored stickers.
Jeep has yet to publish any pricing information for these special editions, but the standard Wrangler Sport S starts at $34,995, while the Gladiator Sport S starts at $36,745.
Source: Jeep


--- layout: archive title: Zulip Chat Archive permalink: /stream/116395-maths/topic/Result.20exists.3F.html ---
Is there any results in mathlib similar to this, int.gcd a b = int.gcd c d ↔ ∀ k, (k ∣ a) ∧ (k ∣ b) ↔ (k ∣ c) ∧ (k ∣ d)
, exist?
I guess the roadmap to proving this would be to show that away from the edge cases, the gcd is the largest non-negative int dividing both (which might be in mathlib, it sounds like a natural statement) and then use the fact that if two finite sets are equal then so is their largest element.
My gut feeling is that mathlib is a library of standard results (like the two I mentioned) but then probably it's the user's job to put them together to make statements such as this.
Hopefully there is a lemma (k ∣ a) ∧ (k ∣ b) ↔ k ∣int.gcd a b
. That already gives you one direction.
For the other direction you need to show that two nonnegative integers which divide each other are equal, which should also be a lemma
Aah yes this is a much neater way to do it.
The result Reid mentions is
theorem dvd_gcd_iff (a b c : α) : a ∣ gcd b c ↔ (a ∣ b ∧ a ∣ c)
in algebra.gcd_domain
oh I tried looking for it in euclidean_domain
The single direction exists multiple times, in algebra.euclidean_domain
and data.nat.gcd
and data.pnat.basic
The result Reid mentions is
theorem dvd_gcd_iff (a b c : α) : a ∣ gcd b c ↔ (a ∣ b ∧ a ∣ c)in
algebra.gcd_domain
How do I eventually convert from gcd a b
to int.gcd
?
The final result could be proved using int.antisymm
, but that requires int.gcd
not gcd
there is almost certainly an analogous theorem for int.gcd
Hm, this particular theorem appears to be missing, but you can reconstruct it from int.dvd_gcd
, int.gcd_dvd_left
, int.gcd_dvd_right
or you can use int.coe_gcd
to convert gcd
to int.gcd
Last updated: Apr 24 2020 at 19:02 UTC


/>
Rheumatoid Arthritis in Colombia
Rheumatoid Arthritis in Colombia
Home
Costs Analysis
Insights
About
by Juan Carlos Méndez


Hello!
This page demonstrates that client and server share modules!
In this case, both use the bar module.
Here's the result of each calling bar.bar('test'):


--- title: "Thangavelufest: Harmonic Analysis" layout: standalone css: "thangavelu-fest" ---
A hybrid symposium on Harmonic Analysis will be organized by the Department of Mathematics, Indian Institute of Science on Tuesday, 26 July 2022 on the occasion of the superannuation of Prof. S. Thangavelu.
This is part of a three-day symposium on the occasion of superannuation of three of our faculty members.
The registration process is for all three symposiums. Please register by July 15, 2022 if you wish to attend either in-person or online. If you are at IISc, please also join the Microsoft Teams Team.


{% extends "pretixpresale/event/base.html" %} {% load i18n %} {% load bootstrap3 %} {% load eventsignal %} {% load eventurl %} {% block title %}{% trans "Order details" %}{% endblock %} {% block content %} {% if "thanks" in request.GET or "paid" in request.GET %}
{% trans "Your order has been placed successfully. See below for details." %}
{% trans "Please note that we still await your payment to complete the process." %}
{% trans "Your order has been processed successfully! See below for details." %}
{% else %}{% trans "We successfully received your payment. See below for details." %}
{% endif %}{% blocktrans trimmed %} Please bookmark or save the link to this exact page if you want to download your ticket or change your details later. We also sent you an email containing the link to the address you specified. {% endblocktrans %}
{% blocktrans trimmed %}
Please save the following link if you want to download your ticket or change your details later. We
also sent you an email containing the link to the address you specified.
{% endblocktrans %}
{{ url }}
{% trans "Download all tickets at once:" %} {% for b in download_buttons %} {% if b.multi %} {{ b.text }} {% endif %} {% endfor %}
{% endif %} {% elif not download_buttons %}


Changes on this page are subject to the presence of the ?GamePassword
and ?AdminPassword
options on the
commandline when the server was started. Remove them if you want the changes on this page to have a lasting effect.
Changes on this page are subject to the presence of the ?GamePassword
and ?AdminPassword
options on the
commandline when the server was started. Remove them if you want the changes on this page to have a lasting effect.


DID YOU KNOW ?
{% for displaystudent in userupload %}
Edit
Author:{{displaystudent.author}}
{{displaystudent.description}}
Answer
Comment
{% endfor %} {% for question in question %}
User:{{question.author}}
{{question.question}}
{% for comment in comment %} {% if comment.question == question %}
{{comment.comment}}
{% endif %} {% endfor %} {% comment %}
Answer
{% endcomment %}
Discuss Here
{% endfor %}


Redirecting you automatically in to the master documentation starting page...
Or you can instead go one of the modules documentation:


Change your mindset, think big
Don't treat slivers as individual machines, but as a single distributed system (slice).
Automate everything, you don't want to manually login into any sliver.
Create large slices to compensate for offline nodes and other failures.
Make your experiments idempotent
Nodes come and go, you can not assume all your slivers being on the same homogeneous state.
Write experiments in such a way that can run on both, fresh and already deployed nodes.
Use concurrency for experiment deployment and for collecting results
You really don't want to wait for all of your offline slivers to sequentially timming out their SSH connections.
Use scripting languages like Bash. Bash integrates process management into the language itself:
cat sliver-mgmt-ip.list | while read IP; do
# This block will run concurrently, because of the ending &
{
# Kill possible running experiments
ssh -o stricthostkeychecking=no \
root@$IP 'pkill -f experiment.sh'
# Install and start on background
scp experiment.sh root@[$IP]: &&
ssh root@$IP 'nohup bash experiment.sh'
} &
done
There are specific tools that you can use for parallel execution of one-time commands.
CONFINE REST API doesn't love you too much
It's a node-oriented API, not researcher-oriented. Some essential information for your experiments may be cumbersome to obtain.
This snippet exemplifies how to get all the management IPs of your slivers in order to be able to start with your experiment.
SLICE_ID = 249
# API calls
# Requires "pip install confine-orm"
from orm.api import Api
controller = Api('https://controller.community-lab.net/api/')
slivers = controller.slivers.retrieve()
slivers.retrieve_related('node', 'slice')
# Now calculate sliver MGMT IP according to CONFINE addressing specs
# https://wiki.confine-project.eu/arch:addressing
MGMT_IPV6_PREFIX = controller.testbed_params.mgmt_ipv6_prefix
int_to_hex_str = lambda i,l: ('%.' + str(l) + 'x') % i
split_by_len = lambda s,l: [s[i:i+l] for i in range(0, len(s), l)]
for sliver in slivers.filter(slice__id=SLICE_ID):
node_id = sliver.node.id
slice_id = sliver.slice.id
for iface in sliver.interfaces:
if iface.type == 'management':
nr = '10' + int_to_hex_str(iface.nr, 2)
node_id = int_to_hex_str(node_id, 4)
slice_id = int_to_hex_str(slice_id, 12)
ipv6_words = MGMT_IPV6_PREFIX.split(':')[:3]
ipv6_words.extend([node_id, nr])
ipv6_words.extend(split_by_len(slice_id, 4))
print ':'.join(ipv6_words)
break
Pre-compile things on your own i686 virtual machine
Common sliver free space is arround ~700MB, which may be not enough for installing required dev libraries and compiling your average C++ application.
Moreover, doing so in an overcrowded Intel Atom machine will take like... I don't know... 10x more than in your high-end desktop?
Be prepared for network issues
Not all nodes have Internet connectivity, apt-get install
will not always work.
Network splits are common around community networks. You may end-up having multiple isolated experiments rather than a single one.
Common development tools and network utils are missing :(
For sure your first experiment.sh
will start with something like this (and hope for Internet connectivity being available too):
apt-get update && \
apt-get install -y inetutils-ping git traceroute tcpdump nano \
strace screen python
Also you can create your own sliver templates, but most probably you'll have to learn a few things that you didn't want to.
Did you forgot to set the slice state to start?
Me too :)


This is a modern application which assigns users a name based on their date of birth ,the day of birth and their gender. This application borrows alot from the Akan community naming system whereby children are named after the day of the week they were born.
Almost all ghaneans are named based on the day of week they were born.
The Akan people give names to their children based on the day of the week they were born.The names are also given based on gender
Akosua,Kwesi,Kwasi,Kwadwo Kwabena,Kwaku Monday: Adwoa Tuesday: Abenaa Wednesday: Akua Thursday: Yaa Friday: Afua Saturday: Ama


Class DotNetMemberExtensions
Extension methods that support model that implement the IDotNetMember interface.
Inherited Members
Namespace: CodeFactory.DotNet
Assembly: CodeFactory.DotNet.dll
Syntax
public static class DotNetMemberExtensions
Methods
| Improve this Doc View SourceFormatCSharpComparisonHashCode(IDotNetEvent, Boolean, Boolean, Boolean)
Gets the hash code for a formatted event signature using the C# format.
Declaration
public static int FormatCSharpComparisonHashCode(this IDotNetEvent source, bool includeSecurity = false, bool includeAttributes = false, bool includeKeywords = false)
Parameters
| Type | Name | Description |
|---|---|---|
| IDotNetEvent | source | The sources IDotNetEvent model. |
| Boolean | includeSecurity | Optional parameter that determines to generate security in the definition. By default this is false. |
| Boolean | includeAttributes | Optional parameter that determines if the attributes should be included in the definition. By default this is false. |
| Boolean | includeKeywords | Optional parameter that determines if all keywords are included in the definition. By default this is false. |
Returns
| Type | Description |
|---|---|
| Int32 | The hash code of the formatted model. |
Exceptions
| Type | Condition |
|---|---|
| ArgumentNullException | This is thrown if the model is null. |
FormatCSharpComparisonHashCode(IDotNetField, Boolean, Boolean, Boolean)
Gets the hash code for a formatted field signature using the C# format.
Declaration
public static int FormatCSharpComparisonHashCode(this IDotNetField source, bool includeSecurity = false, bool includeAttributes = false, bool includeKeywords = false)
Parameters
| Type | Name | Description |
|---|---|---|
| IDotNetField | source | The sources IDotNetField model. |
| Boolean | includeSecurity | Optional parameter that determines to generate security in the definition. By default this is false. |
| Boolean | includeAttributes | Optional parameter that determines if the attributes should be included in the definition. By default this is false. |
| Boolean | includeKeywords | Optional parameter that determines if all keywords other then constant are included in the definition. By default this is false. |
Returns
| Type | Description |
|---|---|
| Int32 | The has code of the formatted field. |
Exceptions
| Type | Condition |
|---|---|
| ArgumentNullException | This is thrown if the model is null. |
FormatCSharpComparisonHashCode(IDotNetMethod, Boolean, Boolean, Boolean)
Gets the hash code for a formatted method signature using the C# format.
Declaration
public static int FormatCSharpComparisonHashCode(this IDotNetMethod source, bool includeSecurity = false, bool includeAttributes = false, bool includeKeywords = false)
Parameters
| Type | Name | Description |
|---|---|---|
| IDotNetMethod | source | The sources IDotNetMethod model. |
| Boolean | includeSecurity | Optional parameter that determines to generate security in the definition. By default this is false. |
| Boolean | includeAttributes | Optional parameter that determines if the attributes should be included in the definition. By default this is false. |
| Boolean | includeKeywords | Optional parameter that determines if all keywords are included in the definition. By default this is false. |
Returns
| Type | Description |
|---|---|
| Int32 | The hash code of the formatted model. |
Exceptions
| Type | Condition |
|---|---|
| ArgumentNullException | This is thrown if the model is null. |
FormatCSharpComparisonHashCode(IDotNetProperty, Boolean, Boolean, Boolean)
Gets the hash code for a formatted property signature using the C# format.
Declaration
public static int FormatCSharpComparisonHashCode(this IDotNetProperty source, bool includeSecurity = false, bool includeAttributes = false, bool includeKeywords = false)
Parameters
| Type | Name | Description |
|---|---|---|
| IDotNetProperty | source | The sources IDotNetProperty model. |
| Boolean | includeSecurity | Optional parameter that determines to generate security in the definition. By default this is false. |
| Boolean | includeAttributes | Optional parameter that determines if the attributes should be included in the definition. By default this is false. |
| Boolean | includeKeywords | Optional parameter that determines if all keywords are included in the definition. By default this is false. |
Returns
| Type | Description |
|---|---|
| Int32 | The hash code of the formatted model. |
Exceptions
| Type | Condition |
|---|---|
| ArgumentNullException | This is thrown if the model is null. |
FormatCSharpDeclarationSyntax(IDotNetEvent, Boolean, Boolean, Boolean, Boolean)
Generates the syntax definition of an event in c# syntax.
Declaration
public static string FormatCSharpDeclarationSyntax(this IDotNetEvent source, bool includeSecurity = true, bool includeAttributes = true, bool includeKeywords = true, bool includeAbstractKeyword = false)
Parameters
| Type | Name | Description |
|---|---|---|
| IDotNetEvent | source | The source IDotNetEvent model to generate. |
| Boolean | includeSecurity | Includes the security scope which was defined in the model. |
| Boolean | includeAttributes | Includes definition of the attributes assigned to the model. |
| Boolean | includeKeywords | Includes all keywords assigned to the source model. |
| Boolean | includeAbstractKeyword | Will include the definition for the abstract keyword in the definition if it is defined. default is false. |
Returns
| Type | Description |
|---|---|
| String | Fully formatted event definition or null if the event data could not be generated. |
FormatCSharpDeclarationSyntax(IDotNetField, Boolean, Boolean, Boolean)
Generates the syntax definition of field in c# syntax. The default definition with all options turned off will return the filed signature and constants if defined and the default values.
Declaration
public static string FormatCSharpDeclarationSyntax(this IDotNetField source, bool includeSecurity = true, bool includeAttributes = true, bool includeKeywords = true)
Parameters
| Type | Name | Description |
|---|---|---|
| IDotNetField | source | The source IDotNetField model to generate. |
| Boolean | includeSecurity | Includes the security scope which the field was defined in the model. |
| Boolean | includeAttributes | Includes definition of the attributes assigned to the model. |
| Boolean | includeKeywords | Includes all keywords assigned to the field from the source model. |
Returns
| Type | Description |
|---|---|
| String | Fully formatted field definition or null if the field data could not be generated. |
FormatCSharpDeclarationSyntax(IDotNetMethod, Boolean, Boolean, Boolean, Boolean)
Generates the syntax definition of an method in c# syntax.
Declaration
public static string FormatCSharpDeclarationSyntax(this IDotNetMethod source, bool includeSecurity = true, bool includeAttributes = true, bool includeKeywords = true, bool includeAbstractKeyword = false)
Parameters
| Type | Name | Description |
|---|---|---|
| IDotNetMethod | source | The source IDotNetMethod model to generate. |
| Boolean | includeSecurity | Includes the security scope which was defined in the model. |
| Boolean | includeAttributes | Includes definition of the attributes assigned to the model. |
| Boolean | includeKeywords | Includes all keywords assigned to the source model. |
| Boolean | includeAbstractKeyword | Will include the definition for the abstract keyword in the definition if it is defined. default is false. |
Returns
| Type | Description |
|---|---|
| String | Fully formatted event definition or null if the event data could not be generated. |
FormatCSharpDeclarationSyntax(IDotNetProperty, Boolean, Boolean, Boolean, Boolean)
Generates the syntax definition of a default no backing fields property definition in c# syntax.
Declaration
public static string FormatCSharpDeclarationSyntax(this IDotNetProperty source, bool includeSecurity = true, bool includeAttributes = true, bool includeKeywords = true, bool includeAbstractKeyword = false)
Parameters
| Type | Name | Description |
|---|---|---|
| IDotNetProperty | source | The source IDotNetProperty model to generate. |
| Boolean | includeSecurity | Includes the security scope which the property was defined in the model. |
| Boolean | includeAttributes | Includes definition of the attributes assigned to the model. |
| Boolean | includeKeywords | Includes all keywords assigned to the property from the source model. |
| Boolean | includeAbstractKeyword | Will include the definition for the abstract keyword in the definition if it is defined. default is false. |
Returns
| Type | Description |
|---|---|
| String | Fully formatted property definition or null if the property data could not be generated. |
FormatCSharpMemberComparisonHashCode(IDotNetMember, MemberComparisonType)
Gets the hash code for a formatted model signature using the C# format.
Declaration
public static int FormatCSharpMemberComparisonHashCode(this IDotNetMember source, MemberComparisonType comparisonType = MemberComparisonType.Base)
Parameters
| Type | Name | Description |
|---|---|---|
| IDotNetMember | source | The sources IDotNetModel model. |
| MemberComparisonType | comparisonType | The type of comparision format to use when generating the hashcode. Default is set to the base comparision type. |
Returns
| Type | Description |
|---|---|
| Int32 | The has code of the formatted model. |
Exceptions
| Type | Condition |
|---|---|
| ArgumentNullException | This is thrown if the model is null. |


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Whitefoot the Woodmouse
by Thornton W. Burgess
"Whitefoot Finds a Hole Just in Time"
Additional Information
- Year Published: 1922
- Language: English
- Country of Origin: United States of America
- Source: Burgess T. W. (1922). Whitefoot the Woodmouse Boston: Little, Brown & Co..
-
Readability:
- Flesch–Kincaid Level: 4.5
- Word Count: 614
- Genre: Fantasy
- Keywords: 20th century literature, american literature, life lessons, nature
- ✎ Cite This
Downloads
Just in time, not just too late,
Will make you master of your fate.
—Whitefoot.
Whitefoot, half-way up that dead tree, flattened himself against the trunk and, with his heart going pit-a-pat, pit-a-pat with fright, peered around the tree at an enemy he had not seen for so long that he had quite forgotten there was such a one. It was Butcher the Shrike. Often he is called just Butcher Bird. He did not look at all terrible. He was not quite as big as Sammy Jay. He had no terrible claws like the Hawks and Owls.  But you can not always judge a person by looks, and Whitefoot knew that Butcher was one to be feared.
So his heart went pit-a-pat, pit-a-pat as he wondered if Butcher had seen him. He didn’t have to wait long to find out. Butcher flew to a tree back of Whitefoot and then straight at him. Whitefoot dodged around to the other side of the tree. Then began a dreadful game. At least, it was dreadful to Whitefoot. This way and that way around the trunk of that tree he dodged, while Butcher did his best to catch him.
Whitefoot would not have minded this so much, had he not been so tired, and had he known of a hiding-place close at hand. But he was tired, very tired, for you remember he had had what was a very long and terrible journey to him. He had felt almost too tired to climb that tree in the first place to see if it had any holes in it higher up. Now he didn’t know whether to keep on going up or to go down. Two or three times he dodged around the tree without doing either. Then he decided to go up.
Now Butcher was enjoying this game of dodge. If he should catch Whitefoot, he would have a good dinner. If he didn’t catch Whitefoot, he would simply go hungry a little longer. So you see, there was a very big difference in the feelings of Whitefoot and Butcher. Whitefoot had his life to lose, while Butcher had only a dinner to lose.
Dodging this way and dodging that way, Whitefoot climbed higher and higher. Twice he whisked around that tree trunk barely in time. All the time he was growing more and more tired, and more and more discouraged. Supposing he should find no hole in that tree!
“There must be one. There must be one,” he kept saying over and over to himself, to keep his courage up. “I can’t keep dodging much longer. If I don’t find a hole pretty soon, Butcher will surely catch me. Oh, dear! Oh, dear!”
Just above Whitefoot was a broken branch. Only the stub of it remained. The next time he dodged around the trunk he found himself just below that stub. Oh, joy! There, close under that stub, was a round hole. Whitefoot didn’t hesitate a second. He didn’t wait to find out whether or not any one was in that hole. He didn’t even think that there might be some one in there.  He was just in time. He was just in the nick of time. Butcher struck at him and just missed him as he disappeared in that hole. Whitefoot had saved his life and Butcher had missed a dinner.


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Slicr is a metadata search engine that searches for LINCS L1000 gene expression profiles and signatures matching user's input parameters. It features download of selected search results as csv files in a zipped folder and visualization of selected results in a 3D scatter plot using PCA or MDS. Slicr consists of three views: the search view, the checkout view and the 3D scatter view.


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Changeset 364
- Timestamp:
- 08/02/10 09:30:58 (6 years ago)
- Location:
- branches/alta
- Files:
-
- 2 added
- 4 edited
-
INSTALLATION_INSTRUCTIONS.txt (added)
-
mystic-0.2a1/augmented_lagrangian_constraints.py (modified) (1 diff)
-
mystic-0.2a1/constraint_tools.py (modified) (5 diffs)
-
mystic-0.2a1/test_constraints_rosen2d.py (modified) (3 diffs)
-
mystic-0.2a1/test_constraints_tp6.py (modified) (1 diff)
-
setup_mystic-0.2a1_alta.py (added)
Legend:
- Unmodified
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-
branches/alta/mystic-0.2a1/augmented_lagrangian_constraints.py
r362 r364 16 16 17 17 Inputs: 18 constraints_string -- String of constraints 18 constraints_string -- String of constraints. See constraints_input_info.txt for 19 details. 19 20 ndim -- len(x) 20 21 costfunc -- cost function to minimize -
branches/alta/mystic-0.2a1/constraint_tools.py
r362 r364 988 988 #------------------------------------------------------------------ 989 989 def matrix_to_symbolic(A=None, b=None, G=None, h=None): 990 """Allow linear equality and inequality constraints to be input in 991 the form of matrices, and convert them to a symbolic string. 992 Input as keywords A, b, G, h, 990 """Convert linear equality and inequality constraints from matrices to a 991 symbolic string for input into the "constraints" keywords in the Solve() 992 method of the Mystic solvers. 993 994 Input: 995 Keywords A, b, G, h: can specify either A and b; G and h; or A, b, G, and h, 993 996 where Ax <= b and Gx = h. 994 Can specify either A and b; G and h; or A, b, G, and h. 997 995 998 A, b, G, and h must all be of type list or numpy array. 996 Returns the constraints in a string form that can be directly input 997 into the "constraints" keyword in Solve(). 998 Notes on using the symbolic string: 999 1000 Example: 1001 A = [[3., 4., 5.], 1002 [1., 6., -9.]] 1003 b = [0., 0.] 1004 G = [1., 0., 0.] 1005 h = [5.] 1006 1007 Output: 1008 Returns a string that can be directly input into the "constraints" keyword 1009 in Solve(). 1010 1011 Tips on using the symbolic string: 999 1012 * When inputting the constraints, ignore the keywords "varname" and 1000 "varnamelist". """1001 #XXX Should check dimensions and give warnings if incorrect? 1013 "varnamelist". The default, 'x', is used.""" 1014 1002 1015 ineqstring = "" 1003 1016 if A != None and b != None: 1017 # If one-dimensional and not in a nested list, add a list layer 1004 1018 try: 1005 1019 ndim = len(A[0]) … … 1007 1021 ndim = len(A) 1008 1022 A = [A] 1023 1024 # Check dimensions and give errors if incorrect. 1025 if len(A) != len(b): 1026 raise Exception("Dimensions of A and b are not consistent.") 1027 1028 # 'matrix multiply' and form the string 1009 1029 for i in range(len(b)): 1010 1030 Asum = "" … … 1014 1034 eqstring = "" 1015 1035 if G != None and h != None: 1036 # If one-dimensional and not in a nested list, add a list layer 1016 1037 try: 1017 1038 ndim = len(G[0]) … … 1019 1040 ndim = len(G) 1020 1041 G = [G] 1042 1043 # Check dimensions and give errors if incorrect. 1044 if len(G) != len(h): 1045 raise Exception("Dimensions of G and h are not consistent.") 1046 1047 # 'matrix multiply' and form the string 1021 1048 for i in range(len(h)): 1022 1049 Gsum = "" … … 1025 1052 eqstring += Gsum.rstrip(' + ') + '=' + str(h[i]) + '\n' 1026 1053 totalconstraints = ineqstring + eqstring 1027 return totalconstraints #XXX Better to return constraints function?1054 return totalconstraints 1028 1055 1029 1056 #------------------------------------------------------------------ -
branches/alta/mystic-0.2a1/test_constraints_rosen2d.py
r362 r364 169 169 x0 = [0., 0.75] # feasible. 170 170 #x0 = [-1., 1.] 171 #x0 = [1., 1.] # infeasible, and the unconstrained minimum! 171 172 172 173 constraints_string = """ … … 823 824 test_powelldirectional_penalty() 824 825 test_bfgs_penalty() 825 test_ncg_penalty() # bad answer. adjusting penalty/term doesn't really help 826 test_ncg_penalty() # bad answer. adjusting penalty/term doesn't really help. 827 # works well with x0=[1., 1.] though. 826 828 test_cg_penalty() 827 829 … … 831 833 test_powelldirectional_auglag() 832 834 test_bfgs_auglag() 833 test_ncg_auglag() # gets stuck without the hack in scipy_ncg 835 test_ncg_auglag() # gets stuck without the hack in scipy_ncg. continues to 836 # get stuck with x0=[1., 1.] 834 837 test_cg_auglag() 835 838 -
branches/alta/mystic-0.2a1/test_constraints_tp6.py
r362 r364 223 223 from mystic.termination import VTR 224 224 from mystic.termination import ChangeOverGeneration as COG 225 from mystic.termination import NormalizedChangeOverGeneration as NCOG 225 226 solver = NelderMeadSimplexSolver(ndim) 226 227 227 228 solver.SetInitialPoints(x0) 228 229 solver.enable_signal_handler() 229 term = VTR() 230 #term = COG() 230 term = VTR() # does not work well 231 #term = COG() # does not work well 232 #term = NCOG() # does not work well 231 233 solver.Solve(costfunc, term, constraints=constraints_string, \ 232 234 constraints_method='penalty')
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