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| PREV NEXT | FRAMES NO FRAMES |
| Classes in com.ibm.webest.persistence.model used by com.ibm.webest.gui.action | |
|---|---|
| ApplicationType
Entity implementation class for Entity: ApplicationType Application types are grouped by application type groups. |
|
| ApplicationTypeGroup
Entity implementation class for Entity: ApplicationTypeGroup Groups application type entities. |
|
| Certainty
Entity implementation class for Entity: Certanity |
|
| Constraint
Entity implementation class for Entity: Constraint |
|
| Country
Entity implementation class for Entity: Country Represents a country with ISO 3166-1 country code and name. |
|
| Division
Entity implementation class for Entity: Division |
|
| EffortUnit
Entity implementation class for Entity: EffortUnit Measuring unit for effort (e.g. person days or person hours) used in the project. |
|
| Estimate
Entity implementation class for Entity: Estimate |
|
| GranularityLevel
Entity implementation class for Entity: GranularityLevel |
|
| GranularityQuestion
Entity implementation class for Entity: Solution |
|
| IndustrySector
Entity implementation class for Entity: IndustrySector Industry sectors are grouped by industry sector groups. |
|
| IndustrySectorGroup
Entity implementation class for Entity: IndustryGroup Groups industry sectors. |
|
| MonetaryUnit
Entity implementation class for Entity: MonetaryUnit Currency used in the project. |
|
| MTTDTimeUnit
Entity implementation class for Entity: MTTDTimeUnit Mean time to defect time unit used in the project. |
|
| Organization
Entity implementation class for Entity: Organization |
|
| Phase
Entity implementation class for Entity: Phase A project can define different phases. |
|
| Solution
Entity implementation class for Entity: Solution |
|
| StaffingShape
Entity implementation class for Entity: StaffingShape |
|
| Template
Entity implementation class for Entity: Template Predefined values for the project environment. |
|
| TreeItem
This interface provides the functionality for entities to be represented in a hierarchical form. |
|
| UseCase
Entity implementation class for Entity: UseCase |
|
| UseCaseComplexity
Entity implementation class for Entity: UseCaseComplexity |
|
| UseCasePack
Entity implementation class for Entity: UseCasePack |
|
| User
Entity implementation class for Entity: User |
| Classes in com.ibm.webest.persistence.model used by com.ibm.webest.gui.model | |
|---|---|
| PiHistoryCategory
Entity implementation class for Entity: PiHistoryCategory |
|
| PiHistoryEntry
Entity implementation class for Entity: piHistoryEntry |
|
| UseCase
Entity implementation class for Entity: UseCase |
|
| UseCasePack
Entity implementation class for Entity: UseCasePack |
| Classes in com.ibm.webest.persistence.model used by com.ibm.webest.gui.utils | |
|---|---|
| Estimate
Entity implementation class for Entity: Estimate |
|
| GranularityQuestion
Entity implementation class for Entity: Solution |
|
| MeasuringUnit
Entity implementation class for Entity: MeasuringUnit Abstract entity for different measuring units. |
|
| Solution
Entity implementation class for Entity: Solution |
|
| User
Entity implementation class for Entity: User |
| Classes in com.ibm.webest.persistence.model used by com.ibm.webest.persistence.model | |
|---|---|
| ApplicationType
Entity implementation class for Entity: ApplicationType Application types are grouped by application type groups. |
|
| ApplicationTypeGroup
Entity implementation class for Entity: ApplicationTypeGroup Groups application type entities. |
|
| Certainty
Entity implementation class for Entity: Certanity |
|
| Constraint
Entity implementation class for Entity: Constraint |
|
| Country
Entity implementation class for Entity: Country Represents a country with ISO 3166-1 country code and name. |
|
| DefectCategory
Entity implementation class for Entity: DefectCategory Primary key is id and owner. |
|
| Division
Entity implementation class for Entity: Division |
|
| EffortUnit
Entity implementation class for Entity: EffortUnit Measuring unit for effort (e.g. person days or person hours) used in the project. |
|
| Estimate
Entity implementation class for Entity: Estimate |
|
| GranularityLevel
Entity implementation class for Entity: GranularityLevel |
|
| GranularityQuestion
Entity implementation class for Entity: Solution |
|
| IndustrySector
Entity implementation class for Entity: IndustrySector Industry sectors are grouped by industry sector groups. |
|
| IndustrySectorGroup
Entity implementation class for Entity: IndustryGroup Groups industry sectors. |
|
| MeasuringUnit
Entity implementation class for Entity: MeasuringUnit Abstract entity for different measuring units. |
|
| Milestone
Entity implementation class for Entity: Milestone A project can define different milestone. |
|
| MonetaryUnit
Entity implementation class for Entity: MonetaryUnit Currency used in the project. |
|
| MTTDTimeUnit
Entity implementation class for Entity: MTTDTimeUnit Mean time to defect time unit used in the project. |
|
| Organization
Entity implementation class for Entity: Organization |
|
| Phase
Entity implementation class for Entity: Phase A project can define different phases. |
|
| PiHistoryCategory
Entity implementation class for Entity: PiHistoryCategory |
|
| PiHistoryEntry
Entity implementation class for Entity: piHistoryEntry |
|
| ProjectEnvironment
Entity implementation class for Entity: ProjectEnvironment |
|
| Role
Entity implementation class for Entity: Role Each user is assigned to a role. |
|
| Solution
Entity implementation class for Entity: Solution |
|
| SolutionStaffingShape
Entity implementation class for Entity: SolutionStaffingShape |
|
| StaffingShape
Entity implementation class for Entity: StaffingShape |
|
| Template
Entity implementation class for Entity: Template Predefined values for the project environment. |
|
| TemplateValues
Entity implementation class for Entity: TemplateValues Container for values used in templates and project environments. |
|
| TreeItem
This interface provides the functionality for entities to be represented in a hierarchical form. |
|
| UseCase
Entity implementation class for Entity: UseCase |
|
| UseCaseComplexity
Entity implementation class for Entity: UseCaseComplexity |
|
| UseCasePack
Entity implementation class for Entity: UseCasePack |
|
| User
Entity implementation class for Entity: User |
| Classes in com.ibm.webest.persistence.model used by com.ibm.webest.persistence.service | |
|---|---|
| ApplicationTypeGroup
Entity implementation class for Entity: ApplicationTypeGroup Groups application type entities. |
|
| Certainty
Entity implementation class for Entity: Certanity |
|
| Country
Entity implementation class for Entity: Country Represents a country with ISO 3166-1 country code and name. |
|
| Division
Entity implementation class for Entity: Division |
|
| EffortUnit
Entity implementation class for Entity: EffortUnit Measuring unit for effort (e.g. person days or person hours) used in the project. |
|
| Estimate
Entity implementation class for Entity: Estimate |
|
| GranularityLevel
Entity implementation class for Entity: GranularityLevel |
|
| GranularityQuestion
Entity implementation class for Entity: Solution |
|
| IndustrySectorGroup
Entity implementation class for Entity: IndustryGroup Groups industry sectors. |
|
| MeasuringUnit
Entity implementation class for Entity: MeasuringUnit Abstract entity for different measuring units. |
|
| MonetaryUnit
Entity implementation class for Entity: MonetaryUnit Currency used in the project. |
|
| MTTDTimeUnit
Entity implementation class for Entity: MTTDTimeUnit Mean time to defect time unit used in the project. |
|
| Organization
Entity implementation class for Entity: Organization |
|
| PiHistoryCategory
Entity implementation class for Entity: PiHistoryCategory |
|
| ProjectEnvironment
Entity implementation class for Entity: ProjectEnvironment |
|
| Solution
Entity implementation class for Entity: Solution |
|
| StaffingShape
Entity implementation class for Entity: StaffingShape |
|
| Template
Entity implementation class for Entity: Template Predefined values for the project environment. |
|
| TemplateValues
Entity implementation class for Entity: TemplateValues Container for values used in templates and project environments. |
|
| UseCaseComplexity
Entity implementation class for Entity: UseCaseComplexity |
|
| User
Entity implementation class for Entity: User |
| Classes in com.ibm.webest.persistence.model used by com.ibm.webest.persistence.service.impl | |
|---|---|
| ApplicationTypeGroup
Entity implementation class for Entity: ApplicationTypeGroup Groups application type entities. |
|
| Certainty
Entity implementation class for Entity: Certanity |
|
| Country
Entity implementation class for Entity: Country Represents a country with ISO 3166-1 country code and name. |
|
| Division
Entity implementation class for Entity: Division |
|
| EffortUnit
Entity implementation class for Entity: EffortUnit Measuring unit for effort (e.g. person days or person hours) used in the project. |
|
| Estimate
Entity implementation class for Entity: Estimate |
|
| GranularityLevel
Entity implementation class for Entity: GranularityLevel |
|
| GranularityQuestion
Entity implementation class for Entity: Solution |
|
| IndustrySectorGroup
Entity implementation class for Entity: IndustryGroup Groups industry sectors. |
|
| MeasuringUnit
Entity implementation class for Entity: MeasuringUnit Abstract entity for different measuring units. |
|
| MonetaryUnit
Entity implementation class for Entity: MonetaryUnit Currency used in the project. |
|
| MTTDTimeUnit
Entity implementation class for Entity: MTTDTimeUnit Mean time to defect time unit used in the project. |
|
| Organization
Entity implementation class for Entity: Organization |
|
| PiHistoryCategory
Entity implementation class for Entity: PiHistoryCategory |
|
| Solution
Entity implementation class for Entity: Solution |
|
| StaffingShape
Entity implementation class for Entity: StaffingShape |
|
| Template
Entity implementation class for Entity: Template Predefined values for the project environment. |
|
| UseCaseComplexity
Entity implementation class for Entity: UseCaseComplexity |
|
| User
Entity implementation class for Entity: User |
| Classes in com.ibm.webest.persistence.model used by com.ibm.webest.persistence.validation | |
|---|---|
| Solution
Entity implementation class for Entity: Solution |
|
| TemplateValues
Entity implementation class for Entity: TemplateValues Container for values used in templates and project environments. |
| Classes in com.ibm.webest.persistence.model used by com.ibm.webest.processing.administration | |
|---|---|
| ApplicationTypeGroup
Entity implementation class for Entity: ApplicationTypeGroup Groups application type entities. |
|
| Certainty
Entity implementation class for Entity: Certanity |
|
| Country
Entity implementation class for Entity: Country Represents a country with ISO 3166-1 country code and name. |
|
| EffortUnit
Entity implementation class for Entity: EffortUnit Measuring unit for effort (e.g. person days or person hours) used in the project. |
|
| Estimate
Entity implementation class for Entity: Estimate |
|
| GranularityLevel
Entity implementation class for Entity: GranularityLevel |
|
| GranularityQuestion
Entity implementation class for Entity: Solution |
|
| IndustrySectorGroup
Entity implementation class for Entity: IndustryGroup Groups industry sectors. |
|
| MonetaryUnit
Entity implementation class for Entity: MonetaryUnit Currency used in the project. |
|
| MTTDTimeUnit
Entity implementation class for Entity: MTTDTimeUnit Mean time to defect time unit used in the project. |
|
| Organization
Entity implementation class for Entity: Organization |
|
| PiHistoryCategory
Entity implementation class for Entity: PiHistoryCategory |
|
| ProjectEnvironment
Entity implementation class for Entity: ProjectEnvironment |
|
| Solution
Entity implementation class for Entity: Solution |
|
| StaffingShape
Entity implementation class for Entity: StaffingShape |
|
| Template
Entity implementation class for Entity: Template Predefined values for the project environment. |
|
| UseCaseComplexity
Entity implementation class for Entity: UseCaseComplexity |
|
| User
Entity implementation class for Entity: User |
| Classes in com.ibm.webest.persistence.model used by com.ibm.webest.processing.calculation | |
|---|---|
| GranularityLevel
Entity implementation class for Entity: GranularityLevel |
|
| GranularityQuestion
Entity implementation class for Entity: Solution |
|
| Solution
Entity implementation class for Entity: Solution |
|
| UseCase
Entity implementation class for Entity: UseCase |
| Classes in com.ibm.webest.persistence.model used by com.ibm.webest.processing.impl.administration | |
|---|---|
| ApplicationTypeGroup
Entity implementation class for Entity: ApplicationTypeGroup Groups application type entities. |
|
| Certainty
Entity implementation class for Entity: Certanity |
|
| Country
Entity implementation class for Entity: Country Represents a country with ISO 3166-1 country code and name. |
|
| EffortUnit
Entity implementation class for Entity: EffortUnit Measuring unit for effort (e.g. person days or person hours) used in the project. |
|
| Estimate
Entity implementation class for Entity: Estimate |
|
| GranularityLevel
Entity implementation class for Entity: GranularityLevel |
|
| GranularityQuestion
Entity implementation class for Entity: Solution |
|
| IndustrySectorGroup
Entity implementation class for Entity: IndustryGroup Groups industry sectors. |
|
| MonetaryUnit
Entity implementation class for Entity: MonetaryUnit Currency used in the project. |
|
| MTTDTimeUnit
Entity implementation class for Entity: MTTDTimeUnit Mean time to defect time unit used in the project. |
|
| Organization
Entity implementation class for Entity: Organization |
|
| PiHistoryCategory
Entity implementation class for Entity: PiHistoryCategory |
|
| ProjectEnvironment
Entity implementation class for Entity: ProjectEnvironment |
|
| Solution
Entity implementation class for Entity: Solution |
|
| StaffingShape
Entity implementation class for Entity: StaffingShape |
|
| Template
Entity implementation class for Entity: Template Predefined values for the project environment. |
|
| UseCaseComplexity
Entity implementation class for Entity: UseCaseComplexity |
|
| User
Entity implementation class for Entity: User |
| Classes in com.ibm.webest.persistence.model used by com.ibm.webest.processing.impl.calculation | |
|---|---|
| GranularityLevel
Entity implementation class for Entity: GranularityLevel |
|
| GranularityQuestion
Entity implementation class for Entity: Solution |
|
| Solution
Entity implementation class for Entity: Solution |
|
| UseCase
Entity implementation class for Entity: UseCase |
| Classes in com.ibm.webest.persistence.model used by com.ibm.webest.processing.model | |
|---|---|
| Milestone
Entity implementation class for Entity: Milestone A project can define different milestone. |
|
| Phase
Entity implementation class for Entity: Phase A project can define different phases. |
|
| Solution
Entity implementation class for Entity: Solution |
|
| TreeItem
This interface provides the functionality for entities to be represented in a hierarchical form. |
| Classes in com.ibm.webest.persistence.model used by com.ibm.webest.webservice | |
|---|---|
| Estimate
Entity implementation class for Entity: Estimate |
|
|||||||||||
| PREV NEXT | FRAMES NO FRAMES |


You've told us that the worker's business could arrange and pay for a substitute to work on this engagement, and that the end client would accept it. This suggests that the end client is engaging the worker on a business to business basis, rather than on a personal service basis.
If HMRC investigate this engagement, the end client will need to confirm that they would accept substitution. They may also be asked to demonstrate that they have accepted substitution during this, or a similar engagement.
It is important to demonstrate that it is the worker's business that will pay, and provide the substitute, not the agency, and not the end client


Grand Master Rains:
You who walk the path of courage, be welcome here. I am Rains, head of the Warrior Guild in this place. If you wish to learn about class transfers, you've come to the right place.
Listen to information about first class transfer.
Clan.
Alliance.
Quest.


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---
start: 2018-05-02 13:00
edition: 38
participate: "https://www.eventbrite.com/e/blockchain-beyond-bitcoin-tickets-45334107536"
talk: "https://goo.gl/forms/m9aPSXemMLPBj35k1"
subheader: "Blockchain Beyond Bitcoin"
subnote: ""
agenda:
-
time: "6:30 pm - 7:30 pm"
title: "Blockchain Beyond Bitcoin"
subtitle: "Blockchain is more than just Bitcoin. In this talk the speaker will try to introduce the concepts around Blockchain, how is constructed and its unique features using Ethereum. We will start with basic concepts of Blockchain, Proof of work, Mining and Smart Contracts then look at the current challenges Blockchain is facing and what community is thinking about them and end the talk by going through a set of interesting applications of Blockchain"
speaker_profile: "By: Sreedhar Joshi, is a technologist with over 15 years of experience primarily interested in building scalable and evolvable software. His current areas of interests include Data Engineering and Blockchain."
image: "sreedhar_joshi.jpg"
-
time: "7:30 pm - 7:45 pm"
title: "Break & Networking"
-
time: "7:45 pm - 8:30 pm"
title: "Continued..."
---


This simple walkthrough will explain the research study, the impact it may have on your life and will allow you to provide your consent to participate.


Note
This tutorial was generated from a Jupyter notebook that can be downloaded here.
The pixel transform¶
Even though it is convenient to define surface maps in the spherical harmonic basis (because it allows us to compute fluxes analytically), the spherical harmonic basis does have some downsides. The main one relates to the fact that it’s really hard to ensure positivity of a surface map when we’re in the \(Y_{lm}\) basis. Since spherical harmonics are polynomials on the surface of the sphere, ensuring positivity of a degree \(l\) spherical harmonic expansion is equivalent to ensuring a polynomial of the same total degree has no roots, which isn’t trivial.
It’s much easier to ensure positivity in pixel space, i.e., on a discrete grid on the surface of the sphere. This notebook discusses how to use the get_pixel_tranforms
method to obtain the linear operators that transform back and forth between pixels (on a Mollweide grid) and spherical harmonics.
[3]:
import starry
import matplotlib.pyplot as plt
starry.config.lazy = False
starry.config.quiet = True
We begin by instantiating a map of the Earth at \(l = 20\):
[4]:
map = starry.Map(20)
map.load("earth", sigma=0.075)
y0 = np.array(map.y)
fig, ax = plt.subplots(1, figsize=(12, 5))
map.show(ax=ax, projection="rect", colorbar=True)
Now let’s get the pixel transform on a Mollweide grid:
[5]:
lat, lon, Y2P, P2Y, Dx, Dy = map.get_pixel_transforms()
Here are the two matrices that transform spherical harmonics to pixels and pixels to spherical harmonics, respectively:
[6]:
fig, ax = plt.subplots(1, 2, figsize=(15, 7))
ax[0].imshow(np.log10(np.abs(Y2P)), vmin=-10)
ax[1].imshow(np.log10(np.abs(P2Y)), vmin=-10)
ax[0].set(xticks=[], yticks=[], xlabel=r"$N_{ylm}$", ylabel=r"$N_{pix}$", title="Y2P")
ax[1].set(xticks=[], yticks=[], xlabel=r"$N_{pix}$", ylabel=r"$N_{ylm}$", title="P2Y");
Let’s get the pixel representation of our map…
[7]:
p = Y2P.dot(map.y)
… and visualize this vector on a rectangular lat/lon grid:
[8]:
fig, ax = plt.subplots(1, figsize=(12, 5))
im = ax.scatter(lon, lat, s=300, c=p, alpha=0.5, ec="none", cmap="plasma")
plt.colorbar(im)
ax.set_xlim(-190, 190)
ax.set_ylim(-90, 90)
ax.set_xlabel("Longitude [deg]")
ax.set_ylabel("Latitude [deg]");
That’s the forward transform. We can now transform back to spherical harmonics and see what we get:
[9]:
y = P2Y.dot(p)
Here’s the difference between the original spherical harmonic vector and the vector after the full cycle of transformations. Because of numerics (and a small regularization term in the inversion), the transform isn’t exactyl one-to-one, but it’s close.
[10]:
plt.plot(np.abs((y0 - y) / y0))
plt.yscale("log")
plt.ylabel("difference")
plt.xlabel("spherical harmonic index");
We can also plot the new map using starry
:
Note
Maps in starry
require the coefficient of the \(Y_{0,0}\) term to be unity.
In order to ingest the new \(Y_{lm}\) coefficients into starry
, we divide them by the
\(Y_{0,0}\) term then set the map amplitude equal to the \(Y_{0,0}\) term to get the
correct scaling.
[11]:
map[1:, :] = y[1:] / y[0]
map.amp = y[0]
fig, ax = plt.subplots(1, figsize=(12, 5))
map.show(ax=ax, projection="rect", colorbar=True)
Differentiation¶
We can also obtain the derivatives of the pixel representation with respect to longitude and latitude via a purely linear operation:
[12]:
# longitude derivative
Dxp = Dx.dot(p)
Dxp_y = P2Y.dot(Dxp)
map[1:, :] = Dxp_y[1:] / Dxp_y[0]
map.amp = Dxp_y[0]
fig, ax = plt.subplots(1, figsize=(12, 5))
map.show(ax=ax, projection="rect", colorbar=True)
[13]:
# latitude derivative
Dyp = Dy.dot(p)
Dyp_y = P2Y.dot(Dyp)
map[1:, :] = Dyp_y[1:] / Dyp_y[0]
map.amp = Dyp_y[0]
fig, ax = plt.subplots(1, figsize=(12, 5))
map.show(ax=ax, projection="rect", colorbar=True)


| DOSE: T+ 0:00 |
3.5 g |
oral |
Mushrooms |
(plant material) |
| T+ 1:00 |
3.5 g |
oral |
Mushrooms |
(plant material) |
It had been a while since I last used mushrooms, so I was feeling a strong urge to trip again. This time, however, I wanted to trip alone. I've heard that solo trips are much more spiritual, generally, than trips with friends. So, I go to my friend who recently went shroom hunting and buy a quarter off him. The week goes by, and finally it's Friday night.
I decided to take the shrooms around 10pm, so I could safely go outside without being seen by anyone. The time comes, and I chew up an 1/8 ounce, thinking this is all I would need. However, an hour went by and I felt nothing, beside the sort of spaced-out feeling you get with shrooms.  About 15 minutes after eating the second 1/8 and smoking the bowl, the effects kick in. Hard. I look around the room and everything is growing and shrinking, breathing and morphing. The patterns on the bedsheet start shifting and moving around. I can't focus on anything at all, I just stare mindlessly at the room.
Suddenly, I had a great surge of energy, and decided to go outside. However, I was having a difficult time walking.  When I got to the door, I saw my two cats eagerly awaiting to go outside. I smiled uncontrollably at the two furry creatures, and released them from the confines of my kitchen. They run outside, and I stumble along behind them. This is where everything went insane. It's been around 30 minutes since I ate the second 1/8, even though it's felt like two hours.
Have you ever been to a rave and seen people liquid dancing? That's essentially what I was doing, only it was uncontrollable. The only way for me to walk was by liquiding around. I liquided around outside for a few minutes, then I looked down on the patio about 10 feet in front of me and noticed a tiny, baby pill bug. I was extremely fascinated by it, and got down on the ground and squirmed my way over to it. I pick it up and start talking to it, letting it walk over my hand. It converses back to me, asking me what I'm doing. We talk for a few hours, in reality a few minutes, then I set it down and stand up again.
My cats had wandered off about 50 feet away from me, so I decide to go play with them. I liquid over to them, get back on the ground and start rolling around. They get curious and come up to me, rubbing their heads against mine and purring extremely loudly. Again, I felt an uncontrollable surge of happiness, and I start laughing. I stand back up and they walk away, heading toward the street. I lost focus of them and started looking at the pool.
The water had become alive, every particle shimmering with energy. My eyes get locked on the water, when I suddenly heard a dead leaf roll by. I become intensely interested in this leaf, and follow it with my eyes as it flows gently along it's path. It ends up going down to the street, to where my cats are. I decided to risk it, hoping no one was outside, and liquided my way down to my cats again. All this while, ever since I went outside, I've been breathing VERY VERY deep and loudly, just to feel the cool air rubbing against my lips, going into my mouth and lungs, and to hear the noise it makes while doing so. My cats are playing, chasing each other around the yard. Suddenly, I felt like I was a cat, so I got down on my hands and knees and started chasing them around. They weren't scared at all, and let me enjoy their little game.
It's been about an hour since the effects kicked in. It felt like it had been longer, but I know that's due to the drug. Suddenly, I sat down on the side of the street, and look at the trees. They formed an intricate, continuously moving and evolving fractal pattern. I lay back and look up at the sky, listening to all the noises, watching the clouds gently fly over. I could HEAR the universe. I could hear the atmosphere shifting and moving and bending and breaking, as the earth hurled through space. As I was lying on the ground, I felt as if I was in the very front of the earth, as if I was controlling it's flight through the cosmos. I felt like the earth was my backpack, my engine, guiding me through the heavens.
I laid there for what I believe was about an hour, although, again, it felt much longer, absorbing the universe into me. Eventually, I remember that my cats are still outside, and decide to find them to let them back inside. It was also getting very cold outside, and my fingers were numbing up. I felt a connection to the world as I've never felt before, and I wished to never leave the outdoors, yet I felt that I had to go back inside, for myself and for my cats. So, I liquid my way back up, and I see my cats waiting for me to let them back inside. I open the door, step in, and close it again. The noise of the world was suddenly gone, replaced by a warm, strangely comforting silence. I stumble back upstairs and plop down in front of my computer, to message anyone I possibly could about the experience I had. Only one of my friends was on, so I decide to message him. However, the only thing I could say was 'I exisssst'.
Gradually I come down, listening to Bhudda Bar and Shpongle. The trip lasted only around 4 hours, but it was very intense, very enjoyable.
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.


Basics of Machine Learning Series
Introduction
For a given dataset, PCA tries to find a lower dimensional surface onto which these points can be projected while minimizing the approximation losses. For example consider the dataset (marked by blue dots’s) in \(\mathbb{R}^2\) in the the plot below. The line formed by the red x’s is the projection of the data from \(\mathbb{R}^2\) to \(\mathbb{R}\).
Implementation
# library imports
import matplotlib.pyplot as plt
import numpy as np
from sklearn.decomposition import PCA
# random data generation
X = np.zeros((50, 2))
X[:, 0] = np.linspace(0, 50)
X[:, 1] = X[:, 0]
X = X + 5*np.random.randn(X.shape[0], X.shape[1])
# applying PCA on data
# same number of dimensions will help visualize components
pca = PCA(2)
# reduced number of dimensions will help understand projections
pca_reduce = PCA(1)
# projection on new components found
X_proj = pca_reduce.fit_transform(X)
# rebuilding the data back to original space
X_rebuild = pca_reduce.inverse_transform(X_proj)
X_proj = pca.fit_transform(X)
plt.figure(figsize=(7, 7))
# plot data and projection
plt.scatter(X[:,0], X[:, 1], alpha=0.5, c='green')
plt.scatter(X_rebuild[:, 0], X_rebuild[:, 1], alpha=0.3, c='r')
# plot the components
soa = np.hstack((
np.ones(pca.components_.shape) * pca.mean_,
pca.components_ * np.atleast_2d(
# components scaled to the length of their variance
np.sqrt(pca.explained_variance_)
).transpose()
))
x, y, u, v = zip(*soa)
ax = plt.gca()
ax.quiver(
x, y, u, v,
angles='xy',
scale_units='xy',
scale=0.5,
color='rb'
)
plt.axis('scaled')
plt.draw()
plt.legend([
'original',
'projection'
])
# plot the projection errors
for p_orig, p_proj in zip(X, X_rebuild):
plt.plot([p_orig[0], p_proj[0]], [p_orig[1], p_proj[1]], c='g', alpha=0.3)
plt.show()
From the above plot it is easier to point out what exactly PCA is doing. The green points show the original data. So all PCA is trying to do is to find the orthogonal components along which the eigenvalues are maximized which is basically a fancy way of saying that PCA finds a feature set in the order of decreasing variance for a given dataset. In the above example, the red vector is displaying higher variance and is the first component, while the blue vector is displaying relatively less variance.
Performing PCA for number of components greater than the current number of dimensions is useless as the data is preserved with 100% variance in the current dimension and no new dimension can help enhance that metric.
So, when dimensionality reduction is done using PCA as can be seen in the red dots, the projection is done along the more dominant feature among the two as it is more representative of the data among the two dimensions. Also, it can be seen that the red vector lies on a line than minimizes the projection losses represented by the green lines from the original data point to the projected data points.
Mean Normalization and feature scaling are a must before performing the PCA, so that the variance of a component is not affected by the disparity in the range of values.
Generalizing to n-dimensional data the same technique can be used to reduce the data to k-dimensions in a similar way by finding the hyper-surface with least projection error.
Projection vs Prediction
PCA is not Linear Regression.
In linear regression, the aim is predict a given dependent variable, \(y\) based on independent variables, \(x\), i.e. minimize the prediction error. In contrast, PCA does not have a target variable, \(y\), it is mere feature reduction by minimizing the projection error. The difference is clear from the plot in Fig-3.
# import the sklearn model
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(X[:,0].reshape(-1, 1), X[:, 1])
# coef_ gives the regression coefficients
y_pred = X[:,0] * lin_reg.coef_
plt.figure(figsize=(7, 7))
# plot data and projection
plt.scatter(X[:,0], X[:, 1], alpha=0.5, c='green')
plt.scatter(X_rebuild[:, 0], X_rebuild[:, 1], alpha=0.3, c='r')
plt.scatter(X[:,0], y_pred, alpha=0.5, c='blue')
# plot the projection errors
for p_orig, p_proj in zip(X, X_rebuild):
plt.plot([p_orig[0], p_proj[0]], [p_orig[1], p_proj[1]], c='r', alpha=0.3)
# plot the prediction errors
for p_orig, y in zip(X, np.hstack((X[:,0].reshape(-1, 1), y_pred.reshape(-1, 1)))):
plt.plot([p_orig[0], y[0]], [p_orig[1], y[1]], c='b', alpha=0.3)
The blue points display the prediction based on linear regression, while the red points display the projection on the reduced dimension. The optimization objectives of the two algorithms are different. While linear regression is trying to minimize the squared errors represented by the blue lines, PCA is trying to minimize the projection errors represented by the red lines.
Mean Normalization and Feature Scaling
It is important to have both these steps in the preprocessing, before PCA is applied. The mean of any feature in a design matrix can be calculated by,
Following the calculation of means, the normalization can be done by replacing each \(x_j\) by \(x_j - \mu_j\). Similarly, feature scaling is done by replacing each \(x_j^{(i)}\) by,
where \(s_j\) is some measure of the range of values of feature \(j\). It can be \(max(x_j) - min(x_j)\) or more commonly the standard deviation of the feature.
PCA Algorithm
- Compute covariance matrix given by,
All covariance matrices, \(\Sigma\), satisfy a mathematical property called symmetric positive definite. (Will take up in future posts.)
- Follow by this, eigen vectors and eigen values are calculated. There are various ways of doing this, most popularly done by singular value decomposition (SVD) of the covariance matrix. SVD returns three different matrices given by,
where,
- \(\Sigma\) is a \(n * n\) matrix because each \(x^{(i)}\) is a \(n * 1\) vector.
- \(U\) is the \(n * n\) matrix where each column represents a component of the PCA. In order to reduce the dimensionality of the data, one needs to choose the first \(k\) columns to form a matrix, \(U_{reduce}\), which is a \(n * k\) matrix.
So the dimensionally compressed data is given by,
Since, \(U_{reduce}^T\) is \(k * n\) matrix and \(x^{(i)}\) is \(n * 1\) vector, the product, \(z^{(i)}\) is a \(k * 1\) vector with reduced number of dimensions.
Given a reduced representation, \(z^{(i)}\), we can find its approximate reconstruction in the higher dimension by,
Since, \(U_{reduce}\) is \(n * k\) matrix and \(z^{(i)}\) is \(k * 1\) vector, the product, \(x_{approx}^{(i)}\) is a \(n * 1\) vector with the original number of dimensions.
Number of Principal Components
How to determine the number pricipal components to retain during the dimensionality reduction?
Consider the following two metrics
- The objective of PCA is to minimize the projection error given by,
- Total variation in the data is given by,
Rule of Thumb is, choose the smallest value of \(k\), such that,
i.e. \(99\%\) of the variance is retained (Generally values such as \(95-90\%\) variance retention are used). It will be seen overtime than often the amount of dimensions reduced is significant while maintaining the 99% variance. (because many features are highly correlated.)
Talking about the amount of variance retained in more informative than citing the number of principal components retained.
So for choosing k the following method could be used,
- Try PCA for \(k=1\)
- Compute \(U_{reduce}\), \(z^{(1)}, z^{(2)}, \cdots, z^{(m)}\), \(x_{approx}^{(1)}, x_{approx}^{(2)}, \cdots, x_{approx}^{(m)}\)
- Check variance retention using \eqref{9}.
- Repeat the steps for \(k = 2, 3, \cdots\) to satisfy \eqref{9}.
There is an easy work around to bypass this tedious process by using \eqref{4}. The matrix \(S\) returned by SVD is a diagonal matrix of eigenvalues corresponding to each of the components in \(U\). \(S\) is a \(n * n\) matrix with diagonal eigenvalues \(s_{11}, s_{22}, \cdots, s_{nn}\) and off-diagonal elements equal to 0. Then for a given value of \(k\),
Using \eqref{10}, \eqref{9} can be written as,
Now, it is easier to calculate the variance retained by iterating over values of \(k\) and calculating the value in \eqref{11}.
The value in \eqref{10} is a good metrics to cite as the performance of PCA, as to how well is the reduced dimensional data representing the original data.
Suggestions for Using PCA
- Speed up a learning algorithm by reducing the number of features by applying PCA and choosing top-k to maintain 99% variance. PCA should be only applied on the training data to get the \(U_{reduce}\) and not on the cross-validation or test data. This is because \(U_{reduce}\) is parameter of the model and hence should be only learnt on the training data. Once the matrix is determined, the same mapping can be applied on the other two sets.
Run PCA only on the training data, not on cross-validation or test data.
- If using PCA for visualization, it does not make sense to choose \(k \gt 3\).
- Usage of PCA to reduce overfitting is not correct. The reason it works well in some cases is because it reduces the number of features and hence reduces the variance and increases the bias. But often there are better ways of doing this by using regularization and other similar techniques than use PCA. This would be a bad application of PCA. It is generally adviced against because PCA removes some information without keeping into consideration the target values. While this might work when 99% of the variance is retained, it may as well on various occasions lead to the loss of some useful information. On the other hand, regularization parameters are more optimal for preventing overfitting because while penalizing overfitting they also keep in context the values of the target vector.
Do not use PCA to prevent overfitting. Instead look into regularization.
-
It is often worth a shot to try any algorithm without using PCA before diving into dimensionality reduction. So, before implementing PCA, implement the models with original dataset. If this does not give desired result, one should move ahead a try using PCA to reduce the number of features. This would also give a worthy baseline score to match the performance of model against once PCA is applied.
-
PCA can also be used in cases when the original data is too big for the disk space. In such cases, compressed data will give some benefits of space saving by dimensionality reduction.
REFERENCES:
Machine Learning: Coursera - PCA Problem Formulation
Machine Learning: Coursera - Algorithm
Machine Learning: Coursera - Reconstruction
Machine Learning: Coursera - Choosing the number of principal components
Machine Learning: Coursera - Advice


Im looking for a build that can reach the max spot and listen (127). My server can make items with +skill bonus for it and I can get up to 127 spot/listen. It doesn't really matter what build it is. I just want one that can reach the max spot and listen. Thanks!
Isn't that just any build with the max WIS possible, Skill Focus: Spot, Epic Skill Focus: Spot, Skill Focus: Listen and Epic Skill Focus Listen? like a Druid or a Cleric multiclassed with Champion of Torm and Monk, and that's it.
Doh! Should of thought of that Thanks!
Well, if you can get the max skill boost from items (which is +50), then you need to have a raw 77 score. You can get 43 from skill point investment alone. Add in SF and ESF for another 13, which brings you to 56. So you're looking for 21 more points. Both Artist and Blooded give you a +2 bonus to Spot.
Elves, Gnomes, and Halflings get a +2 racial bonus to Listen, so one of those races and one of the above mentioned feats will mean you need to scrounge up 19 more points somewhere. That's a 48 WIS score. Looks like CLC or Druid (as Thax said) starting with maxed WIS, boosting it as high as it can go, and maxing out your WIS with items/spells. _________________ It's rogue, dammit, ROGUE!!!


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Welcome to KubePlus documentation¶
KubePlus is a turn-key solution to transform any containerized application into a SaaS. It takes an application Helm chart and delivers it as-a-service by automating multi-tenancy management and day2 operations such as monitoring, troubleshooting and application upgrades. KubePlus consists of a CRD that enables creating new Kubernetes APIs (CRDs) to realize such services. The new CRDs enable creation of a Helm release per tenant with tenant level isolation, monitoring and consumption tracking.
KubePlus is designed to help software vendors accelerate their journey to SaaS or enterprise platform teams rapidly deliver managed services for any custom applications.
Overview¶
KubePlus takes an application Helm chart and delivers it as-a-service by automating multi-tenancy management and day2 operations. KubePlus consists of a CRD that enables creating new Kubernetes APIs (CRDs) to realize such services. Provider of the service has privileged access to the cluster and is able to create these services from Helm charts. Consumer of the service has limited access to the cluster and is able to use newly created service API / CRD and create an instance of the application. Behind the scene the provider is able to upgrade, monitor or govern this service.
Create: Create SaaS for any application packaged as Helm chart.
Govern: Tenant level policies for isolation and resource utilization.
Monitor: Tenant level consumption metrics for CPU, memory, storage, network.
Troubleshoot: Tenant level Kubernetes resource relationship graphs.
Next section provides details about KubePlus components.


Building the PyVVO Documentation¶
This section discusses what is required to build PyVVO’s documentation.
TL;DR (though please read): python build_docs.py --checkout
Prerequisites¶
This may not be comprehensive, but I’ll try.
Python >= 3.5 (Brandon only tested with 3.7)
LaTex (Brandon installed via MikTex)
LaTex packages (see
latex/packages.tex
, MikTex automagically installs packages)dvisvgm (Brandon didn’t have to install this, so maybe it comes with MikTex?)
Virtual Environment¶
To keep things simple, Brandon builds the documentation via a Python interpreter within a local virtual environment, rather than using a Docker-based interpreter. The Python docs provide a nice tutorial for building and using virtual environments. TL;DR:
cd ~/git/pyvvo
python3 -m venv venv
source venv/bin/activate
After activating the virtual environment, install sphinx
and
numpy
. The original goal was to only require installing sphinx
in the virtual environment, but zip.py
has quite a few constants
that depend on numpy
, so mocking
the numpy
import simply isn’t practical. Install like so:
python -m pip install sphinx numpy
Initial Setup¶
The files conf.py
, index.rst
, make.bat
, and Makefile
were originally created using the sphinx-quickstart tool.
All these files have since been modified.
Building the Documentation¶
Building the documentation is wicked simple. After activating your
virtual environment (see Prerequisites), and assuming your shell’s
current working directory is pyvvo/docs
, simply:
python build_docs.py --checkout
NOTE: Be careful when using the --checkout
flag. You should
ensure that your Git workspace in docs/rst_latex
is clean before
running build_docs.py
with the –checkout command. Really, you
should ensure you have a clean workspace before running
build_docs.py
at all, because it does some find+replace in the
rst_latex
directory.
For details on the available flags/arguments, run:
python build_docs.py --help
Note that all tools (latex
, dvisvgm
, sphinx-build
) have
their outputs “quieted.” Thus, if you get any output besides the
obvious output from the Python script itself, that’s cause for concern.
For reference, with no warnings, clean output from build_docs.py
should look like:
INFO:root:Running tex2svg for flow_conventions… INFO:root:Done. INFO:root:Running tex2svg for main_loop… INFO:root:Done. INFO:root:Building the documentation… INFO:root:Done. INFO:root:Checking out files in rst_latex… INFO:root:Done.
build_docs.py
will emit a warning like so if any of the \ref{}
references in the rst_latex
files don’t match up with the .tex
files:
WARNING:root:Some reference(s) did not get updated in main_loop.rst, and are thus not defined in main_loop.tex: [‘\ref{flow:update-glm-manager}’]
For an unknown reason, Brandon had troubles where the docs would not always get completely updated after building. There’s a simple fix: delete the doctrees and html directories and build again.
Viewing the Built Documentation¶
Simply open ~/git/pyvvo/docs/html/index.html
in your web browser.


Class Thing
Class that represents all things in the game.
Inherited Members
Namespace: Fibula.Common
Assembly: Fibula.Common.dll
Syntax
public abstract class Thing : IThing, ILocatable, IContainedThing, IEquatable<IThing>, ICloneableThing<IThing>
Constructors
| Improve this Doc View SourceThing()
Initializes a new instance of the Thing class.
Declaration
public Thing()
Properties
| Improve this Doc View SourceCanBeMoved
Gets a value indicating whether this thing can be moved.
Declaration
public abstract bool CanBeMoved { get; }
Property Value
| Type | Description |
|---|---|
| System.Boolean |
CarryLocation
Gets the location where this thing is being carried at, if any.
Declaration
public abstract Location? CarryLocation { get; }
Property Value
| Type | Description |
|---|---|
| System.Nullable<Location> |
Location
Gets this thing's location.
Declaration
public virtual Location Location { get; }
Property Value
| Type | Description |
|---|---|
| Location |
ParentContainer
Gets or sets the parent container of this thing.
Declaration
public IThingContainer ParentContainer { get; set; }
Property Value
| Type | Description |
|---|---|
| IThingContainer |
TrackedEvents
Gets the tracked events for this thing.
Declaration
public IDictionary<string, IEvent> TrackedEvents { get; }
Property Value
| Type | Description |
|---|---|
| System.Collections.Generic.IDictionary<System.String, IEvent> |
TypeId
Gets the type id of this thing.
Declaration
public abstract ushort TypeId { get; }
Property Value
| Type | Description |
|---|---|
| System.UInt16 |
UniqueId
Gets the unique id of this item.
Declaration
public Guid UniqueId { get; }
Property Value
| Type | Description |
|---|---|
| System.Guid |
Methods
| Improve this Doc View SourceClone()
Creates a new IThing that is a shallow copy of the current instance.
Declaration
public abstract IThing Clone()
Returns
| Type | Description |
|---|---|
| IThing | A new IThing that is a shallow copy of this instance. |
DescribeForLogger()
Provides a string describing the current thing for logging purposes.
Declaration
public abstract string DescribeForLogger()
Returns
| Type | Description |
|---|---|
| System.String | The string to log. |
Equals(IThing)
Indicates whether the current object is equal to another object of the same type.
Declaration
public bool Equals(IThing other)
Parameters
| Type | Name | Description |
|---|---|---|
| IThing | other | The other object to compare against. |
Returns
| Type | Description |
|---|---|
| System.Boolean | True if the current object is equal to the other parameter, false otherwise. |
RaiseLocationChanged(Location)
Invokes the LocationChanged event on this thing.
Declaration
public void RaiseLocationChanged(Location fromLocation)
Parameters
| Type | Name | Description |
|---|---|---|
| Location | fromLocation | The location from which the change happened. |
StartTrackingEvent(IEvent, String)
Makes the thing start tracking an event.
Declaration
public void StartTrackingEvent(IEvent evt, string identifier = "")
Parameters
| Type | Name | Description |
|---|---|---|
| IEvent | evt | The event to stop tracking. |
| System.String | identifier | Optional. The identifier under which to start tracking the event. If no identifier is provided, the event's EventType is used. |
StopTrackingEvent(IEvent)
Makes the thing stop tracking an event.
Declaration
public void StopTrackingEvent(IEvent evt)
Parameters
| Type | Name | Description |
|---|---|---|
| IEvent | evt | The event to stop tracking. |
StopTrackingEvent(IEvent, String)
Makes the thing stop tracking an event.
Declaration
public void StopTrackingEvent(IEvent evt, string identifier = "")
Parameters
| Type | Name | Description |
|---|---|---|
| IEvent | evt | The event to stop tracking. |
| System.String | identifier | The identifier under which to look for and stop tracking the event. |
ToString()
Declaration
public override string ToString()
Returns
| Type | Description |
|---|---|
| System.String |
Overrides
Events
| Improve this Doc View SourceLocationChanged
Event to invoke when the thing's location has changed.
Declaration
public event OnLocationChanged LocationChanged
Event Type
| Type | Description |
|---|---|
| OnLocationChanged |


I recently acquired a prescription for adderall, a substance which I have had many months of positive and very negative experiences with. I just took 40 mg about twenty minutes ago, and I'm already recognizing the coming euphoria. It starts at the head, a small tingling effect, and I become more alert of my surroundings. As the alertness intensifies, so does my desire to just do something, anything. But the part of adderall I got most addicted to, and still struggle with, is the massive appetite suppression.
I struggled with my weight when I was in elementary school and junior high, and my obsession with weight and getting thinner began around seventh grade. Although I never really bothered to do much about it, I thought about my weight constantly. The years went by, and nothing eventful happened. I got progressively more and more depressed, and instead of working out or trying to eat healthier, I used food as a coping mechanism. I just couldn't figure out how to flip that switch in my head to turn the 'FOOD' signal off. It was then, in eleventh grade, that I discovered adderall.
The first time I tried it, I really hadn't meant to. I was sitting alone in a hallway, and a girl came up to me and just started talking to me. She later told me she was on adderall at the time, which makes the very sociable behavior understandable. I glared at her all the while, wondering why she was talking to me. I then asked, sarcastically, if she had any 'uppers.' She laughed and said 'Actually, yes I do.' She pulled out a pretty little prescription bottle and popped two 20mg, peach colored adderall into my hand. 'Down those with a coke. You'll feel better.' she promised. She was right.
The rush came quickly, as I hadn't eaten anything so far that day. Again, it was the tingling sensation first, crawling down my body. Then it was the increased awareness of my surroundings, almost like I hadn't seen colors before, and all of a sudden such wonderful colors were everywhere around me. Imagine a blind man suddenly being able to see. I smiled constantly, and babbled with her to no end. We virtually competed for who could talk about the most random things the fastest. It was such a wonderful, euphoric rush. I suddenly thought to myself, wow, this is what it's like to be normal. Not quite.
As the high continued, I noticed my thoughts of food completely subsided. Not only did they subside, but when I thought of food, the thoughts merely grossed me out. This had never happened before. I asked Leslie, my 'best' friend by this point, if she received that effect from it. 'Yeah, but I just smoke some weed to offset it.' I wondered why the hell would you want to offset the feeling that food doesn't control your life. I bummed about six more pills off of her before the end of the day. Her doctor had prescribed her a bottle of 120x 20mg pills, to be taken 4x daily. She said she rarely took more than 2 a day, and she was more than happy to supply me with my little, peach appetite killers. My obsession began.
To make matters worse, as I began to lose weight, which started almost immediately.  People would compliment me. 'Wow Lex, you look great. Keep it up!' etc. I loved it, and it gave me a feeling of power. The feeling of power, combined with the powerful euphoric feelings the adderall gave me, I quickly started becoming a different person.  When I'd gone from 155 lbs down to 130, I decided that I was done using some girl for my adderall. I wanted it myself.
I convinced a psychiatrist that I had a horrible case of ADHD, and that adderall was the only drug for me. It was my 'savior.' Ironic how this term can be applied in so many ways. I thought I was finally saved from my weight obsessions and the constant weigh-ins with myself, the endless lists of foods I made during the day that I would eat later, the limitless combinations (never above 400-600 calories!) that I could eat. However, something strange happened when I got the adderall for myself. It was no longer enjoyable for me. In fact, it became like a burden, and a my obsession with getting thinner rose, so did my doses of adderall.
I started taking the 60mg a day I was prescribed, along with two to three more 20mg pills per day that I would get from Leslie. The euphoric rush was gone after about two months, and the only reason I took adderall was to control my weight. I really thought I was in control, but in reality, my weight, and the adderall, were controlling me.
I'd first used adderall in early October of 2006, and by that christmas, I was down from 155 pounds to 120. I am 5'9', and 120 was a low weight for a girl my height. People stopped commenting positively on my weight, and started advising me to 'eat up' over the holiday. My family in particular was worried. They were completely oblivious to my obsession, however, and I was fine with that.
I won't detail the following 3 months, but they happened pretty much as follows. I continued to take adderall, and started almost eating less than I was before. By late February I was down to a mere 100 pounds, a nice 'even' number I'd decided on. But I wasn't happy. In fact, I was miserable. My misery surprised me, and I was mad at myself for not being more content with the 'progress' I had made. In March 2007, my parents finally figured it out, and took the adderall away. Now, here I am, once again taking adderall, this time illegally, and wondering where I'll go next. I'm up to 125 pounds now, but I still obsess, with or without the adderall.
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.


The information on this page is NOT an official record. Do not rely on the correctness or completeness of this information. Verify all information with the official record keeper. The information contained in this report is provided in compliance with the Oklahoma Open Records Act, 51 O.S. 24A.1. Use of this information is governed by this act, as well as other applicable state and federal laws.
| STATE OF OKLAHOMA, Plaintiff, v. TANICE LASHAY NELSON, Defendant, and DANIEL WALDEMAR LAUGHLIN, Defendant. |
No. CF-2016-5438 (Criminal Felony) Filed: 07/06/2016 Closed: 11/07/2017 Judge: Henderson, Timothy R |
LAUGHLIN, DANIEL WALDEMAR,
Defendant
NELSON, TANICE LASHAY,
Defendant
OCPD,
ARRESTING AGENCY
STATE OF OKLAHOMA,
Plaintiff
None
| Event | Party | Docket | Reporter |
|---|---|---|---|
|
Thursday, January 12, 2017 at 9:00 AM PRELIMINARY HEARING CONFERENCE |
NELSON, TANICE LASHAY | Lisa K. Hammond | |
|
Thursday, January 12, 2017 at 1:30 PM PRELIMINARY HEARING CONFERENCE |
NELSON, TANICE LASHAY | Larry Shaw | |
|
Wednesday, January 25, 2017 at 9:00 AM PRELIMINARY HEARING CONFERENCE X1 CONT |
NELSON, TANICE LASHAY | Larry Shaw | |
|
Monday, March 6, 2017 at 9:00 AM PRELIMINARY HEARING CONFERENCE X2 CONT |
NELSON, TANICE LASHAY | Larry Shaw | |
|
Thursday, June 15, 2017 at 0:00 AM PRELIMINARY HEARING CONFERENCE |
LAUGHLIN, DANIEL WALDEMAR | Lisa K. Hammond | |
|
Thursday, June 15, 2017 at 1:30 PM PRELIMINARY HEARING CONFERENCE |
LAUGHLIN, DANIEL WALDEMAR | Larry Shaw | |
|
Tuesday, August 1, 2017 at 9:00 AM PRELIMINARY HEARING CONFERENCE X1 CONT |
LAUGHLIN, DANIEL WALDEMAR | Mark McCormick | |
|
Tuesday, September 19, 2017 at 1:30 PM PRELIMINARY HEARING CONFERENCE X2 CONT |
LAUGHLIN, DANIEL WALDEMAR | Mark McCormick | |
|
Tuesday, November 7, 2017 at 9:00 AM PRELIMINARY HEARING CONFERENCE X3 CONT |
LAUGHLIN, DANIEL WALDEMAR | Mark McCormick | |
|
Wednesday, December 19, 2018 at 0:00 AM COST ADMINISTRATION REVIEW |
LAUGHLIN, DANIEL WALDEMAR | Cost Admin. Judge (General) | |
|
Wednesday, December 4, 2019 at 0:00 AM COST ADMINISTRATION REVIEW |
NELSON, TANICE LASHAY | Cost Admin. Judge (General) | |
|
Thursday, December 5, 2019 at 2:30 PM MOTION FOR RULE 8 |
NELSON, TANICE LASHAY | April Collins | |
|
Thursday, December 3, 2020 at 0:00 AM COST ADMINISTRATION REVIEW |
NELSON, TANICE LASHAY | Cost Admin. Judge (General) | |
|
Thursday, December 3, 2020 at 2:30 PM MOTION FOR RULE 8 |
NELSON, TANICE LASHAY | April Collins |
Parties appear only under the counts with which they were charged. For complete sentence information, see the court minute on the docket.
| Count # 1. |
Count as Filed:
COMPU, COMPUTER FOR THE PURPOSE OF VIOLATING OKLAHOMA STATUES,
in violation of 21 O.S. 1951-1981 Date of Offense: 06/20/2016 |
| Party Name | Disposition Information | |
|---|---|---|
|
Disposed: CONVICTION,
02/13/2017.
Guilty Plea Count as Disposed: COMPUTER FOR THE PURPOSE OF VIOLATING OKLAHOMA STATUES(COMPU) Violation of 21 O.S. 1951-1981 |
| Count # 2. |
Count as Filed:
DU8II, POSSESSION OF A CDS - METHAMPHETAMINE,
in violation of 63 O.S. 2-401-2-420 Date of Offense: 06/20/2016 |
| Party Name | Disposition Information | |
|---|---|---|
|
Disposed: DEFERRED,
11/07/2017.
Guilty Plea Count as Disposed: POSSESSION OF A CDS - METHAMPHETAMINE(DU8II) Violation of 63 O.S. 2-401-2-420 |
| Count # 3. |
Count as Filed:
PROCD, POSSESSION OF PROCEEDS DERIVED FROM A VIOLATION OF THE UNIFORM CONTROLLED DANGEROUS SUBSTANCE ACT,
in violation of 63 O.S. 2-401-2-420 Date of Offense: 06/20/2016 |
| Party Name | Disposition Information | |
|---|---|---|
|
Disposed: DEFERRED,
11/07/2017.
Guilty Plea Count as Disposed: POSSESSION OF PROCEEDS DERIVED FROM A VIOLATION OF THE UNIFORM CONTROLLED DANGEROUS SUBSTANCE ACT(PROCD) Violation of 63 O.S. 2-401-2-420 |
| Count # 4. |
Count as Filed:
PROS, ENGAGE IN AN ACT OF PORSTITUTION,
in violation of 21 O.S. 1026-1031 Date of Offense: 06/20/2016 |
| Party Name | Disposition Information | |
|---|---|---|
|
Disposed: CONVICTION,
02/13/2017.
Guilty Plea Count as Disposed: ENGAGE IN AN ACT OF PORSTITUTION(PROS) Violation of 21 O.S. 1026-1031 |
| Count # 5. |
Count as Filed:
DU8IV, POSSESSION OF A CDS - XANAX,
in violation of 63 O.S. 2-401-2-420 Date of Offense: 06/20/2016 |
| Party Name | Disposition Information | |
|---|---|---|
|
Disposed: DISMISSED,
11/07/2017.
Dismissed- Request of the State Count as Disposed: POSSESSION OF A CDS - XANAX(DU8IV) Violation of 63 O.S. 2-401-2-420 |
| Count # 6. |
Count as Filed:
PROS, AIDING AND ABETTING PROSTITUTION,
in violation of 21 O.S. 1026-1031 Date of Offense: 06/20/2016 |
| Party Name | Disposition Information | |
|---|---|---|
|
Disposed: DEFERRED,
11/07/2017.
Guilty Plea Count as Disposed: AIDING AND ABETTING PROSTITUTION(PROS) Violation of 21 O.S. 1026-1031 |
| Date | Code | Description | Count | Party | Amount |
|---|---|---|---|---|---|
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WARRANT OF ARREST ISSUED, JUDGE: RUSSELL HALL - BOND AMOUNT: $21,500.00 |
LAUGHLIN, DANIEL WALDEMAR | $ 50.00 | |
|
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|
OKLAHOMA COURT INFORMATION SYSTEM REVOLVING FUND |
LAUGHLIN, DANIEL WALDEMAR | $ 25.00 | |
|
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CRIMINAL FELONY INITIAL FILING. Document Available at Court Clerk's Office |
NELSON, TANICE LASHAY | ||
|
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COVER SHEET Document Available at Court Clerk's Office |
NELSON, TANICE LASHAY | ||
|
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|
COVER SHEET Document Available at Court Clerk's Office |
LAUGHLIN, DANIEL WALDEMAR | ||
|
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DEFENDANT TANICE LASHAY NELSON WAS CHARGED WITH COUNT #1, COMPUTER FOR THE PURPOSE OF VIOLATING OKLAHOMA STATUES IN VIOLATION OF 21 O.S. 1951-1981 |
1 | NELSON, TANICE LASHAY | |
|
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DEFENDANT DANIEL WALDEMAR LAUGHLIN WAS CHARGED WITH COUNT #2, POSSESSION OF A CDS - METHAMPHETAMINE IN VIOLATION OF 63 O.S. 2-401-2-420 |
2 | LAUGHLIN, DANIEL WALDEMAR | |
|
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DEFENDANT DANIEL WALDEMAR LAUGHLIN WAS CHARGED WITH COUNT #3, POSSESSION OF PROCEEDS DERIVED FROM A VIOLATION OF THE UNIFORM CONTROLLED DANGEROUS SUBSTANCE ACT IN VIOLATION OF 63 O.S. 2-401-2-420 |
3 | LAUGHLIN, DANIEL WALDEMAR | |
|
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DEFENDANT TANICE LASHAY NELSON WAS CHARGED WITH COUNT #4, ENGAGE IN AN ACT OF PORSTITUTION IN VIOLATION OF 21 O.S. 1026-1031 |
4 | NELSON, TANICE LASHAY | |
|
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DEFENDANT DANIEL WALDEMAR LAUGHLIN WAS CHARGED WITH COUNT #5, POSSESSION OF A CDS - XANAX IN VIOLATION OF 63 O.S. 2-401-2-420 |
5 | LAUGHLIN, DANIEL WALDEMAR | |
|
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DEFENDANT DANIEL WALDEMAR LAUGHLIN WAS CHARGED WITH COUNT #6, AIDING AND ABETTING PROSTITUTION IN VIOLATION OF 21 O.S. 1026-1031 |
6 | LAUGHLIN, DANIEL WALDEMAR | |
|
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OCIS HAS AUTOMATICALLY ASSIGNED JUDGE JONES, GLENN M. TO THIS CASE. |
|||
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WARRANT OF ARREST ISSUED, JUDGE: RUSSELL HALL - BOND AMOUNT: $4,500.00 |
NELSON, TANICE LASHAY | $ 50.00 | |
|
|
|
OKLAHOMA COURT INFORMATION SYSTEM REVOLVING FUND |
NELSON, TANICE LASHAY | $ 25.00 | |
|
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WARRANT RETURNED 12/20/2016, WARRANT ISSUED ON 7/7/2016 Document Available at Court Clerk's Office |
NELSON, TANICE LASHAY | ||
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HALL: COMES ON FOR ARRAIGNMENT DEFT APPEARS IN CUSTODY WITHOUT AN ATTY, PD TENTIVELY APPOINTED, DEFT IS ARRAIGNED AND IS GIVE A TRUE COPY OF INFORMATION SHEET, ENTERS A PLEA OF NOT GUILTY AND IS SET FOR PHC ON 1-12-17 AT 9AM BEFORE JUDGE HAMMOND, BOND SET IN THE AMOUNT OF $4500 Document Available at Court Clerk's Office |
NELSON, TANICE LASHAY | ||
|
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ORDER REASSIGNING CRIMINAL DISTRICT JUDGE AND RESETTING CONFERENCE DKT, JUDGE G. JONES TO JUDGE HENDERSON, TO FOLLOW CF-14-6327 Document Available at Court Clerk's Office |
NELSON, TANICE LASHAY | ||
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JUDGE SHAW: DEFT APPEARS IN CUSTODY W/ ATTY WINTERS. STATE PRESENT BY ADA COLLINS. PHC CONT TO 1-25-17 @ 9AM BEFORE JUDGE SHAW Document Available at Court Clerk's Office |
NELSON, TANICE LASHAY | ||
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JUDGE SHAW: DEFT APPEARS IN CUSTODY W/ COUNSEL JAMES TODD. STATE PRESENT BY ADA KELLY COLLINS. PHC CONT TO 3-6-2017 AT 9AM BEFORE JUDGE SHAW. Document Available at Court Clerk's Office |
NELSON, TANICE LASHAY | ||
|
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ISSUE COMMITMENT |
NELSON, TANICE LASHAY | ||
|
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HENDERSON: COMES ON FOR PLEA AND SENTENCING. DEFT APPEARS IN CUSTODY W/ COUNSEL J TODD. STATE PRESENT BY ADA COLLINS. DEFT ENTERS A NEGOTIATED PLEA OF GUILTY AND IS SENTENCED AS FOLLOWS: CT1: (5) YEARS TO DO IN DOC, CT4: (1) YEAR TO DO IN OK CO JAIL. COUNTS TO RUN C/C AND C/C WITH CF-14-6327, CF-15-7704 CREDUT FOR TIME SERVED 58 DAYS. DEFT IS ORDERED TO PAY $100 FINES, $85 VCA, $80 DA FEE, $150 DNA FEE AND COURT COSTS. ALL COSTS DUE INSTANTER. COURT REPORTER WAIVED. ANY BOND NOT IN FORFEITURE, EXONERATED. COMMITMENT ISSUED |
1 | NELSON, TANICE LASHAY | |
|
|
|
COURT COSTS ON FELONY |
1 | NELSON, TANICE LASHAY | $ 103.00 |
|
|
|
OKLAHOMA COURT INFORMATION SYSTEM REVOLVING FUND |
1 | NELSON, TANICE LASHAY | $ 25.00 |
|
|
|
DA COUNCIL PROSECUTION ASSESSMENT FOR FELONY |
1 | NELSON, TANICE LASHAY | $ 80.00 |
|
|
|
MEDICAL EXPENSE LIABILITY REVOLVING FUND |
1 | NELSON, TANICE LASHAY | $ 10.00 |
|
|
|
SHERIFF’S SERVICE FEE FOR COURT HOUSE SECURITY |
1 | NELSON, TANICE LASHAY | $ 10.00 |
|
|
|
CLEET PENALTY ASSESSMENT |
1 | NELSON, TANICE LASHAY | $ 9.00 |
|
|
|
LAW LIBRARY FEE |
1 | NELSON, TANICE LASHAY | $ 6.00 |
|
|
|
AFIS FEE |
1 | NELSON, TANICE LASHAY | $ 5.00 |
|
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|
SHERIFF'S SERVICE FEE ON ARRESTS |
1 | NELSON, TANICE LASHAY | $ 5.00 |
|
|
|
FORENSIC SCIENCE IMPROVEMENT ASSESSMENT |
1 | NELSON, TANICE LASHAY | $ 5.00 |
|
|
|
ATTORNEY GENERAL VICTIM SERVICES UNIT |
1 | NELSON, TANICE LASHAY | $ 3.00 |
|
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|
C.H.A.B. STATUTORY FEE |
1 | NELSON, TANICE LASHAY | $ 3.00 |
|
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|
DNA LAB FEE- - PAY TO OSBI |
1 | NELSON, TANICE LASHAY | $ 150.00 |
|
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|
FINES PAYABLE TO COUNTY |
1 | NELSON, TANICE LASHAY | $ 100.00 |
|
|
|
VICTIMS COMPENSATION ASSESSMENT (AC12) |
1 | NELSON, TANICE LASHAY | $ 85.00 |
|
|
|
COURT CLERK ADMINISTRATIVE FEE ON COLLECTIONS |
1 | NELSON, TANICE LASHAY | $ 27.50 |
|
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|
TIMOTHY R HENDERSON , JUDGE: CASE DISPOSED. CONVICTION. SEE CT 1 |
4 | NELSON, TANICE LASHAY | |
|
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|
COURT COSTS ON FELONY |
4 | NELSON, TANICE LASHAY | $ 103.00 |
|
|
|
DA COUNCIL PROSECUTION ASSESSMENT FOR FELONY |
4 | NELSON, TANICE LASHAY | $ 25.00 |
|
|
|
OKLAHOMA COURT INFORMATION SYSTEM REVOLVING FUND |
4 | NELSON, TANICE LASHAY | $ 25.00 |
|
|
|
MEDICAL EXPENSE LIABILITY REVOLVING FUND |
4 | NELSON, TANICE LASHAY | $ 10.00 |
|
|
|
SHERIFF’S SERVICE FEE FOR COURT HOUSE SECURITY |
4 | NELSON, TANICE LASHAY | $ 10.00 |
|
|
|
CLEET PENALTY ASSESSMENT |
4 | NELSON, TANICE LASHAY | $ 9.00 |
|
|
|
LAW LIBRARY FEE |
4 | NELSON, TANICE LASHAY | $ 6.00 |
|
|
|
FORENSIC SCIENCE IMPROVEMENT ASSESSMENT |
4 | NELSON, TANICE LASHAY | $ 5.00 |
|
|
|
AFIS FEE |
4 | NELSON, TANICE LASHAY | $ 5.00 |
|
|
|
SHERIFF'S SERVICE FEE ON ARRESTS |
4 | NELSON, TANICE LASHAY | $ 5.00 |
|
|
|
ATTORNEY GENERAL VICTIM SERVICES UNIT |
4 | NELSON, TANICE LASHAY | $ 3.00 |
|
|
|
C.H.A.B. STATUTORY FEE |
4 | NELSON, TANICE LASHAY | $ 3.00 |
|
|
|
COURT CLERK ADMINISTRATIVE FEE ON COLLECTIONS |
4 | NELSON, TANICE LASHAY | $ 7.00 |
|
|
|
PLEA OF GUILTY - SUMMARY OF FACTS Document Available at Court Clerk's Office |
NELSON, TANICE LASHAY | ||
|
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WAIVER OF PRELIMINARY HEARING AND BIND- OVER ORDER Document Available at Court Clerk's Office |
NELSON, TANICE LASHAY | ||
|
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JUDGMENT AND SENTENCE/ELECTRONICALLY TRANSFERRED/TIME TO DO/JUDGE HENDERSON Document Available at Court Clerk's Office |
NELSON, TANICE LASHAY | ||
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TEMPORARY COMMITMENT W/SHERIFF'S RETURN Document Available at Court Clerk's Office |
NELSON, TANICE LASHAY | ||
|
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OKLAHOMA DEPARTMENT OF CORRECTIONS RECEIPT FOR PRISONER/DOCUMENTS/DETAINER Document Available at Court Clerk's Office |
NELSON, TANICE LASHAY | ||
|
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WARRANT RETURNED 5/19/2017, WARRANT ISSUED ON 7/7/2011 Document Available at Court Clerk's Office |
LAUGHLIN, DANIEL WALDEMAR | ||
|
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JUDGE HALL: COMES FOR ARRAIGNMENT. DEFT APPEARS IN CUSTODY, PUBLIC DEFENDER TENTATIVELY APPOINTED. THE STATE DOES NOT APPEAR. A PLEA OF "NOT GUILTY" IS ENTERED. PHC IS SET FOR 06-15-17 AT 9AM BEFORE JUDGE HAMMOND. BAIL IS SET AT $10,000. Document Available at Court Clerk's Office |
LAUGHLIN, DANIEL WALDEMAR | ||
|
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|
ORDER REASSIGNING CRIMINAL DISTRICT JUDGE AND RESETTING CONFERENCE DOCKET FROM JUDGE G. JONES TO JUDGE HENDERSON. Document Available at Court Clerk's Office |
LAUGHLIN, DANIEL WALDEMAR | ||
|
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|
JUDGE SHAW: DEFT APPEARS IN CUSTODY W/ COUNSEL J.P. HILL. STATE PRESENT BY ADA KELLY COLLINS. PHC CONT TO 8-1-2017 AT 9AM BEFORE JUDGE SHAW. Document Available at Court Clerk's Office |
LAUGHLIN, DANIEL WALDEMAR | ||
|
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RECOGNIZANCE BOND FOR LAUGHLIN, DANIEL WALDEMAR, POSTED 06/15/2017 Document Available at Court Clerk's Office |
LAUGHLIN, DANIEL WALDEMAR | $ 35.00 | |
|
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ORDER OF RELEASE W/SHERIFF'S RETURN Document Available at Court Clerk's Office |
LAUGHLIN, DANIEL WALDEMAR | ||
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JUDGE MCCORMICK: DEFT APPEARS IN PERSON W/ COUNSEL J.P. HILL. STATE PRESENT BY ADA KELLY COLLINS. PHC CONT TO 9-19-2017 AT 1:30PM BEFORE JUDGE MCCORMICK. Document Available at Court Clerk's Office |
LAUGHLIN, DANIEL WALDEMAR | ||
|
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JUDGE MCCORMICK: DEFT APPEARS IN PERSON W/ COUNSEL J.P. HILL. STATE PRESENT BY ADA MEAGAN TAYLOR. PHC CONT TO 11-7-2017 AT 9AM BEFORE JUDGE MCCORMICK. Document Available at Court Clerk's Office |
LAUGHLIN, DANIEL WALDEMAR | ||
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CLAIM FOR INTERCEPT OF TAX REFUND |
NELSON, TANICE LASHAY | ||
|
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|
CLAIM FOR INTERCEPT OF TAX REFUND |
LAUGHLIN, DANIEL WALDEMAR | ||
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JUDGE MCCORMICK: DEFT APPEARS IN PERSON WITH COUNSEL J.P. HILL. THE STATE IS PRESENT BY ADA MEAGAN TAYLOR. THE DEFT ENTERS A PLEA OF GUILTY AND IS SENTENCING IS DEFERRED UNTIL 11-6-2019; (2) YRS FOR CT{2}{3}; 11-6-2018; (1) YR FOR CT{6}. CT{5} IS DISMISSED W/O COST. DEFT IS TO BE SUPERVISED BY DOC.  ALL COSTS DUE INSTANTER. COURT REPORTER WAIVED. ANY BOND NOT IN FORFEITURE IS TO BE EXONERATED |
2 | LAUGHLIN, DANIEL WALDEMAR | |
|
|
|
COURT COSTS ON FELONY |
2 | LAUGHLIN, DANIEL WALDEMAR | $ 103.00 |
|
|
|
TRAUMA CARE ASSISTANCE REVOLVING FUND |
2 | LAUGHLIN, DANIEL WALDEMAR | $ 100.00 |
|
|
|
OKLAHOMA COURT INFORMATION SYSTEM REVOLVING FUND |
2 | LAUGHLIN, DANIEL WALDEMAR | $ 25.00 |
|
|
|
DA COUNCIL PROSECUTION ASSESSMENT FOR FELONY |
2 | LAUGHLIN, DANIEL WALDEMAR | $ 150.00 |
|
|
|
MEDICAL EXPENSE LIABILITY REVOLVING FUND |
2 | LAUGHLIN, DANIEL WALDEMAR | $ 10.00 |
|
|
|
SHERIFF’S SERVICE FEE FOR COURT HOUSE SECURITY |
2 | LAUGHLIN, DANIEL WALDEMAR | $ 10.00 |
|
|
|
CLEET PENALTY ASSESSMENT |
2 | LAUGHLIN, DANIEL WALDEMAR | $ 9.00 |
|
|
|
LAW LIBRARY FEE |
2 | LAUGHLIN, DANIEL WALDEMAR | $ 6.00 |
|
|
|
FORENSIC SCIENCE IMPROVEMENT ASSESSMENT |
2 | LAUGHLIN, DANIEL WALDEMAR | $ 5.00 |
|
|
|
AFIS FEE |
2 | LAUGHLIN, DANIEL WALDEMAR | $ 5.00 |
|
|
|
SHERIFF'S SERVICE FEE ON ARRESTS |
2 | LAUGHLIN, DANIEL WALDEMAR | $ 5.00 |
|
|
|
ATTORNEY GENERAL VICTIM SERVICES UNIT |
2 | LAUGHLIN, DANIEL WALDEMAR | $ 3.00 |
|
|
|
C.H.A.B. STATUTORY FEE |
2 | LAUGHLIN, DANIEL WALDEMAR | $ 3.00 |
|
|
|
COURT CLERK ADMINISTRATIVE FEE ON COLLECTIONS |
2 | LAUGHLIN, DANIEL WALDEMAR | $ 49.50 |
|
|
|
FINES PAYABLE TO COUNTY |
2 | LAUGHLIN, DANIEL WALDEMAR | $ 45.00 |
|
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|
VICTIMS COMPENSATION ASSESSMENT (AC12) |
2 | LAUGHLIN, DANIEL WALDEMAR | $ 120.00 |
|
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|
ATTORNEY FEE - TULSA AND OKLAHOMA COUNTY PUBLIC DEFENDER |
2 | LAUGHLIN, DANIEL WALDEMAR | $ 200.00 |
|
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MARK MCCORMICK , JUDGE: CASE DISPOSED. DEFERRED. |
3 | LAUGHLIN, DANIEL WALDEMAR | |
|
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COURT COSTS ON FELONY |
3 | LAUGHLIN, DANIEL WALDEMAR | $ 103.00 |
|
|
|
TRAUMA CARE ASSISTANCE REVOLVING FUND |
3 | LAUGHLIN, DANIEL WALDEMAR | $ 100.00 |
|
|
|
DA COUNCIL PROSECUTION ASSESSMENT FOR FELONY |
3 | LAUGHLIN, DANIEL WALDEMAR | $ 25.00 |
|
|
|
OKLAHOMA COURT INFORMATION SYSTEM REVOLVING FUND |
3 | LAUGHLIN, DANIEL WALDEMAR | $ 25.00 |
|
|
|
MEDICAL EXPENSE LIABILITY REVOLVING FUND |
3 | LAUGHLIN, DANIEL WALDEMAR | $ 10.00 |
|
|
|
SHERIFF’S SERVICE FEE FOR COURT HOUSE SECURITY |
3 | LAUGHLIN, DANIEL WALDEMAR | $ 10.00 |
|
|
|
CLEET PENALTY ASSESSMENT |
3 | LAUGHLIN, DANIEL WALDEMAR | $ 9.00 |
|
|
|
LAW LIBRARY FEE |
3 | LAUGHLIN, DANIEL WALDEMAR | $ 6.00 |
|
|
|
AFIS FEE |
3 | LAUGHLIN, DANIEL WALDEMAR | $ 5.00 |
|
|
|
SHERIFF'S SERVICE FEE ON ARRESTS |
3 | LAUGHLIN, DANIEL WALDEMAR | $ 5.00 |
|
|
|
FORENSIC SCIENCE IMPROVEMENT ASSESSMENT |
3 | LAUGHLIN, DANIEL WALDEMAR | $ 5.00 |
|
|
|
ATTORNEY GENERAL VICTIM SERVICES UNIT |
3 | LAUGHLIN, DANIEL WALDEMAR | $ 3.00 |
|
|
|
C.H.A.B. STATUTORY FEE |
3 | LAUGHLIN, DANIEL WALDEMAR | $ 3.00 |
|
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|
COURT CLERK ADMINISTRATIVE FEE ON COLLECTIONS |
3 | LAUGHLIN, DANIEL WALDEMAR | $ 17.00 |
|
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MARK MCCORMICK , JUDGE: CASE DISPOSED. DISMISSED. |
5 | LAUGHLIN, DANIEL WALDEMAR | |
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MARK MCCORMICK , JUDGE: CASE DISPOSED. DEFERRED. |
6 | LAUGHLIN, DANIEL WALDEMAR | |
|
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|
COURT COSTS ON FELONY |
6 | LAUGHLIN, DANIEL WALDEMAR | $ 103.00 |
|
|
|
OKLAHOMA COURT INFORMATION SYSTEM REVOLVING FUND |
6 | LAUGHLIN, DANIEL WALDEMAR | $ 25.00 |
|
|
|
DA COUNCIL PROSECUTION ASSESSMENT FOR FELONY |
6 | LAUGHLIN, DANIEL WALDEMAR | $ 25.00 |
|
|
|
MEDICAL EXPENSE LIABILITY REVOLVING FUND |
6 | LAUGHLIN, DANIEL WALDEMAR | $ 10.00 |
|
|
|
SHERIFF’S SERVICE FEE FOR COURT HOUSE SECURITY |
6 | LAUGHLIN, DANIEL WALDEMAR | $ 10.00 |
|
|
|
CLEET PENALTY ASSESSMENT |
6 | LAUGHLIN, DANIEL WALDEMAR | $ 9.00 |
|
|
|
LAW LIBRARY FEE |
6 | LAUGHLIN, DANIEL WALDEMAR | $ 6.00 |
|
|
|
FORENSIC SCIENCE IMPROVEMENT ASSESSMENT |
6 | LAUGHLIN, DANIEL WALDEMAR | $ 5.00 |
|
|
|
AFIS FEE |
6 | LAUGHLIN, DANIEL WALDEMAR | $ 5.00 |
|
|
|
SHERIFF'S SERVICE FEE ON ARRESTS |
6 | LAUGHLIN, DANIEL WALDEMAR | $ 5.00 |
|
|
|
ATTORNEY GENERAL VICTIM SERVICES UNIT |
6 | LAUGHLIN, DANIEL WALDEMAR | $ 3.00 |
|
|
|
C.H.A.B. STATUTORY FEE |
6 | LAUGHLIN, DANIEL WALDEMAR | $ 3.00 |
|
|
|
COURT CLERK ADMINISTRATIVE FEE ON COLLECTIONS |
6 | LAUGHLIN, DANIEL WALDEMAR | $ 7.00 |
|
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PLEA OF GUILTY - SUMMARY OF FACTS: FINDINGS OF FACT, ACCEPTANCE OF PLEA/ JUDGE MCCORMICK Document Available at Court Clerk's Office |
LAUGHLIN, DANIEL WALDEMAR | ||
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SUPPLEMENTAL COURT ORDER: COMMUNITY SERVICE - RESTITUTION - COURT COST/ JUDGE MCCORMICK Document Available at Court Clerk's Office |
LAUGHLIN, DANIEL WALDEMAR | ||
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RULES & CONDITIONS OF SUPERVISED PROBATION/ JUDGE MCCORMICK Document Available at Court Clerk's Office |
LAUGHLIN, DANIEL WALDEMAR | ||
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DA PROSECUTION REIMBURSEMENT NOTICE/ JUDGE MCCORMICK Document Available at Court Clerk's Office |
LAUGHLIN, DANIEL WALDEMAR | ||
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THE STATUS OF THE BOND ENTRY DETAILED IN DOCKET SERIAL #91616017 ABOVE HAS CHANGED TO READ AS FOLLOWS: |
LAUGHLIN, DANIEL WALDEMAR | ||
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DEFENDANT INFORMATION TO BE SENT TO THE OKLAHOMA BUREAU OF NARCOTICS FOR ADDITION ON THE METHAMPHETAMINE OFFENDER REGISTRY |
LAUGHLIN, DANIEL WALDEMAR | ||
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ABSTRACT SENT TO D.P.S. |
5 | LAUGHLIN, DANIEL WALDEMAR | |
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COST ADMINISTRATION PAYMENT PLAN AGREEMENT, 11-07-17 Document Available at Court Clerk's Office |
LAUGHLIN, DANIEL WALDEMAR | ||
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MOTION TO DISMISS AND TO RECALL WARRANT/ CT 5 / ORDER TO DISMISS AND TO RECALL WARRANT/ JUDGE MARK MCCORMICK. Document Available at Court Clerk's Office |
LAUGHLIN, DANIEL WALDEMAR | ||
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NOTICE OF COURT HEARING FOR PAYMENT OF FINES AND COSTS Document Available at Court Clerk's Office |
NELSON, TANICE LASHAY | ||
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ORDER FOR INSTALLMENT PAYMENT OF COURT COSTS- COLLINS Document Available at Court Clerk's Office |
NELSON, TANICE LASHAY | ||
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CLAIM FOR INTERCEPT OF TAX REFUND |
LAUGHLIN, DANIEL WALDEMAR | ||
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CLAIM FOR INTERCEPT OF TAX REFUND |
NELSON, TANICE LASHAY | ||
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ADJUSTING ENTRY: MONIES DUE TO AC09-CARD ALLOCATION |
NELSON, TANICE LASHAY | $ 0.63 | |
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ADJUSTING ENTRY: MONIES DUE TO THE FOLLOWING AGENCIES REDUCED BY THE FOLLOWING AMOUNTS: |
NELSON, TANICE LASHAY | ||
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RECEIPT # 2019-4532470 ON 02/01/2019. |
NELSON, TANICE LASHAY | ||
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ADJUSTING ENTRY: MONIES DUE TO AC09-CARD ALLOCATION |
NELSON, TANICE LASHAY | $ 0.63 | |
|
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ADJUSTING ENTRY: MONIES DUE TO THE FOLLOWING AGENCIES REDUCED BY THE FOLLOWING AMOUNTS: |
NELSON, TANICE LASHAY | ||
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RECEIPT # 2019-4550743 ON 03/01/2019. |
NELSON, TANICE LASHAY | ||
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ADJUSTING ENTRY: MONIES DUE TO AC09-CARD ALLOCATION |
NELSON, TANICE LASHAY | $ 0.63 | |
|
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ADJUSTING ENTRY: MONIES DUE TO THE FOLLOWING AGENCIES REDUCED BY THE FOLLOWING AMOUNTS: |
NELSON, TANICE LASHAY | ||
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RECEIPT # 2019-4593070 ON 05/01/2019. |
NELSON, TANICE LASHAY | ||
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APPLICATION TO ACCELERATE DEFERRED SENTENCE Document Available at Court Clerk's Office |
LAUGHLIN, DANIEL WALDEMAR | ||
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ADJUSTING ENTRY: MONIES DUE TO AC09-CARD ALLOCATION |
NELSON, TANICE LASHAY | $ 0.63 | |
|
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|
ADJUSTING ENTRY: MONIES DUE TO THE FOLLOWING AGENCIES REDUCED BY THE FOLLOWING AMOUNTS: |
NELSON, TANICE LASHAY | ||
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RECEIPT # 2019-4610603 ON 05/29/2019. |
NELSON, TANICE LASHAY | ||
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|
COST DOCKET ORDER JUDGE COLLINS |
LAUGHLIN, DANIEL WALDEMAR | ||
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ADJUSTING ENTRY: MONIES DUE TO AC09-CARD ALLOCATION |
NELSON, TANICE LASHAY | $ 0.63 | |
|
|
|
ADJUSTING ENTRY: MONIES DUE TO THE FOLLOWING AGENCIES REDUCED BY THE FOLLOWING AMOUNTS: |
NELSON, TANICE LASHAY | ||
|
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RECEIPT # 2019-4668839 ON 09/01/2019. |
NELSON, TANICE LASHAY | ||
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|
ADJUSTING ENTRY: MONIES DUE TO AC09-CARD ALLOCATION |
NELSON, TANICE LASHAY | $ 0.63 | |
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ADJUSTING ENTRY: MONIES DUE TO THE FOLLOWING AGENCIES REDUCED BY THE FOLLOWING AMOUNTS: |
NELSON, TANICE LASHAY | ||
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RECEIPT # 2019-4687557 ON 10/01/2019. |
NELSON, TANICE LASHAY | ||
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CLAIM FOR INTERCEPT OF TAX REFUND |
LAUGHLIN, DANIEL WALDEMAR | ||
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CLAIM FOR INTERCEPT OF TAX REFUND |
NELSON, TANICE LASHAY | ||
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MOTION FOR RULE 8 HEARING Document Available at Court Clerk's Office |
LAUGHLIN, DANIEL WALDEMAR | ||
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ADJUSTING ENTRY: MONIES DUE TO AC09-CARD ALLOCATION |
NELSON, TANICE LASHAY | $ 0.63 | |
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ADJUSTING ENTRY: MONIES DUE TO THE FOLLOWING AGENCIES REDUCED BY THE FOLLOWING AMOUNTS: |
NELSON, TANICE LASHAY | ||
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RECEIPT # 2019-4721705 ON 11/30/2019. |
NELSON, TANICE LASHAY | ||
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WAIVER OF CONFLICT Document Available at Court Clerk's Office |
LAUGHLIN, DANIEL WALDEMAR | ||
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RULE 8 REVIEW ORDER/JUDGE COLLINS Document Available at Court Clerk's Office |
LAUGHLIN, DANIEL WALDEMAR | ||
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ADJUSTING ENTRY: MONIES DUE TO AC09-CARD ALLOCATION |
NELSON, TANICE LASHAY | $ 0.63 | |
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ADJUSTING ENTRY: MONIES DUE TO THE FOLLOWING AGENCIES REDUCED BY THE FOLLOWING AMOUNTS: |
NELSON, TANICE LASHAY | ||
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RECEIPT # 2020-4744709 ON 01/11/2020. |
NELSON, TANICE LASHAY |


Bobbin, or a small spool of thread, can often be found in a bin as it is used to store and organize the thread before use. [G|D]
Dobbin was always a reliable horse, which is why the farmer chose to keep him in the cabin near the family home - it was a sign of respect that he was given such a special place to stay. [G|D]
Jacobin was a revolutionary and dedicated supporter of the fight for freedom, and his red hemoglobin spoke loudly of his commitment to a noble cause. [G|D]
In the garden, a robin was perched atop a nubbin on the tree, contemplating the world below as he basked in the sunshine. [G|D]


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HIGH COURT OF JUDICATURE AT ALLAHABAD ?Court No. - 7 Case :- WRIT - C No. - 36759 of 2019 Petitioner :- M/S H.M.K. Brick Works And Another Respondent :- State Of U.P. And 5 Others Counsel for Petitioner :- Surya Narayan,Shashi Shukla Counsel for Respondent :- C.S.C.,Hari Nath Tripathi Hon'ble Biswanath Somadder,J.
Hon'ble Ajay Bhanot,J.
The issue sought to be raised in the instant writ petition is with regard to inaction on the part of U.P. Pollution Control Board to consider and dispose of a consent to operate application of the writ petitioners dated 15.12.2017 filed under the relevant provisions of the Air (Prevention & Control of Pollution) Act, 1981.
When the matter is taken up for consideration, the learned advocate appearing on behalf of the U.P. Pollution Control Board draws our attention to the order dated 02.01.2018, which appears to have been passed by one R.K. Singh, Regional Officer of U.P. Pollution Control Board.
This order reveals that a decision has been taken with regard to the consent application dated 15.12.2017.
Let a copy of the instructions handed over by the learned advocate representing U.P. Pollution Control Board be taken on record.
Since a decision has been rendered in the matter on 02.01.2018 the writ petition has essentially become infructuous and is disposed of accordingly.
Order Date :- 14.11.2019 Pravin (Biswanath Somadder,J.) (Ajay Bhanot,J.)


According to Statista, there are 1.7 million Australians using online dating apps at present and revenue in this sector is expected to grow from $35 million in 2020, to $51 million by 2024. Tinder wants to not only retain but grow their market share but remain the number one used dating app online. This means increasing user retention. User interviews were conducted as part of the research phase.
A survey was conducted to to further understand our users.
Here are our key findings from the interviews.
After completing an affinity diagram with the data collected, here are the major trends identified amongst user interviews:
From the results of the user interviews and surveys, the user insight was formed.
A group brainstorm using the I like, I wish, What if method to find solutions to our user insight’s.
Using a feature prioritisation matrix, ideas and features were placed in a matrix to find high impact and low complexity solutions to the user’s problems with dating apps.
We observed that Tinder isn’t achieving the goal of having its users find meaningful connections online, and many people feel online dating is very superficial. This is causing our users to get frustrated and confused as they get ghosted, ultimately moving to competitor apps. Therefore, we need to enhance our users’ experience by allowing users’ friends to take an active role in the matchmaking process. This in turn will allow users to make more meaningful connections and solve the issue of users being ghosted online.
A competitive analysis has been conducted amongst major competitors such as Bumble, Hinge and Plenty of Fish. We compared Tinder, including the matchmaker feature we had ideated to solve our user insight with other dating app services in the market. Upon performing the analysis, it was discovered that no other dating app service allows users to collaborate with friends. If Tinder Matchmaker is successful, it will be the only dating app service that provides a blind dating service. No other app allows users to verify their identity. However, Plenty of Fish combats catfishing by using “filter detecting” AI. It was identified in the feature prioritisation that verifying user IDs would have a high level of impact, and was very doable as there are many existing services out there that allow users to verify their details in minutes. Tinder’s strengths are its number of users. As the most downloaded dating app in the world, Tinder’s competitive advantage is its customer base. Upon conducting the SWOT analysis, a gap in the market was uncovered, and an opportunity for Tinder to accommodate the needs of its users. As Tinder’s users are the competitive advantage, it is crucial for Tinder to accommodate the needs of its existing users, so that they don’t get fed up, delete our app and move to a competitor’s service. The user persona was then formed.
The storyboard for the user’s journey was then created with a strong focus on the new matchmaker feature.
Three key features were picked to add to Tinder’s current dating app. Here are the user flows and how they fit into Tinder’s current dating app. In the wireframes and user flow, it was imperative to stay true to the iconic UI elements of Tinder and the mechanics of the swipe features. In this user flow, the user is frustrated with their matches and has an existing Tinder account. The user sends a link to their friend to help in the matchmaking process. The onboarding process for matchmakers is illustrated on the right. Once the matchmakers have onboarded, they can start swiping, as per normally done on Tinder. The matchmaker can go to their messages to chat with candidates to set up dates, or unmatch them. Regarding the matchmakers, creating the wireframes and mapping the user flow helped identify potential issues on where this feature will physically be within the app, as well as how matchmakers (who potentially have Tinder accounts) can switch between their own personal account, and the matchmaker one.
Results from our testing were very positive. We found that our features were indeed quite intuitive. Through AB testing, we found that users preferred the option to manually input their friend’s contact numbers to invite as Matchmakers.
These findings were then changed in the final prototype.
KEY LEARNINGS Daily Standups Completing daily stand ups helped us keep on track on what we were doing Understanding the user flow of the existing app It was key for this task that because we were adding extra features to a dating app that already exists, it was very important to thoroughly study the app and understand the user flow already in place. FUTURE IMPROVEMENTS - improve the elements on the ‘chats to friends’ screen to incorporate the bios of all the matches - creating the ability to vote and poll candidates amongst a group chat of friends - ‘Anti-filter’ AI technology to combat ‘cat-fishing’


subl
RoboFace perceives the environment through the webcam in his forehead. The task to give him understanding of the world through the image stream from the webcam belongs to the problems that computer vision tries to solve. The algorithm of our choice is a convolutional neural network (CNN), since neural networks are the state of the art today in computer vision.
Our implementation is a supervised learning algorithm, since we feed in labels (groundtruth) for every training image. We define a loss function that minimises the distance between the prediction and the true label. In this way, we learn the actual representation of the training data and can apply it to our test images and on RoboFace.
A neural network is composed of linear functions called neurons, followed by a nonlinear transformation called activation function. We can not only have many neurons per layer, but also stack layers consecutively. This results in a much better and faster training, since the minimum of the non-convex function we want to minimise in training is a better one (closer to the global optimum). But interesting is, that it was mathematically proven, that a single hidden layer (so a neural network containing input, output and one middle layer) of finite number of neurons, is enough for approximating any continous function. For more details, see the universal approximation theorem.
The architecture defines how this neurons are stacked in layers. A widely used layer in vision solving algorithms is one implementing convolutions. This is inspired by the human vision system, that also bundles a region of photosensitive cells and combine their signal into one neuron that send his own output to be processed further. In the minimalistic example on the right, you can see the nomenclature. Notice that the first layer implements a 2 convolution, since a neuron gets input only from two neighbors. The second layer is a fully connected layer, since every neuron is connected to every other, as well as the last one.
We take more of this convolutional layers and stack them together, to generate a so called deep neural network. The deepness shows its effectiveness in maximising the field of view of the network. Since a single convolution has for example the size 9 x 9, one layer can see only this much context. But stacking a next convolutional layer, the output of the previous 9 x 9 is considered again in 7 x 7 window for example, grows the field of view of the second layer considerably.
In the architecture we implemented, there is a Max Pooling Layer following every convolution step. This layer is actually downscaling the image to the half, in order to reduce the number of parameters and avoid redundancy of information.
The activations representing the nonlinearity is the ReLU (rectified linear unit) which penalises for wrong answers of each neuron during training. The piecewise linear function has the advantage that it rewards for the good answer in proportionality to the distance to the truth. But it has the disatvantage, that the neurons specialise in recognising their pattern and have no feedback when an arbitrary pattern is present. As an alternative, one can choose the LeakyReLU, which also introducing a slope for the wrong predictions. A very popular version is the ELU, that saturates in the negative regime, as seen in red in the figure.
Dropout layers in our architecture shut down randomly 25% and 50% of the neurons in order to make a better generalising neural network, since the neurons do not get the chance to learn the data by heart and over-fit.
The dense layer is connecting every neuron in the previous one with the ones in the current layer. Hence it contains more parameters than the convolutional ones, where the weights are vastly shared. We can interpret the role of the dense layers as the classification step in the network, whereas the convolutions did the feature extraction. So it is sensible to choose the number of neurons in the last layer as the amount of classes we want to predict, which is 13 in our case.
Our last activation function in the network is the sigmoid, it converts the output of the neurons to a probability in the interval [0, 1]. The loss function penalises for wrong classification. We use the binary crossentropy and not the categorical one, since the classes are not mutually dependent. We can have a male with straight hair, this attributes do not exclude each other. There are some which do, like straight and wavy hair. But on the one hand, a good trained network should not predict both at the same time, since it learned to distinguish the classes well. On the orher hand, this can be avoided in a postprocessing step at prediction time.
For training a neural network that generalises as much as possible, we take the big CelebA dataset, which is freely available at http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html. It contains 202.599 images of 10.177 celebrities annotated with 40 attributes per image.
From this attributes, we take 13 that are based only of the face region (so not taking the neck, shoulders or head top into account) and those where we think RoboFace could make something of. These are:
The neural network implementation is done with the Python keras
module using the
TensorFlow
backend. For training the neural network, we make use of the parallelising capacity of
the GPU, which results into a training time of 2 seconds per 1024 images. CPU training
is obviously much slower. The optimiser for finding a good local optimum of the loss
function is the Adadelta optimiser from keras, which stands out for his adaptive learning
rate and faster convergence than normal stochastic gradient descent SGD. The overall
125.709 parameters are found in this way.
A wrong way to train the neural network is to feed in the CelebA images from the dataset exactly as they are. Even though the training curve looks very fine, since we achieve a low loss value and a high accuracy on the training set and on the test set, the neural network over-fits the CelebA dataset and does not generalise at all on real world images as taken from RoboFace. He completely fails by saying that in the following image, there is a male with no beard with probabilities around 90%. The reasons for the failure on our images of interest could be numerous: the illumination of the images is different to theh training set, the background clutter in our images is much higher than in CelebA. So the first solution idea is to cut out the background and concentrate only on the face.
The right way to do it, is by normalising the training and the test set, as well as the images
taken by RoboFace. This is done in neural_network_training/normalisation.py
the following steps:
Now, the loss-accuracy curve looks as well as before, but the generalisation capabilities are vastly enhanced.
In the plot showing the accuracy per each class, we see as expected, that wavy and straight hair are comparably not distinguished that well. This comes form the fact, that the normalisation step crops out most of the region that does not contain the face, so a lot of hair information gets lost. The eyeglasses class, which is in the center of the face image, gets the best accuracy.
In the following are some examples of the neural network predicting on images taken by RoboFace.
Improving the socket on which the head is placed as well as the way it moves would provide the biggest benefit for the hardware.
Software wise it is mostly an issue of getting enough labeled images to train the neural network. We already used hundreds of thousands of them tough. Improving much without going into the millions is unlikely.


- saveBtn - Variable in class libsys.EditBookDialog
-
- saveBtn - Variable in class libsys.EditUserDialog
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- saveBtn - Variable in class libsys.SettingsDialog
-
- saveBtnActionPerformed(ActionEvent) - Method in class libsys.EditBookDialog
-
- saveBtnActionPerformed(ActionEvent) - Method in class libsys.EditUserDialog
-
- saveBtnActionPerformed(ActionEvent) - Method in class libsys.SettingsDialog
-
- search(int, int, int) - Method in class libsys.BookFactory
-
Recursive binary search through the array list for the Book with the given id
- search(int, int, int) - Method in class libsys.UserFactory
-
Recursive binary search through the array list for the User with the given id
- setBookFileName(String) - Method in class libsys.BookFactory
-
Update the output filename for the object
- setBooksFilename(String) - Method in class libsys.Settings
-
Change the file name for the Books
- setBooksFilenameTest() - Method in class libsys.SettingsTest
-
Test the setBooksFilename function
- setDefault() - Method in class libsys.Settings
-
- setLimit(int) - Method in class libsys.User
-
Change limit of this User
- setName(String) - Method in class libsys.User
-
Change name of this User
- setterTest() - Method in class libsys.UserTest
-
Test the setter functions
- settings - Static variable in class libsys.Main
-
- settings - Variable in class libsys.MainGUI
-
- Settings - Class in libsys
-
Handles all settings related operations and background processing of SettingsDialog
- Settings(String) - Constructor for class libsys.Settings
-
Creates new Settings importing information from the given file name
- settings - Variable in class libsys.SettingsTest
-
- settingsBtn - Variable in class libsys.MainGUI
-
- settingsBtnActionPerformed(ActionEvent) - Method in class libsys.MainGUI
-
- SettingsDialog - Class in libsys
-
UI for updating all the informations in Settings
- SettingsDialog(Frame, boolean, Settings) - Constructor for class libsys.SettingsDialog
-
Creates new SettingsDialog
- settingsFileName - Variable in class libsys.Settings
-
- settingsLbl - Variable in class libsys.SettingsDialog
-
- SettingsTest - Class in libsys
-
Test Settings related operations
- SettingsTest(String) - Constructor for class libsys.SettingsTest
-
Test Settings related operations
- setTitle(String) - Method in class libsys.Book
-
Set the title to the given title
- setTitle(String) - Method in class libsys.Settings
-
Change the title of the application
- setTitlesTest() - Method in class libsys.BookTest
-
Test the setTitles(String) functionality
- setTitleTest() - Method in class libsys.SettingsTest
-
Test the setTitle function
- setUp() - Method in class libsys.BookFactoryTest
-
Set up before testing
- setUp() - Method in class libsys.BookTest
-
Set up before testing
- setUp() - Method in class libsys.HandlerTest
-
Set up before testing
- setup(Settings) - Method in class libsys.SettingsDialog
-
- setUp() - Method in class libsys.UserFactoryTest
-
Set up before testing
- setUp() - Method in class libsys.UserTest
-
Set up before testing
- setUserFileName(String) - Method in class libsys.UserFactory
-
Update the output filename for the object
- setUsersFilename(String) - Method in class libsys.Settings
-
Change the file name for the Users
- setUsersFilenameTest() - Method in class libsys.SettingsTest
-
Test the setUsersFilename function
- size() - Method in class libsys.Settings
-
Amount of fields in the settings
- status - Variable in class libsys.Book
-
- status - Variable in class libsys.NewBookDialog
-
- status() - Method in class libsys.User
-
Check status of the User
- statusComboBox - Variable in class libsys.NewBookDialog
-
- statusLbl - Variable in class libsys.EditBookDialog
-
- statusLbl - Variable in class libsys.NewBookDialog
-
- statusOptions - Variable in class libsys.NewBookDialog
-
- suite() - Static method in class libsys.BookFactoryTest
-
- suite() - Static method in class libsys.BookTest
-
- suite() - Static method in class libsys.HandlerTest
-
- suite() - Static method in class libsys.SettingsTest
-
- suite() - Static method in class libsys.UserFactoryTest
-
- suite() - Static method in class libsys.UserTest
-


The 31C3 halfnarp web scheduling helper app
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- Help us to reduce conflicts of scheduling 31C3's Fahrplan.
- Click on the talks you likely would like to watch.
- Press submit.
- Repeat as necessary.
- Read our blogpost for details.
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WATCH: Michael V. shares the biggest lesson he learned in doing 'Family History'
by AEDRIANNE ACARMulti-awarded comedian and content creator Michael V. dished out the most important thing he learned while shooting his debut film Family History.
EXCLUSIVE: Michael V., natuwa na maraming Pinoy sa Amerika ang tinamaan sa 'Family History'
During his guesting in Kapuso ArtisTambayan on Monday, November 19, Direk Bitoy stressed that aspiring filmmakers should study the whole process of doing a movie.
He added that getting a mentor would also be beneficial to help you in the learning process.
“The best lesson or best advice that I can give you is to actually study,
“Pag-aralan ninyo kasi hindi basta-basta pinapasok itong filmmaking. Marami sa atin, hindi man lang nagka-crash course sa pagpasok sa paggawa ng pelikula.
“I would suggest na magkaroon ka ng mentor para talagang maituro sa iyo ‘yung tamang paraan ng paggawa ng pelikula, hindi talaga basta-basta.”
Michael V. released his first film Family History last July a joint-production with Mic Test Entertainment and GMA Pictures.
Mga Pinoy sa Amerika, excited na mapanood ang 'Family History'


An undergraduate student at the University of Michigan, Ann Arbor studying Biomolecular Science and Computer Science.
I came into college interested in healthcare, with the goal of attending pharmacy school after my undergraduate studies. I discovered a passion for technology during my sophomore year, leading to a pursue of degrees in both fields. I hope to apply my skillsets in a fulfilling career, making a positive impact on the community.
When I am not studying medicine or coding, I enjoy lifting weights, doing yoga, discovering new restaurants, going on walks, binge watching TV shows, and keeping up with recent health/technology advances.
1.) Born in Hong Kong and moved to Macomb, MI in 2004.
2.) Almost became British by immigrating to the U.K. instead, where most of my extend family are.
3.) Pineapples go on pizzas. That's a fact.
4.) I went skydiving 2 days before high school graduation. One of the best experiences of my life and would highly recommend it.
5.) Squat: 320 Bench: 285 Deadlift: 405
6.) Went to the movies during one of those unlimited popcorn refills nights. Finished my movie, then went to different rooms to watch two more. Let's just say I ate a LOT of popcorn that night.
7.) I had over 7k calories once for one meal. It was unintentional, but very tasty. I think MDining actually loses money from me.
8.) Favorite dessert: Egg Tarts.
9.) When I was an intern, I was referred to as "the gravy on top" during a team meeting to estimate work output.


---
layout: guide
permalink: cyberduck
category: File Transfer
guide-title: SFTP via Cyberduck
guide-desc: The steps below will walk you through transferring files using the Cyberduck SFTP client.
guide-image: sftp_cyberduck_02.png
steps:
- step-hr: hr
- step-heading: 1. Download Cyberduck
- step-instruction: Go to http://cyberduck.io. Download and install the SFTP client for your operating system.
- step-note: "Note: Download directly from the website. Do not use the Mac/Windows App Store, as they are not free."
- step-hr: hr
- step-heading: 2. Open a connection
- step-instruction: Click the Open Connection button. Make sure to select SFTP (SSH File Transfer Protocol) from the top dropdown. Enter sftp.cusp.adrf.cloud
as the server. Use the default port of 22
. Finally, enter your username and password, then click Connect.
- step-image: sftp_cyberduck_01.png
- step-hr: hr
- step-heading: 3. Drag and drop files
- step-instruction: Once a connection is established, you will see your home folder. You may now drag and drop files to this window from your local machine.
- step-image: sftp_cyberduck_02.png
---