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JMRI is...
There are three types of consists used on DCC systems:
In this type of consist, each Locomotive is assigned the same address on the programming track, or on the main with OpsMode Programming (if supported by the command station and decoder). You can use either a long (CV17 and CV18) or short (CV1) address for a primary address consist.
A
Command Station Assisted Consist
(CSAC).
A command station assisted consist is built using a function of your command station. Command Station Assisted Consists go by the trade names listed in the table below. The table also lists the known limitations imposed by each manufacturer.
The common trait shared by all versions of CSAC is that a separate speed and direction command is sent to the track for each Locomotive that is in the consist.
JMRI provides support for consists using the Consist Tool, which is accessible through the Tools or Actions menu of any JMRI application. (There is also a
NCEspecific Consisting Tool
that works somewhat differently from the tool described here)
The Consisting tool provides a visual tool for manipulating the Decoder Assisted Consists and, on some command stations, Command Station Assisted Consists. Backing up the Consist Tool is a consist manager. The consist manager is responsible for maintaining information about existing consists and for communicating the necessary information between the consist tool and the Command Station.
To select the consist manager in use, we first need to open the defaults tab in the preferences.
Once the preferences are loaded, the choices for the consist manager will be selectable in the "Consists" column (inside the box labeled 1 in the figure above). Here we see two choices, one for a Loconet connection, labeled A and one for the Internal connection labeled B. If you have more system connections available, other options may be available as well.
If you choose option A, then you choose to use the consist manager associated with the system. This will either be a system specific consist manager or one of the generic consist managers, depending on the support of the system.
If you choose option B, then the
Internal Consist Manager
manager is used. In this case, if there is a selection made in the Command Station column (in the box labeled 2 in the figure) then the consist will be created on the system with the selected command station. This selection will cause consists to be created using a special DCC packet for creating consists.
If you choose option B and there is no selection for the command station, consists will be created on the system with an Ops Mode Programmer selected in Column 3. This selection will cause consists to be created using Operations Mode Programming. Note that if the Internal system is selected in this case, no consists will be created.
An Ecumenical Week of Accompanied Prayer
What is a Week of Accompanied Prayer?
A Week of Accompanied Prayer (WAP) is designed to help people in their prayer; it’s a bit like a retreat, but without their having to stay away from home. WAPs have elements of a residential retreat, but as the people involved continue in their normal daily lives the week takes on a richness of its own and the prayer remains closely in touch with the daily happenings in each individual’s personal life and in the world.
What happens each day on a WAP?
Participants in a WAP are asked to set aside each day time for personal prayer – typically 30 minutes – taking account of each individual’s situation. In addition, they also commit themselves to meet individually with a prayer Guide, in a quiet place at church, for about 30 minutes each day of the WAP. This meeting is an opportunity to talk about what is happening in their prayer, and its relationship with their whole faith life.
So the commitment of the WAP is about an hour each day, Monday to Friday – 30 minutes’ prayer, 30 minutes’ time with your Guide.
When does the WAP begin?
The WAP begins on the first Sunday when all involved are present and prayed for at the Parish Eucharist. After the service there is a short meeting when all of the Participants and Guides can meet one another. This session provides an opportunity for introductions, general guidance about prayer to help Participants get started, and initial conversations.
Also this is a good opportunity for Participants to meet all together so that they can be aware throughout the WAP retreat of others who are also making the week of prayer, and so foster a sense of mutual support. At the end of the week everyone will come together again to reflect upon and celebrate the event on the final Sunday morning.
Who are the Prayer Guides?
The team of Prayer Guides includes women and men of various denominations, some lay, some ordained. All will have received training in helping others in their prayer. The role of the Guide is mainly to listen actively to the Participant discuss their prayer life: to accompany the Participants during the week, encouraging each person to honour his or her own prayer and to be aware of how it may be developed. The Guide may also make suggestions about new ways to pray or help think about what makes prayer difficult for us.
Does the WAP cost anything?
The WAP is free to join, but donations to help cover the costs of the event are of course welcome. Please ask if you would like more details.
What do I do next?
Why not get in touch with us and find out more. There are just 30 spaces available so if you want to apply email [email protected] All applicants will be asked to provide a simple reference from their priest or minister.
Applications close two weeks before the WAP.
A prayer for the Hornsey Week of Accompanied Prayer
Almighty God, to whom all hearts are open, all desires known, and from whom no secrets are hidden; cleanse the thoughts of our hearts by the inspiration of your Holy Spirit, so that we may truly love you and worthily praise your holy name; through our Saviour, Jesus Christ. Amen.
Resourcing the Space
What's happening
Third post of our series on
classification from scratch
, following the previous post introducing smoothing techniques, with
(b)splines
. Consider here
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based techniques. Note that here, we do not use the “logistic” model… it is purely nonparametric.
kernel based estimated, from scratch
I like kernels because they are somehow very intuitive. With , the goal is to estimate
$\stackrel{^}{m}\left(x\right)=E(Y\mid X=x)$
m
^
(
x
)
=
E
(
Y
∣
X
=
x
)
. Heuritically, we want to compute the (conditional) expected value on the neighborhood of
$x$
x
. If we consider some spatial model, where
$x$
x
is the location, we want the expected value of some variable
$Y$
Y
, “on the neighborhood” of
$x$
x
. A natural approach is to use some administrative region (county, departement, region, etc). This means that we have a partition of
$X$
X
(the space with the variable(s) lies). This will yield the regressogram, introduced in
Tukey (1961)
. For convenience, assume some interval / rectangle / box type of partition. In the univariate case, consider
${\stackrel{^}{m}}_{a}\left(x\right)=\frac{\underset{i=1}{\overset{n}{\sum}}1({x}_{i}\in [{a}_{j},{a}_{j+1}\left)\right){y}_{i}}{\underset{i=1}{\overset{n}{\sum}}1({x}_{i}\in [{a}_{j},{a}_{j+1}\left)\right)}$
m
^
a
(
x
)
=
∑
i
=
1
n
1
(
x
i
∈
[
a
j
,
a
j
+
1
)
)
∑
i
=
1
n
1
(
x
i
∈
[
a
j
,
a
j
+
1
)
)
y
i
or the moving regressogram
$\stackrel{^}{m}\left(x\right)=\frac{\underset{i=1}{\overset{n}{\sum}}1({x}_{i}\in [x\pm h\left]\right){y}_{i}}{\underset{i=1}{\overset{n}{\sum}}1({x}_{i}\in [x\pm h\left]\right)}$
m
^
(
x
)
=
∑
i
=
1
n
1
(
x
i
∈
[
x
±
h
]
)
∑
i
=
1
n
1
(
x
i
∈
[
x
±
h
]
)
y
i
In that case, the neighborhood is defined as the interval
$(x\pm h)$
(
x
±
h
)
. That’s nice, but clearly very simplistic. If
${x}_{i}=x$
x
i
=
x
and
${x}_{j}=xh+\epsilon $
x
j
=
x
−
h
+
ε
(with
$\epsilon >0$
ε
>
0
), both observations are used to compute the conditional expected value. But if
${x}_{{j}^{\prime}}=xh\epsilon $
x
j
′
=
x
−
h
−
ε
, only
${x}_{i}$
x
i
is considered. Even if the distance between
${x}_{j}$
x
j
and
${x}_{{j}^{\prime}}$
x
j
′
is extremely extremely small. Thus, a natural idea is to use weights that are function of the distance between
${x}_{i}$
x
i
‘s and
$x$
x
.Use
$\stackrel{~}{m}\left(x\right)=\frac{\underset{i=1}{\overset{n}{\sum}}{y}_{i}\cdot {k}_{h}\left(x{x}_{i}\right)}{\underset{i=1}{\overset{n}{\sum}}{k}_{h}\left(x{x}_{i}\right)}$
m
~
(
x
)
=
∑
i
=
1
n
k
h
(
x
−
x
i
)
∑
i
=
1
n
y
i
⋅
k
h
(
x
−
x
i
)
where (classically)
${k}_{h}\left(x\right)=k\left(\frac{x}{h}\right)$
k
h
(
x
)
=
k
(
h
x
)
for some
kernel
$k$
k
(a nonnegative function that integrates to one) and some bandwidth
$h$
h
.
Usually
, kernels are denoted with capital letter
$K$
K
, but I prefer to use
$k$
k
, because it can be interpreted as the density of some random noise we add to all observations (independently).
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