Xilinx DPU Setup Guide
05 May 2022The Xilinx setup tutorials demonstrate two paths to setting up the DPU. One route provides a pre-built PetaLinux system image with code examples, precompiled example binaries, and pre-trained models. The image can be flashed onto an SD card, and you should be able to boot the board directly from the SD card. Vivado and Vitis are not required. In fact, you can plug in a display and keyboard onto the ZCU104 board and use the board in standalone mode. It is also possible to SSH into the board over a network and interact with the board over SSH. More on that later. The other route, found here, shows how to start from a board design in Vivado and work your way up to Vitis and develop custom applications [1]. That is not a part of this guide; we will stick to the first option only for now.
Getting Started
You will want to download a copy of the PetaLinux DPU system image from the below link. Select the link matching your board model, in my case, ZCU104.
https://docs.xilinx.com/r/en-US/ug1414-vitis-ai/Flashing-the-OS-Image-to-the-SD-Card
You will also need BalenaEtcher to flash the system image to your SD card. So make sure you have that installed on your system. After you have both the system image and Etcher, follow the steps shown on the same page you downloaded the system image to create your bootable SD card. In most cases, you do not have to extract the image from the compressed archive; Etcher will take care of that. However, if Etcher fails to complete the process or verify the flashed image, you may have to extract the archive first using your favorite archive manager like WinRAR.
Booting PetaLinux
Assuming you are not planning on using the board in standalone mode, there are a few things you will need to get right for this next part.
- Ensure the Xilinx platform cable drivers are installed; otherwise, you will not see anything on the serial console. The cable drivers are installed along with Vivado when you run that installation.
- Have Putty, TeraTerm, or whatever is your favorite serial console application installed and ready.
- Configure the dip switches on the ZCU104 board to boot from the SD card.
On Windows, with the USB cable connected between your board and computer, you can look at the Device Manager to see which new COM/Serial device appears when you power on the board. Two new devices should show up, and you may have to try both when you open a serial connection. Use the following configuration to open a serial link:
Baudrate: 115200
Data bits: 8
Stop bits: 1
Parity: None
Check this configuration if you see garbage on screen (in TeraTerm or whatever) when you boot the board.
When you power on the board, you should see a light sequence that goes from red to green in the area near the SD card slot. If the light is not turning green, check either the dip switches and make sure they are set to boot from the SD card or something went wrong while flashing the SD card.
You should see PetaLinux booting in your console window if everything went as expected. At the end of the boot sequence, the boot process will appear to be stuck. Just press any key, and the program will continue and drop down to a shell.
First Step
When your board is booting, the first thing you want to do is get the board connected to the network. Connect an Ethernet cable to the board and plug the other end into a switch or router on the same network as your computer.
Once you’ve plugged the board onto the network, run ip -h -f inet address
command to see the IP address assigned to eth0
link on the board. You will need it to SSH into the board.
Connecting Over SSH
ssh root@{ip_address}
When you’re ready to SSH into the board, I recommend using MobaXterm because MobaXterm gives you an all-in-one environment that can handle the file transfer, SSH, and, best of all, an X session. There are two pitfalls you want to watch out for:
- Using other terminal programs, SSH commands like
scp
for copying files between host and server will sometimes not work correctly.  - You need to establish an X session to view the output of the DPU in some cases. For example, when the output is an image or video, you will not be able to see what the DPU is doing without an X session. MobaXterm will automatically open an X window to display images. Enable the X-Session forwarding option in MobaXterm to get this working. If you see this error message,
Gtk-WARNING **: 04:15:35.330: cannot open display:
, make sure X-session forwarding is enabled and make sure you’re connected over SSH not UART.
Once you have established a connection, you can move on to the next section and copy model inputs onto the board.
Almost There
Now let’s copy the model inputs onto the board. This is the last step before we can actually run some examples. Back on the Xilinx page, under the ‘Running Vitis-AI Examples’ section, find a download link and download the examples archive. You can use wget
to download the examples directly onto the board or use MobaXterm or FileZilla.
Extract the examples archive into the VART directory.
tar -xzvf vitis_ai_runtime_r2.0.0_image_video.tar.gz -C ~/Vitis-AI/demo/VART
Running Examples
Xilinx documentation on the DPU lists all the available examples and how to run them. Below you will find all the examples and how to run them. All the example directories also contain source code which may be useful in crafting similar applications targeting the DPU.
Resnet50
# root@xilinx-zcu104-2021_2:~#
cd /home/root/Vitis-AI/demo/VART/resnet50 ./resnet50 /usr/share/vitis_ai_library/models/resnet50/resnet50.xmodel
# Output
Resnet50 Output:
# root@xilinx-zcu104-2021_2:~#
WARNING: Logging before InitGoogleLogging() is written to STDERR
I0330 04:15:34.966986 1071 main.cc:292] create running for subgraph: subgraph_conv1
Image : 001.jpg
top[0] prob = 0.982662 name = brain coral
top[1] prob = 0.008502 name = coral reef
top[2] prob = 0.006621 name = jackfruit, jak, jack
top[3] prob = 0.000543 name = puffer, pufferfish, blowfish, globefish
top[4] prob = 0.000330 name = eel
![[resnet50 output.png]]
Other Examples
Adas Detection
# root@xilinx-zcu104-2021_2:~#
cd /home/root/Vitis-AI/demo/VART/adas_detection
./adas_detection video/adas.webm /usr/share/vitis_ai_library/models/yolov3_adas_pruned_0_9/yolov3_adas_pruned_0_9.xmodel
Inception
# root@xilinx-zcu104-2021_2:~#
cd /home/root/Vitis-AI/demo/VART/inception_v1_mt_py
python3 inception_v1.py 1 /usr/share/vitis_ai_library/models/inception_v1_tf/inception_v1_tf.xmodel
Pose Detection
# root@xilinx-zcu104-2021_2:~#
cd /home/root/Vitis-AI/demo/VART/pose_detection
./pose_detection video/pose.webm /usr/share/vitis_ai_library/models/sp_net/sp_net.xmodel /usr/share/vitis_ai_library/models/ssd_pedestrian_pruned_0_97/ssd_pedestrian_pruned_0_97.xmodel
Resnet50_mt_py
# root@xilinx-zcu104-2021_2:~#
cd /home/root/Vitis-AI/demo/VART/resnet50_mt_py
python3 resnet50.py 1 /usr/share/vitis_ai_library/models/resnet50/resnet50.xmodel
Segmentation
# root@xilinx-zcu104-2021_2:~#
cd /home/root/Vitis-AI/demo/VART/segmentation
./segmentation video/traffic.webm /usr/share/vitis_ai_library/models/fpn/fpn.xmodel
Squeezenet_pytorch
# root@xilinx-zcu104-2021_2:~#
cd /home/root/Vitis-AI/demo/VART/squeezenet_pytorch
./squeezenet_pytorch /usr/share/vitis_ai_library/models/squeezenet_pt/squeezenet_pt.xmodel
Video Analysis
# root@xilinx-zcu104-2021_2:~#
cd /home/root/Vitis-AI/demo/VART/video_analysis
./video_analysis video/structure.webm /usr/share/vitis_ai_library/models/ssd_traffic_pruned_0_9/ssd_traffic_pruned_0_9.xmodel


Girivihar Society, Vimal Vihar Road,
Datta Nagar, Nandurbar.
Maharashtra.
425412
Girivihar Society, Vimal Vihar Road,
Datta Nagar, Nandurbar.
Maharashtra.
425412
That was the day when Facebook came to Rescue. We were searching for each other and suddenly Sushank found Mohini on Facebook as a strange Girl. Then these Strangers never looked back on their journey towards a successful Relationship.
The Propose was quite unusual. As we love each other, we too love Technology, So the Propose went through WhatsApp and the Reply saying "YES" also come through same channel. Well it took Sushank long to gather guts, but he made it.
Sushank : It was huge preparation I done. Lot of prerequisites, and lot of "Makhhan". But Yes I Made It.
It's a TOP SECRET! But it's ok to unveil it now. Yes we Meet Secretely. For some time it was silence, but a Compliment from Sushank worked.
Sushank : Believe me Guys, I gave her compliment and the result was she didn't even allowed me to see her face all time that Day. Horrible! wasn't it?
THE JUDGEMENT DAY!
We unveiled the Secret of our LOVE infront of our Parents. Mom said there's no Surprise, i had Doubt from beginning. That was Shocking, Really Shocking. Guys, They accepted, We Won!
That day we were in Heaven, We were so Happy. Well, we are happy now too.
The day arise for which we were waiting so long. We always wanted to make our Relation Public. And Nowadays nothing can be better than Facebook, to Announce anything Publically.
ENGAGEMENT DAY.
This is a very special day for everyone in His/Her life. Engagement means a second last step towards a Permanent Bond. Lot of people witnessed the Function, we were so much Happy and our Parents too. It was HAPPY but not the Ending, indeed Starting!
The Bridegroom
The Bride
Like every love story, it always starts with one moment that makes your heart skips a beat and your world turns around. Strangely enough, our story began with a simple Facebook message from Sushank to Mohini that changed our life forever. Our relationship then filled with long distance calls and virtual commitment between Shirpur and Pune for a good 3 years.
Oh yeah, did we mention that our first time meeting each other physically was 3 months after we committed into the relationship? Yes it was! And one funny story, the first thing that came out from Sushank when we met for the first time at Prakasha Temple was "You Look Much More Beautiful Than You Look On Social Network". And then we laughed together. That was definitely an ice breaking moment!
Fast forward to January 2015, Sushank proposed to Mohini and luckily she said... 'YES!'. This wedding will definitely be one of the most important moments in our life and we want to share the memories with people that we care the most.


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Present: RF PC DC SW TBL NW(Chair)
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This package creates short sprint (<6sec) profiles using the split times, or the radar gun data. Mono-exponential equation is used to estimate maximal sprinting speed (MSS), relative acceleration (TAU), and other parameters. These parameters can be used to predict kinematic and kinetics variables and to compare individuals.
# Install from CRAN
install.packages("shorts")
# Or the development version from GitHub
# install.packages("devtools")
devtools::install_github("mladenjovanovic/shorts")
{shorts} comes with two sample data sets: split_times
and radar_gun_data
with N=5 athletes. Let’s load them both:
To model sprint performance using split times, distance will be used as predictor and time as target. Since split_times
contains data for multiple athletes, let’s extract only one athlete and model it using shorts::model_timing_gates()
function.
| athlete | bodyweight | distance | time |
|---|---|---|---|
| Kimberley | 55 | 5 | 1.158 |
| Kimberley | 55 | 10 | 1.893 |
| Kimberley | 55 | 15 | 2.541 |
| Kimberley | 55 | 20 | 3.149 |
| Kimberley | 55 | 30 | 4.313 |
| Kimberley | 55 | 40 | 5.444 |
shorts::model_timing_gates()
returns an object with parameters
, model_fit
, model
returned from minpack.lm::nlsLM()
function and data
used to estimate parameters. Parameters estimated using mono-exponential equation are maximal sprinting speed (MSS), and relative acceleration (TAU). Additional parameters computed from MSS and TAU are maximal acceleration (MAC) and maximal relative power (PMAX) (which is calculated as MAC*MSS/4).
kimberley_profile <- shorts::model_timing_gates(
distance = kimberley_data$distance,
time = kimberley_data$time
)
kimberley_profile
#> Estimated model parameters
#> --------------------------
#> MSS TAU MAC PMAX
#> 8.59 0.81 10.59 22.74
#>
#> Model fit estimators
#> --------------------
#> RSE R_squared minErr maxErr maxAbsErr RMSE MAE MAPE
#> 0.034 1.000 -0.053 0.027 0.053 0.028 0.023 1.193
summary(kimberley_profile)
#>
#> Formula: time ~ TAU * I(LambertW::W(-exp(1)^(-distance/(MSS * TAU) - 1))) +
#> distance/MSS + TAU
#>
#> Parameters:
#> Estimate Std. Error t value Pr(>|t|)
#> MSS 8.5911 0.1225 70.1 2.5e-07 ***
#> TAU 0.8113 0.0458 17.7 6.0e-05 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 0.034 on 4 degrees of freedom
#>
#> Number of iterations to convergence: 5
#> Achieved convergence tolerance: 1.49e-08
coef(kimberley_profile)
#> MSS TAU MAC PMAX
#> 8.59 0.81 10.59 22.74
To return the predicted outcome (in this case time variable), use predict()
function:
predict(kimberley_profile)
#> [1] 1.2 1.9 2.5 3.1 4.3 5.5
To create a simple plot, use S3 plot()
method:
If you are interested in calculating average split velocity, use shorts::format_splits()
kable(shorts::format_splits(
distance = kimberley_data$distance,
time = kimberley_data$time
))
| split | split_distance_start | split_distance_stop | split_distance | split_time_start | split_time_stop | split_time | split_mean_velocity | split_mean_acceleration |
|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 5 | 5 | 0 | 1.158 | 1.158 | 4.317789…. | 3.728660…. |
| 2 | 5 | 10 | 5 | 1.158 | 1.893 | 0.735 | 6.802721…. | 3.380859…. |
| 3 | 10 | 15 | 5 | 1.893 | 2.541 | 0.648 | 7.716049…. | 1.409457…. |
| 4 | 15 | 20 | 5 | 2.541 | 3.149 | 0.608 | 8.223684…. | 0.834925…. |
| 5 | 20 | 30 | 10 | 3.149 | 4.313 | 1.164 | 8.591065…. | 0.315619…. |
| 6 | 30 | 40 | 10 | 4.313 | 5.444 | 1.131 | 8.841732…. | 0.221633…. |
Let’s plot observed vs fitted split times. For this we can use data
returned from shorts::model_timing_gates()
since it contains pred_time
column.
ggplot(kimberley_profile$data, aes(x = distance)) +
theme_bw() +
geom_point(aes(y = time)) +
geom_line(aes(y = pred_time)) +
xlab("Distance (m)") +
ylab("Time (s)")
To plot predicted velocity, acceleration, air resistance, force, and power over distance, use shorts:predict_XXX()
. Please note that to calculate force, air resistance, and power, we need Kimberley’s bodymass and height (as well as other characteristics such as air pressure, temperature and wind - see get_air_resistance()
function).
kimberley_bodymass <- 60 # in kilograms
kimberley_bodyheight <- 1.7 # in meters
kimberley_pred <- tibble(
distance = seq(0, 40, length.out = 1000),
# Velocity
pred_velocity = shorts::predict_velocity_at_distance(
distance,
kimberley_profile$parameters$MSS,
kimberley_profile$parameters$TAU
),
# Acceleration
pred_acceleration = shorts::predict_acceleration_at_distance(
distance,
kimberley_profile$parameters$MSS,
kimberley_profile$parameters$TAU
),
# Air resistance
pred_air_resistance = shorts::predict_air_resistance_at_distance(
distance,
kimberley_profile$parameters$MSS,
kimberley_profile$parameters$TAU,
bodymass = kimberley_bodymass,
bodyheight = kimberley_bodyheight
),
# Force
pred_force = shorts::predict_force_at_distance(
distance,
kimberley_profile$parameters$MSS,
kimberley_profile$parameters$TAU,
bodymass = kimberley_bodymass,
bodyheight = kimberley_bodyheight
),
# Power
pred_power = shorts::predict_power_at_distance(
distance,
kimberley_profile$parameters$MSS,
kimberley_profile$parameters$TAU,
bodymass = kimberley_bodymass,
bodyheight = kimberley_bodyheight
),
)
# Convert to long
kimberley_pred <- gather(kimberley_pred, "metric", "value", -distance)
ggplot(kimberley_pred, aes(x = distance, y = value)) +
theme_bw() +
geom_line() +
facet_wrap(~metric, scales = "free_y") +
xlab("Distance (m)") +
ylab(NULL)
To do prediction simpler, use shorts::predict_kinematics()
function. This will provide kinetics and kinematics for 0-6s sprint using 100Hz.
predicted_kinematics <- predict_kinematics(
kimberley_profile,
bodymass = kimberley_bodymass,
bodyheight = kimberley_bodyheight
)
kable(head(predicted_kinematics))
| time | distance | velocity | acceleration | bodymass | net_horizontal_force | air_resistance | horizontal_force | horizontal_force_relative | vertical_force | resultant_force | resultant_force_relative | power | relative_power | RF | force_angle |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.00 | 0.00 | 0.00 | 11 | 60 | 635 | 0.00 | 635 | 11 | 589 | 866 | 14 | 0 | 0.0 | 0.73 | 43 |
| 0.01 | 0.00 | 0.11 | 10 | 60 | 628 | 0.00 | 628 | 10 | 589 | 860 | 14 | 66 | 1.1 | 0.73 | 43 |
| 0.02 | 0.00 | 0.21 | 10 | 60 | 620 | 0.01 | 620 | 10 | 589 | 855 | 14 | 130 | 2.2 | 0.73 | 44 |
| 0.03 | 0.00 | 0.31 | 10 | 60 | 612 | 0.02 | 612 | 10 | 589 | 849 | 14 | 191 | 3.2 | 0.72 | 44 |
| 0.04 | 0.01 | 0.41 | 10 | 60 | 605 | 0.04 | 605 | 10 | 589 | 844 | 14 | 250 | 4.2 | 0.72 | 44 |
| 0.05 | 0.01 | 0.51 | 10 | 60 | 597 | 0.06 | 597 | 10 | 589 | 839 | 14 | 307 | 5.1 | 0.71 | 45 |
To get model residuals, use residuals()
function:
residuals(kimberley_profile)
#> [1] -0.053 -0.004 0.020 0.027 0.014 -0.022
Package {shorts} comes with find_XXX()
family of functions that allow finding peak power and it’s location, as well as critical distance over which velocity, acceleration, or power drops below certain threshold:
# Peak power and location
shorts::find_max_power_distance(
kimberley_profile$parameters$MSS,
kimberley_profile$parameters$TAU
)
#> $max_power
#> [1] 163
#>
#> $distance
#> [1] 30
# Distance over which power is over 50%
shorts::find_power_critical_distance(
MSS = kimberley_profile$parameters$MSS,
MAC = kimberley_profile$parameters$MAC,
percent = 0.5
)
#> $lower
#> [1] 0.086
#>
#> $upper
#> [1] 8.4
# Distance over which acceleration is under 50%
shorts::find_acceleration_critical_distance(
MSS = kimberley_profile$parameters$MSS,
MAC = kimberley_profile$parameters$MAC,
percent = 0.5
)
#> [1] 1.3
# Distance over which velocity is over 95%
shorts::find_velocity_critical_distance(
MSS = kimberley_profile$parameters$MSS,
MAC = kimberley_profile$parameters$MAC,
percent = 0.95
)
#> [1] 14
The radar gun data is modeled using measured velocity as target variable and time as predictor. Individual analysis is performed using shorts::model_radar_gun()
function. Let’s do analysis for Jim:
jim_data <- filter(radar_gun_data, athlete == "Jim")
jim_profile <- shorts::model_radar_gun(
time = jim_data$time,
velocity = jim_data$velocity
)
jim_profile
#> Estimated model parameters
#> --------------------------
#> MSS TAU MAC PMAX TC
#> 8.0e+00 8.9e-01 9.0e+00 1.8e+01 1.1e-04
#>
#> Model fit estimators
#> --------------------
#> RSE R_squared minErr maxErr maxAbsErr RMSE MAE MAPE
#> 0.051 0.999 -0.164 0.151 0.164 0.051 0.039 Inf
summary(jim_profile)
#>
#> Formula: velocity ~ MSS * (1 - exp(1)^(-(time + TC)/TAU))
#>
#> Parameters:
#> Estimate Std. Error t value Pr(>|t|)
#> MSS 7.99801 0.00319 2504.54 <2e-16 ***
#> TAU 0.88880 0.00218 407.81 <2e-16 ***
#> TC 0.00011 0.00123 0.09 0.93
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 0.051 on 597 degrees of freedom
#>
#> Number of iterations to convergence: 6
#> Achieved convergence tolerance: 1.49e-08
plot(jim_profile) +
theme_bw()
The object returned from shorts::model_radar_gun()
is same as object returned from shorts::model_timing_gates()
. Let’s plot Jim’s measured velocity and predicted velocity:
ggplot(jim_profile$data, aes(x = time)) +
theme_bw() +
geom_line(aes(y = velocity), alpha = 0.5) +
geom_line(aes(y = pred_velocity), color = "red", alpha = 0.5) +
xlab("Time (s)") +
ylab("Velocity (m/s)")
Rather than estimating MSS, shorts::model_radar_gun()
function allows you to utilize peak velocity observed in the data as MSS. This is done by setting the use_observed_MSS
parameter to TRUE
:
jim_profile <- shorts::model_radar_gun(
time = jim_data$time,
velocity = jim_data$velocity,
use_observed_MSS = TRUE
)
jim_profile
#> Estimated model parameters
#> --------------------------
#> MSS TAU MAC PMAX TC
#> 8.095 0.933 8.678 17.563 0.011
#>
#> Model fit estimators
#> --------------------
#> RSE R_squared minErr maxErr maxAbsErr RMSE MAE MAPE
#> 0.080 0.999 -0.229 0.183 0.229 0.080 0.064 Inf
summary(jim_profile)
#>
#> Formula: velocity ~ MSS * (1 - exp(1)^(-(time + TC)/TAU))
#>
#> Parameters:
#> Estimate Std. Error t value Pr(>|t|)
#> MSS 8.09500 0.00521 1554.10 < 2e-16 ***
#> TAU 0.93279 0.00361 258.74 < 2e-16 ***
#> TC 0.01118 0.00203 5.52 5.1e-08 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 0.08 on 597 degrees of freedom
#>
#> Number of iterations to convergence: 5
#> Achieved convergence tolerance: 1.49e-08
Some tether devices provide data out in a velocity-at-distance format. In this case, velocity is the outcome variable and distance is the predictor. To estimate sprint profiles from tether data, use model_tether()
function:
distance <- c(5, 10, 20, 30, 40)
velocity <- predict_velocity_at_distance(distance, MSS = 10, MAC = 8) +
rnorm(length(distance), 0, 0.1)
m1 <- model_tether(distance = distance, velocity = velocity)
df <- data.frame(
distance = distance,
obs_velocity = velocity
)
plot(m1) +
geom_point(data = df, aes(x = distance, y = obs_velocity))
Setting use_observed_MSS
parameter to TRUE
in the model_tether()
function also allows you to use observed peak velocity as MSS.
To estimate Force-Velocity profile using approach by Samozino et al. (2016, 2022) use shorts::make_FV_profile()
:
kimberley_fv <- shorts::make_FV_profile(
MSS = kimberley_profile$parameters$MSS,
MAC = kimberley_profile$parameters$MAC,
# These are needed to estimate air resistance
bodymass = kimberley_bodymass,
bodyheight = kimberley_bodyheight
)
kimberley_fv
#> Estimated Force-Velocity Profile
#> --------------------------------
#> bodymass F0 F0_rel V0 Pmax
#> 6.0e+01 6.3e+02 1.1e+01 8.8e+00 1.4e+03
#> Pmax_rel FV_slope RFmax_cutoff RFmax Drf
#> 2.3e+01 -1.2e+00 3.0e-01 6.0e-01 -1.0e-01
#> RSE_FV RSE_Drf F0_poly F0_poly_rel V0_poly
#> 1.0e+00 9.5e-03 6.4e+02 1.1e+01 8.8e+00
#> Pmax_poly Pmax_poly_rel FV_slope_poly
#> 1.4e+03 2.3e+01 -1.2e+00
plot(kimberley_fv) +
theme_bw()
You have probably noticed that estimated MSS and TAU were a bit too high for splits data. Biased estimates are due to differences in starting positions and timing triggering methods for certain measurement approaches (e.g. starting behind first timing gate, or allowing for body rocking).
Here I will provide quick summary. Often, this bias in estimates is dealt with by using heuristic rule of thumb of adding time correction (time_correction
) to split times (e.g. from 0.3-0.5sec; see more in Haugen et al., 2012). To do this, just add time correction to time split:
kimberley_profile_fixed_TC <- shorts::model_timing_gates(
distance = kimberley_data$distance,
time = kimberley_data$time + 0.3
)
kimberley_profile_fixed_TC
#> Estimated model parameters
#> --------------------------
#> MSS TAU MAC PMAX
#> 9.1 1.4 6.6 15.1
#>
#> Model fit estimators
#> --------------------
#> RSE R_squared minErr maxErr maxAbsErr RMSE MAE MAPE
#> 0.0100 1.0000 -0.0077 0.0164 0.0164 0.0081 0.0064 0.2857
summary(kimberley_profile_fixed_TC)
#>
#> Formula: time ~ TAU * I(LambertW::W(-exp(1)^(-distance/(MSS * TAU) - 1))) +
#> distance/MSS + TAU
#>
#> Parameters:
#> Estimate Std. Error t value Pr(>|t|)
#> MSS 9.1278 0.0536 170.4 7.1e-09 ***
#> TAU 1.3776 0.0213 64.7 3.4e-07 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 0.01 on 4 degrees of freedom
#>
#> Number of iterations to convergence: 5
#> Achieved convergence tolerance: 1.49e-08
coef(kimberley_profile_fixed_TC)
#> MSS TAU MAC PMAX
#> 9.1 1.4 6.6 15.1
Instead of providing for TC
, this parameter can be estimated using shorts::model_timing_gates_TC()
.
kimberley_profile_TC <- shorts::model_timing_gates_TC(
distance = kimberley_data$distance,
time = kimberley_data$time
)
kimberley_profile_TC
#> Estimated model parameters
#> --------------------------
#> MSS TAU MAC PMAX TC
#> 8.97 1.23 7.27 16.31 0.23
#>
#> Model fit estimators
#> --------------------
#> RSE R_squared minErr maxErr maxAbsErr RMSE MAE MAPE
#> 0.00113 1.00000 -0.00118 0.00121 0.00121 0.00080 0.00066 0.02824
Instead of estimating TC
, {shorts} package features a method of estimating flying start distance (FD
):
kimberley_profile_FD <- shorts::model_timing_gates_FD(
distance = kimberley_data$distance,
time = kimberley_data$time
)
kimberley_profile_FD
#> Estimated model parameters
#> --------------------------
#> MSS TAU MAC PMAX FD
#> 9.0 1.3 7.0 15.7 0.3
#>
#> Model fit estimators
#> --------------------
#> RSE R_squared minErr maxErr maxAbsErr RMSE MAE MAPE
#> 0.00039 1.00000 -0.00040 0.00046 0.00046 0.00028 0.00024 0.00783
If you want to use fixed FD
parameter (e.g., when you know what is the flying distance), in a similar vein of using fixed TC
correction, use:
kimberley_profile_fixed_FD <- shorts::model_timing_gates_FD(
distance = kimberley_data$distance,
time = kimberley_data$time,
FD = 0.5
)
kimberley_profile_fixed_FD
#> Estimated model parameters
#> --------------------------
#> MSS TAU MAC PMAX FD
#> 9.2 1.5 6.2 14.3 0.5
#>
#> Model fit estimators
#> --------------------
#> RSE R_squared minErr maxErr maxAbsErr RMSE MAE MAPE
#> 0.0112 1.0000 -0.0079 0.0155 0.0155 0.0079 0.0067 0.3499
model_timing_gates_()
family of functions come with LOOCV feature that is performed by setting the function parameter LOOCV = TRUE
. This feature is very useful for checking model parameters robustness and model predictions on unseen data. LOOCV involve iterative model building and testing by removing observation one by one and making predictions for them. Let’s use Kimberley again, but this time perform LOOCV:
kimberley_profile_LOOCV <- shorts::model_timing_gates(
distance = kimberley_data$distance,
time = kimberley_data$time,
LOOCV = TRUE
)
kimberley_profile_LOOCV
#> Estimated model parameters
#> --------------------------
#> MSS TAU MAC PMAX
#> 8.59 0.81 10.59 22.74
#>
#> Model fit estimators
#> --------------------
#> RSE R_squared minErr maxErr maxAbsErr RMSE MAE MAPE
#> 0.034 1.000 -0.053 0.027 0.053 0.028 0.023 1.193
#>
#>
#> Cross-Validation
#> ------------------------------
#> Parameters:
#> # A tibble: 6 × 4
#> MSS TAU MAC PMAX
#> <dbl> <dbl> <dbl> <dbl>
#> 1 8.69 0.856 10.2 22.1
#> 2 8.60 0.815 10.5 22.7
#> 3 8.56 0.795 10.8 23.0
#> 4 8.57 0.797 10.8 23.0
#> 5 8.61 0.813 10.6 22.8
#> 6 8.39 0.760 11.1 23.2
#>
#> Testing model fit:
#> RSE R_squared minErr maxErr maxAbsErr RMSE MAE MAPE
#> NA 0.999 -0.080 0.034 0.080 0.047 0.039 1.723
Box-plot is suitable method for plotting estimated parameters:
LOOCV_parameters <- gather(kimberley_profile_LOOCV$CV$parameters)
ggplot(LOOCV_parameters, aes(y = value)) +
theme_bw() +
geom_boxplot() +
facet_wrap(~key, scales = "free") +
ylab(NULL) +
theme(axis.ticks.x = element_blank(), axis.text.x = element_blank())
Let’s plot model LOOCV predictions and training (when using all data set) predictions against observed performance:
kimberley_data <- kimberley_data %>%
mutate(
pred_time = predict(kimberley_profile_LOOCV),
LOOCV_time = kimberley_profile_LOOCV$CV$data$pred_time
)
ggplot(kimberley_data, aes(x = distance)) +
theme_bw() +
geom_point(aes(y = time)) +
geom_line(aes(y = pred_time), color = "black") +
geom_line(aes(y = LOOCV_time), color = "red") +
xlab("Distance (m)") +
ylab("Time (s)")
Let’s plot predicted velocity using LOOCV estimate parameters to check robustness of the model predictions:
plot_data <- kimberley_profile_LOOCV$CV$parameters %>%
mutate(LOOCV = row_number())
plot_data <- expand_grid(
data.frame(time = seq(0, 6, length.out = 100)),
plot_data
) %>%
mutate(
LOOCV_velocity = predict_velocity_at_time(
time = time,
MSS = MSS,
MAC = MAC
),
velocity = predict_velocity_at_time(
time = time,
MSS = kimberley_profile_LOOCV$parameters$MSS,
MAC = kimberley_profile_LOOCV$parameters$MAC
)
)
ggplot(plot_data, aes(x = time, y = LOOCV_velocity, group = LOOCV)) +
theme_bw() +
geom_line(alpha = 0.8) +
geom_line(aes(y = velocity), color = "red", size = 0.5) +
xlab("Time (sec)") +
ylab("Velocity (m/s)")
Cross-validation implemented in model_radar_gun()
function involves using n-folds, set by using CV=
parameter:
jim_profile_CV <- shorts::model_radar_gun(
time = jim_data$time,
velocity = jim_data$velocity,
CV = 10
)
jim_profile_CV
#> Estimated model parameters
#> --------------------------
#> MSS TAU MAC PMAX TC
#> 8.0e+00 8.9e-01 9.0e+00 1.8e+01 1.1e-04
#>
#> Model fit estimators
#> --------------------
#> RSE R_squared minErr maxErr maxAbsErr RMSE MAE MAPE
#> 0.051 0.999 -0.164 0.151 0.164 0.051 0.039 Inf
#>
#>
#> Cross-Validation
#> ------------------------------
#> Parameters:
#> # A tibble: 10 × 5
#> MSS TAU MAC PMAX TC
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 8.00 0.889 8.99 18.0 0.000320
#> 2 8.00 0.889 8.99 18.0 0.000138
#> 3 8.00 0.889 9.00 18.0 0.000194
#> 4 8.00 0.888 9.00 18.0 0.0000546
#> 5 8.00 0.889 9.00 18.0 0.0000358
#> 6 8.00 0.888 9.00 18.0 -0.000136
#> 7 8.00 0.888 9.01 18.0 -0.000279
#> 8 8.00 0.889 8.99 18.0 0.000417
#> 9 8.00 0.889 9.00 18.0 0.000175
#> 10 8.00 0.889 9.00 18.0 0.000141
#>
#> Testing model fit:
#> RSE R_squared minErr maxErr maxAbsErr RMSE MAE MAPE
#> NA 0.999 -0.166 0.152 0.166 0.051 0.039 Inf
Using the method outlined in Samozino et al (2022), one can find the optimal profiles, as well as the profile imbalance (compared to the optimal), for both sprint profiles (i.e., MSS and MAC) and Force-Velocity (FV). In addition to this, one can probe the profiles (i.e., increase V0/F0 or MSS/MAC for say 2.5% to check which improvement yield more improvement in sprint time). The following graph depicts estimate profile imbalances. Note that >100% is velocity deficit (i.e., increasing velocity; MSS or V0; will yield more improvement in sprint times), while <100% is force deficit.
MSS <- 10
MAC <- 8
bodymass <- 75
fv <- make_FV_profile(MSS, MAC, bodymass)
opt_df <- tibble(
dist = seq(5, 50, by = 5)
) %>%
mutate(
`Sprint Profile` = optimal_MSS_MAC(
distance = dist,
MSS,
MAC
)[["profile_imb"]],
`FV Profile` = optimal_FV(
distance = dist,
fv$F0_poly,
fv$V0_poly,
bodymass
)[["profile_imb"]],
`FV Profile (PeakPower)` = optimal_FV(
distance = dist,
fv$F0_poly,
fv$V0_poly,
bodymass,
method = "peak"
)[["profile_imb"]],
`Probe FV` = probe_FV(
distance = dist,
fv$F0_poly,
fv$V0_poly,
bodymass
)[["profile_imb"]],
`Probe MSS/MAC` = probe_MSS_MAC(
distance = dist,
MSS,
MAC
)[["profile_imb"]]
) %>%
pivot_longer(-dist, names_to = "profile")
opt_dist <- tibble(
`Sprint Profile` = find_optimal_distance(
MSS,
MAC,
optimal_func = optimal_MSS_MAC
),
`FV Profile` = find_optimal_distance(
fv$F0_poly,
fv$V0_poly,
bodymass,
optimal_func = optimal_FV
),
`FV Profile (PeakPower)` = find_optimal_distance(
fv$F0_poly,
fv$V0_poly,
bodymass,
optimal_func = optimal_FV,
method = "peak"
),
`Probe FV` = find_optimal_distance(
fv$F0_poly,
fv$V0_poly,
bodymass,
optimal_func = probe_FV
),
`Probe MSS/MAC` = find_optimal_distance(
MSS,
MAC,
optimal_func = probe_MSS_MAC
)
) %>%
pivot_longer(cols = 1:5, names_to = "profile")
ggplot(opt_df, aes(x = dist, y = value, color = profile)) +
theme_bw() +
geom_hline(yintercept = 100, linetype = "dashed", alpha = 0.6) +
geom_line() +
geom_point(data = opt_dist, aes(x = value, y = 100), size = 2) +
xlab("Distance (m)") +
ylab("Profile imbalance")
Jovanović, M., Vescovi, J.D. (2022). {shorts}: An R Package for Modeling Short Sprints. International Journal of Strength and Conditioning, 2(1). https://doi.org/10.47206/ijsc.v2i1.74
Jovanović M. (2023). Bias in estimated short sprint profiles using timing gates due to the flying start: simulation study and proposed solutions. Computer Methods in Biomechanics and Biomedical Engineering:1–11. https://doi.org/10.1080/10255842.2023.2170713
Vescovi, JD and Jovanović, M. (2021). Sprint Mechanical Characteristics of Female Soccer Players: A Retrospective Pilot Study to Examine a Novel Approach for Correction of Timing Gate Starts. Front Sports Act Living 3: 629694, 2021. https://doi.org/10.3389/fspor.2021.629694
To cite {shorts}, please use the following command to get the BibTex entry:
citation("shorts")
Please refer to these publications for more information on short sprints modeling using mono-exponential equation:
Chelly SM, Denis C. 2001. Leg power and hopping stiffness: relationship with sprint running performance: Medicine and Science in Sports and Exercise:326–333. DOI: 10.1097/00005768-200102000-00024.
Clark KP, Rieger RH, Bruno RF, Stearne DJ. 2017. The NFL Combine 40-Yard Dash: How Important is Maximum Velocity? Journal of Strength and Conditioning Research:1. DOI: 10.1519/JSC.0000000000002081.
Furusawa K, Hill AV, and Parkinson JL. The dynamics of” sprint” running. Proceedings of the Royal Society of London. Series B, Containing Papers of a Biological Character 102 (713): 29-42, 1927
Greene PR. 1986. Predicting sprint dynamics from maximum-velocity measurements. Mathematical Biosciences 80:1–18. DOI: 10.1016/0025-5564(86)90063-5.
Haugen TA, Tønnessen E, Seiler SK. 2012. The Difference Is in the Start: Impact of Timing and Start Procedure on Sprint Running Performance: Journal of Strength and Conditioning Research 26:473–479. DOI: 10.1519/JSC.0b013e318226030b.
Samozino P, Rabita G, Dorel S, Slawinski J, Peyrot N, Saez de Villarreal E, Morin J-B. 2016. A simple method for measuring power, force, velocity properties, and mechanical effectiveness in sprint running: Simple method to compute sprint mechanics. Scandinavian Journal of Medicine & Science in Sports 26:648–658. DOI: 10.1111/sms.12490.
Samozino P. 2018. A Simple Method for Measuring Force, Velocity and Power Capabilities and Mechanical Effectiveness During Sprint Running. In: Morin J-B, Samozino P eds. Biomechanics of Training and Testing. Cham: Springer International Publishing, 237–267. DOI: 10.1007/978-3-319-05633-3_11.
Samozino P, Peyrot N, Edouard P, Nagahara R, Jimenez‐Reyes P, Vanwanseele B, Morin J. 2022. Optimal mechanical force‐velocity profile for sprint acceleration performance.Scandinavian Journal of Medicine & Science in Sports 32:559–575. DOI: 10.1111/sms.14097.


Your server is running but the Audio Station web interface has not loaded so you can currently only use the iPhone and Android apps to access the server
Download the DS Audio app from:
Download the DS Audio app from:
Audio Station's web interface requires files copyrighted to Synology that cannot be distributed with Open Audio Server.
To copy these files requires either a Synology NAS or an Xpenology virtual machine running Synology DSM. There are premade Xpenology virtual machines for VMWare Workstation (paid) and VMWare Player (free) and guides on how to install in your own virtual machine but you have to find them yourself.
Once you have DSM running, either way, Audio Station must be installed from the "Package Manager", and SSH must be enabled in the "Control Panel" under "Terminal & SNMP".
scp -r administrator@<IP_ADDRESS>:/usr/syno/synoman/ /some/where
Then open Audio Station in your web browser by right clicking the icon in DSM and selecting launch in new window.
Find your browser setting to "View source" and save all the text into `dsaudio.html`.
Update your server settings
$ export SYNOMAN_PATH=/some/where/synoman
$ export DSAUDIO_HTML_PATH=/some/where/dsaudio.html


|
Don't
Tell Me
(Madonna) |
|
Don't
tell me to stop
Tell the rain not to drop Tell the wind not to blow 'Cause you said so, mmm Tell the sun not to shine Not to get up this time, no, no Let it fall by the way But don't leave me where I lay down Chorus: Tell me love isn't true It's just something that we do Tell me everything I'm not (first time:) But please don't tell me to stop (all other times:) But don't ever tell me to stop Tell the leaves not to turn But don't ever tell me I'll learn, no, no Take the black off a crow But don't tell me I have to go Tell the bed not to lay Like the open mouth of a grave, yeah Not to stare up at me Like a calf down on its knees (chorus) (chorus, prefixing 1st and 3rd lines with "Don't you ever") [Don't you ever] Please don't Please don't Please don't tell me to stop [Don't you ever] Don't you ever Don't ever tell me to stop [Tell the rain not to drop] Tell the bed not to lay Like a open mouth of a grave, yeah Not to stare up at me Like a calf down on its knees |


Before Change
weight = 1./(1 + np.exp(self._alpha * num))
weight = normalize(weight, norm="l1")
correct_num = self.processed_dsel[idx_neighbors, :]
correct = np.zeros((query.shape[0], self.k, self.n_classifiers))
for i in range(self.n_classifiers):
correct[:, :, i] = correct_num[:, :, i] * weight
// calculate the classifiers mean accuracy for all samples/base classifier
accuracy = np.mean(correct, axis=1)
return accuracy
After Change
correct_num = self.processed_dsel[idx_neighbors, :]
// Apply the weights to each sample for each base classifier
competence = correct_num * weight[:, :, np.newaxis]
// calculate the classifiers mean competence for all samples/base classifier
competence = np.sum(competence, axis=1)
return competence


|
Fixed Point Arithmetic
0
Fixed-point math library.
|
Modules | |
| Quaternion Errors | |
| This functions calculate the rotation needed to go from 'pos' (current position) to 'setp' (setpoint), in the same reference frame as 'pos'. | |
Data Structures | |
| struct | quat |
| Single precision quaternion. More... | |
| struct | dquat |
| Double precision quaternion. More... | |
Macros | |
| #define | UNIT_QUAT {{FRAC_1_V}, VEC0} |
| Literal for Unit quaternion. More... | |
| #define | UNIT_DQUAT {{DFRAC_1_V}, VEC0} |
| Literal for double precision Unit quaternion. More... | |
Functions | |
| quat | dq_to_q (dquat q) |
| Truncate double precision quaternion to single precision. More... | |
| frac | q_xnormerror (quat q) |
| Pseudo-error of the quaternion norm. More... | |
| dquat | q_scale_dq (quat q, frac f) |
| Scale a quaternion by a fractional, return double precision quaternion. More... | |
| quat | q_scale (quat q, frac f) |
| Scale a quaternion by a fractional, return simple precision quaternion. More... | |
| quat | q_conj (quat q) |
| Conjugate quaternion. More... | |
| quat | q_mul (quat q, quat p) |
| Quaternion multiplication, return value is simple precision. More... | |
| dquat | q_mul_s_dq (quat q, quat p, int f) |
| Scaled Quaternion multiplication, return value is double precision. More... | |
| dquat | q_mul_dq (quat q, quat p) |
| Quaternion multiplication, return value is double precision. More... | |
| dquat | dq_add (dquat q, dquat p) |
| Quaternion addition (double precision). More... | |
| quat | q_add (quat q, quat p) |
| Quaternion addition (single precision) More... | |
| vec3 | q_rot (quat q, vec3 v) |
| Rotate 3D vector by quaternion. More... | |
| quat | q_xrenorm (quat q) |
| Renormalize quaternion (single precision) More... | |
| dquat | dq_xrenorm (dquat q) |
| Renormalize quaternion. More... | |
| quat | q_udecompose (quat q, vec_axis axis) |
| Extract a component from a unit quaternion. More... | |
| struct quat |
Single precision quaternion.
Components are represented by value of frac type
Definition at line 52 of file quaternion_types.h.
| Data Fields | ||
|---|---|---|
| frac | r |
Scalar part |
| vec3 | v |
Vector part |
| struct dquat |
Double precision quaternion.
Components are represented by value of dfrac type.
Definition at line 61 of file quaternion_types.h.
| Data Fields | ||
|---|---|---|
| dfrac | r |
Scalar part |
| dvec3 | v |
Vector part |
| #define UNIT_DQUAT {{DFRAC_1_V}, VEC0} |
Literal for double precision Unit quaternion.
Definition at line 71 of file quaternion_types.h.
| #define UNIT_QUAT {{FRAC_1_V}, VEC0} |
Literal for Unit quaternion.
Definition at line 69 of file quaternion_types.h.
Quaternion addition (double precision).
Definition at line 213 of file quaternion.h.
Truncate double precision quaternion to single precision.
Definition at line 51 of file quaternion.h.
Quaternion addition (single precision)
Definition at line 230 of file quaternion.h.
Quaternion multiplication, return value is simple precision.
Definition at line 131 of file quaternion.h.
Quaternion multiplication, return value is double precision.
Definition at line 187 of file quaternion.h.
Scaled Quaternion multiplication, return value is double precision.
Definition at line 159 of file quaternion.h.
Rotate 3D vector by quaternion.
Creates a purely imaginary quaternion V with vector part equal to v and returns the vector part of q*(V*q')'.
Definition at line 250 of file quaternion.h.
Scale a quaternion by a fractional, return simple precision quaternion.
Definition at line 99 of file quaternion.h.
Scale a quaternion by a fractional, return double precision quaternion.
Definition at line 84 of file quaternion.h.
Extract a component from a unit quaternion.
| q | A unit quaternion. |
| axis | Axis to return. |
Definition at line 302 of file quaternion.h.
Pseudo-error of the quaternion norm.
Calculates an approximation to (1 - norm(q)) The approximation used is sqrt(x) = 0.5(x + 1) for x near 1
Definition at line 72 of file quaternion.h.
Renormalize quaternion (single precision)
This function attempts to bring the norm of the quaternion closer to 1. It only works for quaternions whose norm is already close to 1. It uses and approximation for the norm (q_xnormerror).
Definition at line 268 of file quaternion.h.


Before Change
elif amount_number == 0:
ret = "ศูนย์บาทถ้วน"
else:
amount_number = number_format(amount_number, 2).replace(" ", "")
pt = amount_number.find(".")
number, fraction = "", ""
amount_number1 = amount_number.split(".")
if not pt:
number = amount_number
else:
amount_number = amount_number.split(".")
number = amount_number[0]
fraction = int(amount_number1[1])
number = ast.literal_eval(number.replace(",", ""))
baht = num_to_thaiword(number)
if baht != "":
ret = "".join([ret, baht, "บาท"])
After Change
elif number == 0:
ret = "ศูนย์บาทถ้วน"
else:
num_int, num_dec = "{:.2f}".format(number).split(".")
num_int = int(num_int)
num_dec = int(num_dec)
baht = num_to_thaiword(num_int)


4 writes to _disableOptimizations
System.Management.Automation (4)
engine\parser\VariableAnalysis.cs (4)
124
_disableOptimizations
= disableOptimizations;
253
_disableOptimizations
= true;
286
_disableOptimizations
= true;
301
_disableOptimizations
= true;
1 reference to _disableOptimizations
System.Management.Automation (1)
engine\parser\VariableAnalysis.cs (1)
106
forceNoOptimizing = visitor.
_disableOptimizations
;


param_attr¶
ParamAttr¶
-
class
paddle.fluid.param_attr.
ParamAttr
(name=None, initializer=None, learning_rate=1.0, regularizer=None, trainable=True, gradient_clip=None, do_model_average=None)
WeightNormParamAttr¶
-
class
paddle.fluid.param_attr.
WeightNormParamAttr
(dim=None, **kwargs) Used for weight normalization. Any field in ParamAttr can also be set here. Besides, an extra field dim can be set to indicate the dimension except which to normalize.


BUYCheckoutError Constants Reference
| Declared in | BUYError.h |
|---|
BUYCheckoutError
A collection of enums for error codes specific to the SDK
Definition
typedef NS_ENUM(NSUInteger, BUYCheckoutError ) {
BUYShopifyError_CartFetchError,
BUYShopifyError_NoShippingMethodsToAddress,
BUYShopifyError_NoProductSpecified,
BUYShopifyError_InvalidProductID,
BUYShopifyError_NoCollectionIdSpecified,
BUYShopifyError_NoGiftCardSpecified,
BUYShopifyError_NoCreditCardSpecified,
BUYShopifyError_NoApplePayTokenSpecified,
BUYShopifyError_InvalidCheckoutObject,
};
Constants
BUYShopifyError_CartFetchError
-
An error occurred retrieving the cart for an existing web checkout with BUYStoreViewController
Declared In
BUYError.h
. BUYShopifyError_NoShippingMethodsToAddress
-
No shipping rates are available for the selected address
Declared In
BUYError.h
. BUYShopifyError_NoProductSpecified
-
No product or product ID was provided when loading a product in BUYProductViewController
Declared In
BUYError.h
. BUYShopifyError_InvalidProductID
-
The product ID or IDs provided were invalid for the shop. Check that the product are made visible on the Mobile App channel on /admin.
Declared In
BUYError.h
. BUYShopifyError_NoCollectionIdSpecified
-
No collection ID was provided when loading a collection
Declared In
BUYError.h
. BUYShopifyError_NoGiftCardSpecified
-
No gift card code was provided when applying a gift card to a checkout
Declared In
BUYError.h
. BUYShopifyError_NoCreditCardSpecified
-
No credit card was provided when calling
storeCreditCard:completion:
Declared In
BUYError.h
. BUYShopifyError_NoApplePayTokenSpecified
-
No Apple Pay token was provided when attempting to complete a checkout using Apple Pay
Declared In
BUYError.h
. BUYShopifyError_InvalidCheckoutObject
-
The checkout is invalid and does not have a checkout token. This generally means the BUYCheckout object has not been synced with Shopify via
createCheckout:completion:
before making subsequent calls to update or complete the checkoutDeclared In
BUYError.h
.
Declared In
BUYError.h


This is the base class for all builders.
These methods are predefined and will be called from the application:
Return a relative URI between two source filenames.
May raise environment.NoUri if there’s no way to return a sensible URI.
Main build method.
First updates the environment, and then calls write().
These methods can be overridden in concrete builder classes:
Load necessary templates and perform initialization. The default implementation does nothing.
Return an iterable of output files that are outdated, or a string describing what an update build will build.
If the builder does not output individual files corresponding to source files, return a string here. If it does, return an iterable of those files that need to be written.
Return the target URI for a document name.
typ can be used to qualify the link characteristic for individual builders.
A place where you can add logic before write_doc() is run


| TEP: | 137 |
|---|---|
| Group: | Core Working Group |
| Type: | Documentary |
| Status: | Draft |
| TinyOS-Version: | 2.x |
| Author: | David Moss, Mark Hays, and Mark Siner |
| Draft-Created: | 3-Sept-2009 |
| Draft-Version: | 1.1 |
| Draft-Modified: | 2009-10-01 |
| Draft-Discuss: | TinyOS Developer List <tinyos-devel at mail.millennium.berkeley.edu> |
Note
This memo documents a part of TinyOS for the TinyOS Community, and requests discussion and suggestions for improvements. Distribution of this memo is unlimited. This memo is in full compliance with TEP 1.
This memo proposes traffic control interfaces to be provided by an optional traffic control layer integrated at the highest levels of communication stacks. These traffic control mechanisms are targeted to help improve acknowledgment success rate, energy efficiency, fairness, and routing reliability on any wireless platform. The available reference implementation is a platform independent radio stack layer designed to consume a very small memory footprint.
As the traffic rate of a wireless sensor network increases, the probability of collision, dropped packets, and missed acknowledgments also increases, even with sophisticated CSMA/CA implementations.
It is important, especially in the case mesh networks, for packets to be delivered reliably and acknowledgments to be returned successfully on a hop-by- hop basis. One method to improve reliability is to reduce the rate of transmissions from each node within the network.
Traffic Control has been in use for years in many different wired and wireless applications[1]_,[2]_,[3]_. TinyOS already has traffic control mechanisms integrated directly into some networking libraries, such as CTP[4]_ and Dissemination[5]_. The use of Trickle[6]_ algorithms, also used within CTP and Dissemination, further reduces the rate of traffic throughout a network to improve delivery performance and prevent livelock. There has yet to be a centralized method of traffic control that throttles traffic generated from any component of a user's application.
The traffic control interfaces proposed in this TEP are very basic, and are intended to support many different traffic control implementations. Two interfaces assist the application layer in controlling behavior: TrafficControl and TrafficPriority.
The reference implementation presented here is integrated as a optional and generic radio stack layer (providing a Send and using a SubSend interface) and uses acknowledgments to dynamically adjust the transmit throttle. Other traffic control implementations could employ more sophisticated techniques to control throughput, but likely at the cost of a larger memory footprint.
The ultimate goal is to allow developers to use mesh networking protocols and/or their own protocols without having to worry about implementing any kind of traffic control timer mechanism for each separate component.
Ideally, a traffic control layer SHOULD attempt to balance the rate of transmissions from a single node with the channel throughput capacity. This implies an adaptive control mechanism. If the channel is busy, nodes should add delay between packets to let other nodes transmit. Similarly, if the channel is not busy, a node should be allowed access to the channel more often to prevent inefficient channel downtime. Traffic control SHOULD NOT listen to the channel for long periods of time to determine the appropriate access rates, because that defeats the purpose of low power communications layers used elsewhere.
The traffic control implementation SHOULD have the option to be activated or deactivated on a system-wide level as well as a packet level. This allows for individual high or low priority packets. Traffic control SHOULD be deactivated by default, until the application or networking layers explicitly enable it.
Finally, the traffic control mechanism SHOULD be small in code size to fit on the limited program memory available on most wireless platforms. There SHOULD NOT be additions or modifications to a packet's metadata structure that enables or disables traffic control on a per-packet basis; instead, per-packet priorities SHOULD be performed with a request/call back procedure. This keeps RAM requirements low and can be optimized out at compile time if those functions are not used.
We also recommend any traffic control layer be implemented as an optional compile time add-on to a core radio stack or within the ActiveMessageC platform communication stack definition. This allows applications that do not require traffic control to remove its memory footprint from the system.
The signature of TrafficControlC is RECOMMENDED as follows:
configuration TrafficControlC { provides { interface Send; interface TrafficControl; interface TrafficPriority[am_id_t amId]; } uses { interface Send as SubSend; } }
The Send interface provided on top and SubSend interface used underneath allow the TrafficControlC component to be integrated as a generic layer within any radio stack.
The TrafficControl interface allows the application layer to enable or disable traffic control from a system-wide level. It also allows an application to set and get the current delay between packets. For most systems, we expect that the setDelay() and getDelay() commands may not be used often and will most likely get optimized out at compile time; however, some systems may care to explicitly increase or decrease the delay between packets or collect statistics on how the traffic control layer is performing.
The TEP proposes the following TrafficControl interface:
interface TrafficControl { command void enable(bool active); command void setDelay(uint16_t delay); command uint16_t getDelay(); }
The TrafficPriority interface is parameterized by active message ID. It is a simple request / call back interface that allows components in the application layer to configure individual packets for priorities on a scale from 0 (lowest priority, default) to 5 (highest priority, get the packet out immediately). There are several advantages to this call back method. Metadata does not need to be added to the end of every message_t. Additionally, a component that captures a requestPriority(...) event is not required to adjust the priority as it would if the event returned a value.
When a packet enters the traffic control layer, and traffic control is enabled, the TrafficPriority interface MUST signal out the event requestPriority(...). This event, with all the extra information it provides, allows the application layer to decide whether the packet is a high priority packet or not. Calling the setPriority(uint8_t priority) command within the requestPriority(...) event MAY adjust the traffic control mechanisms applied to the current packet. To aid in the definition of priority, two definitions are available in TrafficControl.h:
enum { TRAFFICPRIORITY_LOWEST = 0, TRAFFICPRIORITY_HIGHEST = 5, };
It is up to the traffic control implementation to define the meaning of each priority level. In the reference implementation, a priority of 0 is the default low priority level that employs the full traffic control delays. Anything above 0 in the reference implementation is considered to be at the highest priority.
If no areas of the application layer care to change the packet's priority, a default event handler will capture the requestPriority(...) event and do nothing. This would result in all packets being sent at a low priority with full traffic control mechanisms enforced.
The TEP proposes the following TrafficPriority interface, to be provided as an interface parameterized by AM type:
interface TrafficPriority { event void requestPriority(am_addr_t destination, message_t \*msg); command void setPriority(uint8_t priority); }
An implementation of the proposed traffic control layer can be found in the CCxx00 radio stack in tinyos-2.x-contrib/blaze/tos/chips/ccxx00_addons/trafficcontrol, with interfaces located in tinyos-2.x-contrib/blaze/tos/chips/ccxx00_single/interfaces and a dummy implementation located in tinyos-2.x-contrib/blaze/tos/chips/ccxx00_single/traffic.
In this implementation, the default core radio stack (ccxx00_single) includes an empty stub for traffic control. Users that wish to include the traffic control implementation in their systems simply override the default stub component with the ccxx00_addons/trafficcontrol directory.
The reference implementation works as follows. All nodes start with a default of 4 seconds between each packet. Changes are made to the time between outbound packets only when a unicast packet is sent with the request for acknowledgment flag set. The reception of an acknowledgment is used as a basic indicator of channel activity. For each acknowledgment received, the amount of time between packets is decreased so the next packet will get sent faster. For each dropped acknowledgment, the amount of time between packets increases, causing the next packet to be sent later.
When the transmission rate reaches a boundary (1 second per packet per node fastest, 10 seconds per packet per node slowest), it is reset to the default rate of 4 seconds per packet per node. This prevents nodes from unfairly capturing the channel.
Testing this traffic control layer in a congested test bed setting of 16 nodes with multiple hidden terminals resulted in the acknowledgment success rate moving from 27-50% without traffic control to 90-100% with traffic control. The memory footprint increased by 260 bytes ROM / 16 bytes RAM with the inclusion of the traffic control layer.
| [1] | Bret Hull, Kyle Jamieson, Hari Balakrishnan. "Mitigating Congestion in Wireless Sensor Networks." In the Proceedings of the ACM Sensys Conference 2004 |
| [2] | Wan, C.-Y., Eisenman, S., and Campbell, A. "CODA: Congestion Detection and Avoidance in Sensor Networks." In the Proceedings of the ACM Sensys Conference 2003 |
| [3] | Woo, A., and Culler, D. "A Transmission Control Scheme for Media Access in Sensor Networks." In ACM MOBICOM 2001 |
| [4] | Rodrigo Fonseca, Omprakash Gnawali, Kyle Jamieson, Sukun Kim, Philip Levis, and Alec Woo.. "TEP123: Collection Tree Protocol" |
| [5] | Philip Levis and Gilman Tolle. "TEP118: Dissemination of Small Values." |
| [6] | Philip Levis, Neil Patel, David Culler, and Scott Shenker. "Trickle: A Self-Regulating Algorithm for Code Maintenance and Propagation in Wireless Sensor Networks." In Proceedings of the First USENIX/ACM Symposium on Networked Systems Design and Implementation (NSDI 2004). |


Employer View forms part of Ofsted’s goal to increase the involvement of employers in the training of their current and prospective employees. The tool allows employers to lodge their views on specific providers, inspected by Ofsted, which train their employees, including apprentices and those that are working for them through work experience or work placements.
Whilst the information collected will be a vital asset to Ofsted inspectors, employers will be able to use the tool to find out the views of other employers on providers to help them make informed choices about which providers will best suit their needs. Providers will also benefit from being given a clear view of what their employers think of the services they provide.
National Director for Further Education and Skills, Lorna Fitzjohn, commenting on the launch, said:
Employers in the past have often not been sufficiently included in the education and training of learners, including apprentices, and we recognised that this needed to change.
Engagement with employers was highlighted as a major theme for improvement in Ofsted’s Annual Report and we hope Employer View will facilitate a dialogue between employers and providers that will improve the provision of further education and training. The tool will inform inspectors’ understanding of the effectiveness of provision, increase employers’ engagement in education and training, and help them and employees to choose the provider that is right for them.
Employer View underpins Ofsted’s aim to raise standards in education and training. I therefore urge all employers using a provider inspected by Ofsted to visit the Employer View page on our website and contribute to improving the standard of training across the further education sector.
Employer View uses the same model as Learner View, launched in September 2012, which allows learners to register their views on the provision of their further education and skills provider. Employer View will compliment this existing tool, enabling employers to register their views independently of the provider they use. It will enable anyone who accesses the site to see what employers think of any provider in England inspected by Ofsted.
When they visit the site, employers will be prompted to fill in a concise questionnaire which asks them to rate providers on the quality of their communication, monitoring of employee progress, training provision, and feedback and support. Finally, an employer can say whether they would recommend the provider to other employers.Each response registered on Employer View will be completely confidential. Ofsted will not reveal any details of those who register their views to providers. Safeguards are also in place to guard against abuse of the site.
To register your views and to see information on providers, please visit:
www.employerview.ofsted.gov.uk
.


B-red took to his social media page to announce the birth of his son, Jordan Adetayo Osioke Adeleke with his “wifeyy” Faith Johnson.

B-Red and his partner tried all they could to keep the pregnancy a secret. He even at some point deactivated his social media page after which there were speculations the two had tied the knot.
Faith was thrown a surprise baby shower sometime in August.
Congratulations to the couple!
Follow us on Facebook – @Lailasnews; Twitter – @LailaIjeoma for updates


This scenario shows how to use the ListView from scenario 1 in a SemanticZoom control. To zoom, use the two finger pinch gesture, ctrl + "+/-", or ctrl + mouse wheel. SemanticZoom provides a zoom out button when the zoomed-in view is shown.


Hi,
do you have any idea how to /with no or minimum hardware adds - only by software in arduino scetch/ detect if clamp (with built-in sampling resistor) si disconnected ?
thanks
Stanislav
Archived Forum |
|
How to detect broken/disconnected clamp ?Submitted by svalent on Thu, 30/04/2015 - 21:58Hi, do you have any idea how to /with no or minimum hardware adds - only by software in arduino scetch/ detect if clamp (with built-in sampling resistor) si disconnected ?
thanks
Stanislav
» |
Re: How to detect broken/disconnected clamp ?
I think you cannot do that reliably. If the analogue input has absolutely no connection to anywhere, it will float to an unknown voltage. It will probably be far enough away from 512 (which is what you expect with our standard bias circuit) for you to be able to be able to say it is disconnected, but there is no guarantee that will be the case.
However, if you put a high value resistor (e.g. 4.7 MΩ) between the analogue input and GND, then I would expect the input to go sufficiently close to 0, and of course there would be no current signal superimposed, for you to say with some confidence that the CT had become disconnected. That resistor will slightly reduce the maximum current that you can measure because the quiescent point is no longer mid-way at 512 counts.
Re: How to detect broken/disconnected clamp ?
many thanks for your opinion, of course - "pull down" is some resolution, but....
what about periodicaly switch pin to output to short time and charge capacitor http://www.homautomation.org/wp-content/uploads/2013/09/step41.jpg and next measure rate of speed drop voltage to 0 (or measure float values if the input is disconnected) .... (my be most different if clamp is connected or not)
PS: only theory at this time....
Re: How to detect broken/disconnected clamp ?
I do not understand "speed drop voltage to 0". What is to say the voltage will drop to 0?
Re: How to detect broken/disconnected clamp ?
use Capacitor is wrong way....
Now I testing easier detecting by average input voltage on pin (input pin is pull down via 10Mohm) :
boolean broken_CT(unsigned int inPinI)
{
long in2=0;
for (unsigned int n = 0; n < 1480; n++)
{
in2 += analogRead(inPinI);
}
in2=in2/1480;
if (in2<100)
{
return false; //is broken/unconected
}
else
{
return true; //is OK
}
}
Re: How to detect broken/disconnected clamp ?
I think you might not need to calculate the average!
If the CT is broken, there will be no alternating voltage (or only a very small alternating voltage) superimposed on the dc level. So if, over a period of 1 cycle of mains, you read one sample > 100 (say), you know the CT is good. If you read < 100 for a whole cycle, you are certain that the CT is bad. If you read < 100 for less than one half-cycle, you might have a large current flowing!
Re: How to detect broken/disconnected clamp ?
If a CT were to "drop off" the cable around which it is supposed to be clipped, there would be virtually no AC signal from it. This would be indistinguishable from a situation where the CT is properly connected, but no primary current is flowing.
This is a genuine concern for the PV Router project. If the output stage happens to be 'on' when the CT is disconnected, it is likely to remain on indefinitely because the control loop has been broken. I've demonstrated this situation at https://www.youtube.com/watch?v=oF8HACCsK4M In the case of an immersion heater, there will normally be two mechanisms to prevent overheating. The situation is no worse than if the heater had been turned on manually and left unattended.
I can't see any way of auto-detecting whether a CT is correctly in place or not.
Re: How to detect broken/disconnected clamp ?
That's fair comment, but the original specification was "detect if clamp (with built-in sampling resistor) si disconnected".
Your PV Router might detect if the CT had physically fallen off the cable whilst still remaining connected, if you knew how long the tank took to cool and presumed that there was no other source of heating: if the CT didn't show current after this time had expired and the triac was on, you could infer that there was a problem, though you could not tell whether the heater was tripped out or isolated, the triac blown, or indeed the CT had fallen off. Personally, I'd have thought any of the others was more likely than the CT falling off.
For what it's worth, we always used spring-loaded terminals and hook crimps for all feedback connections in closed-loop control systems, precisely for the reason that if the feedback disappears, maloperation (at best) and danger to personnel is almost guaranteed.


WsdlMapper
WsdlMapper is a ruby gem to communicate with SOAP services. It provides:
- A generator to create classes from
XSD
schema files. - (De)Serializers to parse and write
XML
files to/from instances of these classes. - A generator to create SOAP clients from
WSDL
files.
Currently it supports only WSDL 1.1, but with Document or RPC message style and literal or encoded style.
Installation
Add this line to your application's Gemfile:
gem 'wsdl-mapper'
And then execute:
$ bundle
Or install it yourself as:
$ gem install wsdl-mapper
Usage
XSD
$ wsdl-mapper generate all some-schema.xsd --out some-schema --module SomeSchema
Will generate models and (de)serializers for the given XSD
schema. The generated classes are all nested within the
specified module SomeSchema
and are put under ${pwd}/some-schema
(pwd
== current working directory).
For all available options, see
$ wsdl-mapper help generate
SOAP / WSDL
$ wsdl-mapper generate svc some-svc.wsdl --out some-schema --module SomeSchema
Will generate models, (de)serializers and a client for the given WSDL
service.
TODOs
- loosen the dependency on concurrent-ruby, make the async way optional.
- refactor code to mitigate the naming mess. (there are so many different domain objects called name or type, which causes a mess in naming variables and methods. probably it makes sense to define acronyms and use them consistently throughout the code.)
- add more documentation, especially:
- how-tos / tutorials / walkthroughs
- inline documentation
- examples
- add logging to parsing + generating
Contributing
- Fork it ( https://github.com/kayoom/wsdl-mapper/fork )
- Create your feature branch (
git checkout -b my-new-feature
) - Commit your changes (
git commit -am 'Add some feature'
) - Push to the branch (
git push origin my-new-feature
) - Create a new Pull Request


00:00
Organize Cards
Instructions
Separate the cards between even, odd and face cards.
Aces are odd; Jokers must be discarded.
Wrong movements will result in penalties.
Controls
Move to odd cards deck
Move to face cards deck
Move to even cards deck
Discard
Difficulty
Easy:
no Jokers; 5 seconds penalty.
Normal:
with Jokers; 5 seconds penalty.
Hard:
with Jokers; no wrong movements.
Easy
Normal
Hard
Your score is
00:00
You
lost
.
Play Again
Back to Home Screen


Ruby Data Structures Array Syntax & Common Methods Hash Syntax Iterating over Nested Data Structures Example 1 Example 2 Example 3 - Comparing All Elements in 2 Arrays
JavaScript Example of a JavaScript Function Literal Notation vs. Constructor Terah & her Children: Create, add properties to, delete properties from, and access values from JS Object literals


This year I started to make an earnest attempt at making Terminal, a text based hacking game. While recently digging around on an old computer I found some sketches I did back in 2007. I think the initial inspiration came from the early joy of playing MUD games on BBS’s as a kid, combined with playing Introversions Uplink. I remember thinking wouldn’t it be cool to make an MMO hacking game (I don’t think that anymore).
The Game
Terminal is going to be predominantly a text based game, but I want to use 3D scenes to help generate an atmosphere and immersion. The game is going to revolve around a hacker doing what it is that hackers do. This is quite a niche game, but I like that. I don’t have time or resources to take on a huge game.
Why Not Unity
To be clear I like Unity as a tool, I’ve used it many times. I have no issue with it (except that maybe you become vendor locked). That said I don’t enjoy using it. It doesn’t scratch my itch at all - Game development is complex and I love that, taking that away makes me less happy. Also from experience, I think it makes people less creative. I’ve seen projects suffer from feature creep because Unity provides everything along with the illusion that it is magically easy. Features end up being used simply because they are there, which makes for a series of half baked ideas rather than a few ideas amazingly executed. So by starting with nothing (more or less) every feature/technology needs to be justified. Its far easier to be creative and find what matters with hard constraints.
Caffeine Engine
I’m not starting from scratch however, SDL provides a solid cross platform window, GL context and input across many platforms (including the web). This pretty much covers the hardest part of cross platform development. I’ve been working on the underlying renderer for years on and off, its just one of those bits of code most game developers have lying around. I’m also using Lua to write the game code. Terminal is mostly text based so for the most part I’m just exposing the ability to render text to the screen. Why is it called Caffeine, I honestly can’t remember - I’ve been using it as a namespace in code since university as a way to identify whats game code and whats not, and calling it an engine is a little bit of a stretch :)
Whats Next
I spend a lot of time commuting so I’ve been trying to write game code in that time, and do the technology side of things on the weekends. I’m currently working on how the laptop works right now, animation support is next on my technology list of things to do. Further blog posts I’ll talk more directly about how the game is progressing.


AnimatedImage QML Type
Plays animations stored as a series of images More...
| Import Statement: | import QtQuick 2.7 |
| Inherits: |
Properties
- currentFrame : int
- frameCount : int
- paused : bool
- playing : bool
- source : url
Detailed Description
The AnimatedImage type extends the features of the Image type, providing a way to play animations stored as images containing a series of frames, such as those stored in GIF files.
Information about the current frame and total length of the animation can be obtained using the currentFrame and frameCount properties. You can start, pause and stop the animation by changing the values of the playing and paused properties.
The full list of supported formats can be determined with QMovie::supportedFormats().
Example Usage
The following QML shows how to display an animated image and obtain information about its state, such as the current frame and total number of frames. The result is an animated image with a simple progress indicator underneath it.
Note: When animated images are cached, every frame of the animation will be cached.

If the image data comes from a sequential device (e.g. a socket), AnimatedImage can only loop if cache is set to true.
import QtQuick 2.0 Rectangle { width: animation.width; height: animation.height + 8 AnimatedImage { id: animation; source: "animation.gif" } Rectangle { property int frames: animation.frameCount width: 4; height: 8 x: (animation.width - width) * animation.currentFrame / frames y: animation.height color: "red" } }
See also BorderImage and Image.
Property Documentation
currentFrame is the frame that is currently visible. By monitoring this property for changes, you can animate other items at the same time as the image.
frameCount is the number of frames in the animation. For some animation formats, frameCount is unknown and has a value of zero.
currentFrame is the frame that is currently visible. By monitoring this property for changes, you can animate other items at the same time as the image.
frameCount is the number of frames in the animation. For some animation formats, frameCount is unknown and has a value of zero.
This property holds whether the animated image is paused.
By default, this property is false. Set it to true when you want to pause the animation.
This property holds whether the animated image is playing.
By default, this property is true, meaning that the animation will start playing immediately.
Note: this property is affected by changes to the actual playing state of AnimatedImage. If non-animated images are used, playing will need to be manually set to true in order to animate following images.
AnimatedImage { onStatusChanged: playing = (status == AnimatedImage.Ready) }
source : url |
This property holds the URL that refers to the source image.
AnimatedImage can handle any image format supported by Qt, loaded from any URL scheme supported by Qt.
See also QQuickImageProvider.


"Photography is a way of feeling, of touching souls, of loving. What you have caught on film is captured forever ... It remembers little things, long after you have forgotten everything."
- Aaron Siskind -
Hello, and welcome! I’m Darius Florentin, Romania based lifestyle photographer and if I would have to sum up what I do in one sentence, this would be it: I inspire people through beauty & authentic stories.
Explore moreMy website showcases my recent photo projects with a focus on architecture and landscape photography. In addition to my own photography, I belong to a group of volunteers that organizes exhibitions and events annually in Timisoara, Romania.
Explore more


Graham: Horowitz FISA Report Will Be Released on December 9 | Breitbart
by Ian HanchettOn Wednesday’s broadcast of the Fox News Channel’s “Hannity,” Senate Judiciary Committee Chairman Lindsey Graham (R-SC) announced that DOJ Inspector General Michael Horowitz’s report on alleged FISA abuses in the 2016 election will be released on December 9.
Graham said, “It’ll be December the 9. You’ll get the report.”
He added, “That’s locked.”
Follow Ian Hanchett on Twitter @IanHanchett


About me:
I am working torwards a Bachelors in Computer Engineering at Iowa State University.
I enjoy the active learning approach, with my coursework involving projects that span the entire field of ECE. This can be low-level hardware topics such as VLSI and FPGA logic design, or higher-level software work in Embedded Software, Web Development, and Game Design. Outside of school, I like to travel, listen to podcasts and watch documentaries.


Black and White (2000) Reviewed by Eugene Novikov http://www.ultimate-movie.com/ Member: Online Film Critics Society
Starring Brooke Shields, Robert Downey Jr., Ben Stiller, Allan Houston, Elijah Wood, Bijou Phillips, Power. Directed by James Toback. Rated R.
I think that in making Black and White director James Toback took all of the ideas he had and threw them up into the air with his eyes closed and fingers crossed, hoping to God that they somehow land in the right place. Indeed, the pieces fit together like a jigsaw puzzle. The film is about a lot of things and all of them are prevalent in one way or another. With such a maddeningly large array of themes and subjects, it's mighty impressive that Black and White works as well as it does. And yet instead of visualizing a cinematic genius intricately crafting a complex work of art, the image I got from watching the movie was that of Toback, sleep-deprived and haggard, desperately attempting the Herculean task of putting together a magnum opus in 15 minutes and miraculously succeeding.
There are a lot of overlapping storylines here and in its plotting, the film resembles a (deliberately?) disorganized version of Magnolia or Cradle Will Rock. It is being sold as a look at white people obsessed with black culture, but that is only one of the stories. It involves a documentary filmmaker (Brooke Shields) shooting a film about the subject, the group of kids (including a surprisingly convincing Elijah Wood and Bijou Phillips) who are her focus and her husband (Robert Downey Jr.), who is obviously gay even though his wife doesn't seem to realize it.
There is also the story of a star college point guard (real basketball player Allan Houston), who is approached by a policeman (Ben Stiller) posing as a gambler. His proposition: throw the upcoming game and get $50,000 cash. The purpose of this maneuver is to set the nice but gullable kid up and then blackmail him into ratting out one of his buddies who is the head of a gang. The policeman also approaches the district attorney whose son is involved with the gang leader with a different sort of proposal. And then there is Mike Tyson who, playing himself, is funny just by showing up.
Aware that a conventional filmmaking method for such unconventional subject matter wouldn't do, Toback decided to let the actors improvise a lot of their scenes. He gave them a general direction in which to head, turned on the camera and let them do their thing. The result is exactly what he was aiming for: the scenes have a flowing, unforced feel to them. It doesn't feel like we're watching a documentary but rather a movie that genuinely reflects real life. And anyway, no movie in which Mike Tyson gets to say "This is what I'm deciphering from your vernacular" can be all bad.
The surprise here is Allan Houston who has a real affablility and screen presence. Unlike fellow basketball-star-cum-thespian Shaquille O'Neal, Houston may have a real career in store for himself if he chooses to use his talent wisely and not accept projects trying to exploit his basketball stardom. In Black and White, Houston's troubled point guard is the only character I liked as a person.
Not everything works. Not every theme comes through loud and clear. The Ben Stiller character gets a lot of screen time and the movie makes a big deal out of him but he's never really explored and we struggle to understand him. In the end, Black and White works as a gritty slice-of- life movie, not trying to say anything concrete but simply to show. A lot is thrown on the screen and most of it hits home.
Grade: B+
©1999 Eugene Novikov
Sent via Deja.com http://www.deja.com/ Before you buy.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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Related links: index of all rec.arts.movies.reviews reviews


EXCLUSIVE: Quando Rondo on CPS Taking Him from His Mother at 2 After He Got Burned
by Jessica ToddQuando Rondo opened up to VladTV about growing up in Savannah, GA and being raised by his great aunt after CPS took him out of his home at 2-years-old. He explained that his leg was burned by an iron during an argument between his parents, and when Quando's mother took him to the hospital two days later, he was taken away by CPS. Quando Rondo explained that he wasn't close to his dad's side of the family, so his living situation was somewhat strange when he was old enough to assess the situation.


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TIN CUP A film review by Mark R. Leeper Copyright 1996 Mark R. Leeper
Capsule: Good dialogue can compensate for a multitude of other sins. Here the sins include a fairly predictable and banal plot and some rather obvious manipulation of the audience. Much of this has been done before, particularly in BULL DURHAM or ROCKY. The real surprise is a restrained performance that becomes one of the best ever for Cheech Marin. The words and the photography are what makes the film. Rating: +1 (-4 to +4)
Voltaire said "Le mieux est l'ennemi du bien." Literally it means "the best is the enemy of the good." That is just what has kept Roy "Tin Cup" McAvoy (Kevin Costner) from being a top ranking golfer. He has to try for the best shot, the dangerous one rather than looking not quite so good. He usually misses it, but at least he tried for the shot that everybody would remember. So instead of a well-known golf pro, he runs a second-rate driving range in Salome, Texas. It is not a great living but in a tiny West Texas town like Salome, where the armadillos outnumber the golfers by hundreds to one, it is amazing he can even survive. Even stranger is that the small town supports a psychiatrist, Dr. Molly Griswold (Rene Russo) and that each has never heard of the other until Griswold shows up a the driving range wanting lessons. McAvoy is attracted to Griswold but she is already attached to someone. The plot thickens when an old friend, now a rival and a top golf pro, David Simms (Don Johnson) turns up and wants McAvoy to caddie for him in a golf tourney. Needing the money McAvoy takes the job, but get himself fired in a grandstand play when Simms goes for a conservative shot at the tourney. McAvoy decides to go for another big one and make a play for Griswold, only to find out her boyfriend is Simms. From there you could plot out the rest of the film and with only a small surprise here and there you would probably be right. (Think of it as ROCKY with a different sport and cast with prettier- looking actors.)
What makes TIN CUP watchable are the characters and the dialog. These are bright people and their small talk as the plot plays itself out is more of interest than the plot itself. McAvoy and Griswold are likable people as are the circle of people who orbit around them. First and foremost of the friends is Romeo Posar, as the most interesting an amiable character that Cheech Marin has ever played. The role was likely written for someone of a milder character to have a little fun hamming it up. Instead Marin plays the role as restrained as anybody he has ever played, so that is not the comic bits that surprise us but the moments of warmth and sincerity. Another close friend is a stripper and ex-girlfriend to whom McAvoy owes money. Making her a stripper seems like it was a calculated move. At 42 Ms. Russo probably prefers to show considerably less flesh than say, Susan Sarandon did in a previous film by director Ron Shelton, BULL DURHAM. The story asks if this old, but still talented, failure McAvoy can make something of himself on the golfing advice of his caddie and the pop psychology of his girlfriend without alienating either too much.
Ron Shelton's films seem to be falling into a pattern of sports, a bit of sex, a large dollop of comedy, some well-drawn characters, and good dialog. His films are thin on plot and give the audience little to chew on after the film is over, but they are audience pleasers and at least the audience when I saw the film were applauding at times. Considering that golf is a gave that even its most ardent fans usually give only polite hand-clapping, getting a film audience so involved in a golf film is no small feat. With the exceptions of Costner's slight grunge edge, the characters are pretty and pretty bland. Johnson actually is the most believable as the blandest of all. I think that the film also sported some golf champions who would have been familiar to some of the audience, though I would not have recognized them if they were two feet away. Some familiar brand names also show up on things like golf caps and even a blimp and they too are playing themselves.
TIN CUP will not leave you with much to think about other than a person's reach should exceed his grasp. This is a pleasant enough throwaway film you will not remember in the fall. Rate it a +1 on the -4 to +4 scale.
Mark R. Leeper<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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Usage of initializers
Initializations define the way to set the initial random weights of Keras layers.
The keyword arguments used for passing initializers to layers will depend on the layer. Usually it is simply kernel_initializer
and bias_initializer
:
model.add(Dense(64,
kernel_initializer='random_uniform',
bias_initializer='zeros'))
Available initializers
The following built-in initializers are available as part of the keras.initializers
module:
Initializer
keras.initializers.Initializer()
Initializer base class: all initializers inherit from this class.
Zeros
keras.initializers.Zeros()
Initializer that generates tensors initialized to 0.
Ones
keras.initializers.Ones()
Initializer that generates tensors initialized to 1.
Constant
keras.initializers.Constant(value=0)
Initializer that generates tensors initialized to a constant value.
Arguments
- value: float; the value of the generator tensors.
RandomNormal
keras.initializers.RandomNormal(mean=0.0, stddev=0.05, seed=None)
Initializer that generates tensors with a normal distribution.
Arguments
- mean: a python scalar or a scalar tensor. Mean of the random values to generate.
- stddev: a python scalar or a scalar tensor. Standard deviation of the random values to generate.
- seed: A Python integer. Used to seed the random generator.
RandomUniform
keras.initializers.RandomUniform(minval=-0.05, maxval=0.05, seed=None)
Initializer that generates tensors with a uniform distribution.
Arguments
- minval: A python scalar or a scalar tensor. Lower bound of the range of random values to generate.
- maxval: A python scalar or a scalar tensor. Upper bound of the range of random values to generate. Defaults to 1 for float types.
- seed: A Python integer. Used to seed the random generator.
TruncatedNormal
keras.initializers.TruncatedNormal(mean=0.0, stddev=0.05, seed=None)
Initializer that generates a truncated normal distribution.
These values are similar to values from a RandomNormal
except that values more than two standard deviations from the mean
are discarded and redrawn. This is the recommended initializer for
neural network weights and filters.
Arguments
- mean: a python scalar or a scalar tensor. Mean of the random values to generate.
- stddev: a python scalar or a scalar tensor. Standard deviation of the random values to generate.
- seed: A Python integer. Used to seed the random generator.
VarianceScaling
keras.initializers.VarianceScaling(scale=1.0, mode='fan_in', distribution='normal', seed=None)
Initializer capable of adapting its scale to the shape of weights.
With distribution="normal"
, samples are drawn from a truncated normal
distribution centered on zero, with stddev = sqrt(scale / n)
where n is:
- number of input units in the weight tensor, if mode = "fan_in"
- number of output units, if mode = "fan_out"
- average of the numbers of input and output units, if mode = "fan_avg"
With distribution="uniform"
,
samples are drawn from a uniform distribution
within [-limit, limit], with limit = sqrt(3 * scale / n)
.
Arguments
- scale: Scaling factor (positive float).
- mode: One of "fan_in", "fan_out", "fan_avg".
- distribution: Random distribution to use. One of "normal", "uniform".
- seed: A Python integer. Used to seed the random generator.
Raises
- ValueError: In case of an invalid value for the "scale", mode" or "distribution" arguments.
Orthogonal
keras.initializers.Orthogonal(gain=1.0, seed=None)
Initializer that generates a random orthogonal matrix.
Arguments
- gain: Multiplicative factor to apply to the orthogonal matrix.
- seed: A Python integer. Used to seed the random generator.
References
Identity
keras.initializers.Identity(gain=1.0)
Initializer that generates the identity matrix.
Only use for 2D matrices. If the desired matrix is not square, it gets padded with zeros for the additional rows/columns.
Arguments
- gain: Multiplicative factor to apply to the identity matrix.
lecun_uniform
keras.initializers.lecun_uniform(seed=None)
LeCun uniform initializer.
It draws samples from a uniform distribution within [-limit, limit]
where limit
is sqrt(3 / fan_in)
where fan_in
is the number of input units in the weight tensor.
Arguments
- seed: A Python integer. Used to seed the random generator.
Returns
An initializer.
References
glorot_normal
keras.initializers.glorot_normal(seed=None)
Glorot normal initializer, also called Xavier normal initializer.
It draws samples from a truncated normal distribution centered on 0
with stddev = sqrt(2 / (fan_in + fan_out))
where fan_in
is the number of input units in the weight tensor
and fan_out
is the number of output units in the weight tensor.
Arguments
- seed: A Python integer. Used to seed the random generator.
Returns
An initializer.
References
glorot_uniform
keras.initializers.glorot_uniform(seed=None)
Glorot uniform initializer, also called Xavier uniform initializer.
It draws samples from a uniform distribution within [-limit, limit]
where limit
is sqrt(6 / (fan_in + fan_out))
where fan_in
is the number of input units in the weight tensor
and fan_out
is the number of output units in the weight tensor.
Arguments
- seed: A Python integer. Used to seed the random generator.
Returns
An initializer.
References
he_normal
keras.initializers.he_normal(seed=None)
He normal initializer.
It draws samples from a truncated normal distribution centered on 0
with stddev = sqrt(2 / fan_in)
where fan_in
is the number of input units in the weight tensor.
Arguments
- seed: A Python integer. Used to seed the random generator.
Returns
An initializer.
References
lecun_normal
keras.initializers.lecun_normal(seed=None)
LeCun normal initializer.
It draws samples from a truncated normal distribution centered on 0
with stddev = sqrt(1 / fan_in)
where fan_in
is the number of input units in the weight tensor.
Arguments
- seed: A Python integer. Used to seed the random generator.
Returns
An initializer.
References
he_uniform
keras.initializers.he_uniform(seed=None)
He uniform variance scaling initializer.
It draws samples from a uniform distribution within [-limit, limit]
where limit
is sqrt(6 / fan_in)
where fan_in
is the number of input units in the weight tensor.
Arguments
- seed: A Python integer. Used to seed the random generator.
Returns
An initializer.
References
An initializer may be passed as a string (must match one of the available initializers above), or as a callable:
from keras import initializers
model.add(Dense(64, kernel_initializer=initializers.random_normal(stddev=0.01)))
# also works; will use the default parameters.
model.add(Dense(64, kernel_initializer='random_normal'))
Using custom initializers
If passing a custom callable, then it must take the argument shape
(shape of the variable to initialize) and dtype
(dtype of generated values):
from keras import backend as K
def my_init(shape, dtype=None):
return K.random_normal(shape, dtype=dtype)
model.add(Dense(64, kernel_initializer=my_init))


--- layout: post status: publish published: true title: Some Thoughts on Oslo author: display_name: Tomas Restrepo login: tomasr email<EMAIL_ADDRESS>url: http://winterdom.com/ author_login: tomasr author_email<EMAIL_ADDRESS>author_url: http://winterdom.com/ wordpress_id: 783 wordpress_url: http://winterdom.com/2008/10/somethoughtsonoslo date: '2008-10-29 15:13:27 +0000' date_gmt: '2008-10-29 15:13:27 +0000' categories: - Oslo tags: [] comments: - id: 529 author: modeler author_email: '' author_url: '' date: '2008-10-29 19:58:09 +0000' date_gmt: '2008-10-29 19:58:09 +0000' content: | good look at the UI at http://modelsremixed.com - id: 530 author: modeler author_email: '' author_url: '' date: '2008-10-29 20:01:55 +0000' date_gmt: '2008-10-29 20:01:55 +0000' content: | seeing that html is allowed . .. good look at the UI at Models Remixed - id: 531 author: Todd author_email: '' author_url: http://toddkitta.myopenid.com/ date: '2008-10-31 22:25:40 +0000' date_gmt: '2008-10-31 22:25:40 +0000' content: | I actually did hear in a session that M would be VS integrated eventually. ---
I’ve started playing with the Oslo bits a little, and I thought I’d share a couple of thoughts on the platform and its tools:
M is an interesting beast, but to me the most interesting part of the platform to start with. In particular, I’m looking forward to playing with the MGrammar stuff.
I’m not thrilled about the close relationship between M (and its tooling) to SQL, though. In particular, I really think that Don Box and friends really need to find a better example to introduce Models and M; it was painfully obvious (at least to me) during the M language talk that many people just weren’t getting it.
IPad is an interesting tool. It has some useful features for developing M models in the M Mode and MGrammar modes; there’s some good stuff on this on the Intellipad Team Blog. Right now, I’d expect IPad to continue being an important tool in the M developer’s toolkit.
However, what I have not seen really is good justification about why we needed a new text editor. I get the emacs.net analogy, but I have yet to see how creating it was a better option than adding functionality to Visual Studio, where is where most developers would expect to find that functionality (yes, I’m very well aware of the irony regarding how bloated VS already is).
One thing that really caught my eye was the look and feel of the Oslo/CSD tools: It’s very minimalistic, and can be seen in the WF 4.0 Workflow designer, Quadrant and Intellipad. I actually find it rather appealing (though a bit weird at times), but I don’t expect this state to last; too clean for MS :-).


ORGANIZING COMMITTEE
General Chair |
|
|---|---|
| Prof.(Dr.) Shailesh Tiwari Director, ABES Engineering College, Ghaziabad, UP, India |
|
General Co-Chair |
|
| Dr. Amit Sinha Professor and Head (IT), ABES Engineering College, Ghaziabad, UP, India |
|
Conference Chair |
|
| Dr. Anish Gupta Professor(IT), ABES Engineering College, Ghaziabad, UP, India |
|
Advisory Board |
|
| Prof. A.K Misra MNNIT Allahabad, India |
Prof. Sanjiv K. Bhatia Professor, Department of Computer Science, Univ. of Missouri - St. Louis, USA |
| Prof. Phalguni Gupta IIT Kanpur, UP India |
Prof. Vineet Kansal Professor, IET Lucknow, UP, India |
| Dr. Rajiv Srivastava Head (CSE) IIT BHU Varansi |
Dr. Ashish lehar University of Allahabad, UP |
| Dr. Dhananjay Singh Department of Electronics Engineering at Hankuk (Korea) University of Foreign Studies (HUFS), Seoul, South Korea |
Prof. Jong-Myon Kim University of Ulsan, Ulsan, South |
| Prof. Haranath Kar MNNIT Allahabad, Prayagraj, UP, India |
Prof. K.V. Arya ABV-IIITM, Gwalior, MP, India |
| Prof. R.K. Singh MNNIT Allahabad, Prayagraj, UP, India |
Dr. Andrew NG SIT, Singapore |
| Prof. Vishal Bhatnagar NSUT (East Campus), Delhi, India |
Prof. D.P Vidyarthi India |
Program Chairs |
|
| Dr. Pao-Ann Hsiung National Chung Cheng University,Taiwan |
|
| Dr. Munesh C. Trivedi NIT Agartala, Tripura, India |
|
| Dr. Jitender Kumar Chhabra Department of Computer Engineering, NIT Kurukshetra, INDIA |
|
| Prof. Nagender Kumar Suryadevara University of Hyderabad, India |
|
TPC Chairs |
|
| Dr. K. K. Mishra MNNIT Allahabad, Prayagraj, UP, India |
|
| Dr. Wei-Chiang Hong Jiangsu Normal University, China |
|
| Dr. Maheshkumar H.Kolekar IIT, Patna, India |
|
Publication Chairs |
|
| Dr. Yashwant Singh Central University Jammu, INDIA |
Dr. Yugal Kumar Jaypee University of Information Technology, India |
| Dr Kamarul Hawari bin Ghazal Universiti Malaysia Pahang, Malaysia |
Dr. Samayveer Singh NIT Jalandhar, India |
| Dr. Anand Nayyar Duy Tan University, Da Nang, Viet Nam |
|
Publicity Chairs |
|
| Dr. Kunwar Pratap Singh NSIT, Delhi, India |
Dr. Zdzislaw Polkowski Jan Wyzykowski University, Polkowice, Poland |
| Dr. Naveen Chilamkurti La Trobe University, Melbourne, Australia |
Dr. G. Sahoo IIT (ISM) Dhanbad, India |
| Prof. Parmanand Sharda University, Greater Noida, UP, India |
Dr. Nitin Rakesh Sharda University, Greater Noida, UP, India |
| Dr. Ravi Prasad Valluru Narayana Engineering College Nellore, AP, India |
Dr. Sushant Upadyaya MNIT, Jaipur, India |
| Dr. Vishnu Sharma GCET, Greater Noida, UP, India |
Dr. Sanjay Sood, CDAC, Mohali, India |
| Dr Rumyantsev Konstantin Southern Federal University, Russia |
Dr. Ankur Goel Higher College of Technology, Musanna, Sultanate of Oman |
| Dr. T. VennkathRav Director, IIAR, Hyderabad, India |
|
Organizing Committee |
|
| Prof. S.K. Singh ABES Engineering College, Ghaziabad, UP, India |
Prof. Amit Sinha ABES Engineering College, Ghaziabad, UP, India |
| Prof. R.K. Shukla ABES Engineering College, Ghaziabad, India |
Prof. Hariom Upadhyay ABES Engineering College, Ghaziabad, India |
| Prof. Hemant Ahuja ABES Engineering College, Ghaziabad, India |
Prof. Gaurav Dubey ABES Engineering College, Ghaziabad, India |
| Prof. Santosh Kumar ABES Engineering College, Ghaziabad, India |
Prof. Devendra Arya ABES Engineering College, Ghaziabad, India |
| Prof. Pankaj Sharma ABES Engineering College, Ghaziabad, India |
Dr. Tarun Varshney ABES Engineering College, Ghaziabad, India |
| Dr. Amit Agarwal ABES Engineering College, Ghaziabad, India |
Dr. Vikas Yadav ABES Engineering College, Ghaziabad, India |
| Dr. Akash Punhani ABES Engineering College, Ghaziabad, India |
Prof. Deepak Kumar Singh ABES Engineering College, Ghaziabad, India |
| Prof. Anish Gupta ABES Engineering College, Ghaziabad, India |
Dr. Anil Dubey ABES Engineering College, Ghaziabad, India |