In a previous post, I talked about why “shelling out” to spawn a pipeline of external programs via an intermediate shell is a common cause of bugs, security holes, unnecessary overhead, and silent failures. But it’s so convenient! Why can’t running pipelines of external programs be convenient and safe? Well, there’s no real reason, actually. The shell itself manages to construct and execute pipelines quite well. In principle, there’s nothing stopping high-level languages from doing it at least as well as shells do – the common ones just don’t by default, instead requiring users to make the extra effort to use external programs safely and correctly. There are two major impediments:
pipe
, dup2
, fork
, close
, and exec
system calls;This post describes the system we designed and implemented for Julia, and how it avoids the major flaws of shelling out in other languages. First, I’ll present the Julia version of the previous post’s example – counting the number of lines in a given directory containing the string “foo”. The fact that Julia provides complete, specific diagnostic error messages when pipelines fail turns out to reveal a surprising and subtle bug, lurking in what appears to be a perfectly innocuous UNIX pipeline. After fixing this bug, we go into details of how Julia’s external command execution and pipeline construction system actually works, and why it provides greater flexibility and safety than the traditional approach of using an intermediate shell to do all the heavy lifting.
Here’s how you write the example of counting the number of lines in a directory containing the string “foo” in Julia
(you can follow along at home if you have Julia installed from source by changing directories into the Julia source directory and doing cp -a src "source code"; mkdir tmp
and then firing up the Julia repl):
julia> dir = "src";
julia> int(readchomp(`find $dir -type f -print0` |> `xargs -0 grep foo` |> `wc -l`))
5
This Julia command looks suspiciously similar to the naïve Ruby version we started with in the previous post:
`find #{dir} -type f -print0 | xargs -0 grep foo | wc -l`.to_i
However, it isn’t susceptible to the same problems:
julia> dir = "source code";
julia> int(readchomp(`find $dir -type f -print0` |> `xargs -0 grep foo` |> `wc -l`))
5
julia> dir = "nonexistent";
julia> int(readchomp(`find $dir -type f -print0` |> `xargs -0 grep foo` |> `wc -l`))
find: `nonexistent': No such file or directory
ERROR: failed processes:
Process(`find nonexistent -type f -print0`, ProcessExited(1)) [1]
Process(`xargs -0 grep foo`, ProcessExited(123)) [123]
in pipeline_error at process.jl:412
in readall at process.jl:365
in readchomp at io.jl:172
julia> dir = "foo'; echo MALICIOUS ATTACK; echo '";
julia> int(readchomp(`find $dir -type f -print0` |> `xargs -0 grep foo` |> `wc -l`))
find: `foo\'; echo MALICIOUS ATTACK; echo \'': No such file or directory
ERROR: failed processes:
Process(`find "foo'; echo MALICIOUS ATTACK; echo '" -type f -print0`, ProcessExited(1)) [1]
Process(`xargs -0 grep foo`, ProcessExited(123)) [123]
in pipeline_error at process.jl:412
in readall at process.jl:365
in readchomp at io.jl:172
The default, simplest-to-achieve behavior in Julia is:
In the above examples, we can see that even when dir
contains spaces or quotes, the expression still behaves exactly as intended – the value of dir
is interpolated as a single argument to the find
command.
When dir
is not the name of a directory that exists, find
fails – as it should – and this failure is detected and automatically converted into an informative exception, including the fully expanded command-lines that failed.
In the previous post, we observed that using the pipefail
option for Bash allows detection of pipeline failures, like this one, occurring before the last process in the pipeline.
However, it only allows us to detect that at least one thing in the pipeline failed.
We still have to guess at what parts of the pipeline actually failed.
In the Julia example, on the other hand, there is no guessing required:
when a non-existent directory is given, we can see that both find
and xargs
fail.
While it is unsurprising that find
fails in this case, it is unexpected that xargs
also fails.
Why does xargs
fail?
One possibility to check for is that the xargs
program fails with no input.
We can use Julia’s success
predicate to try it out:
julia> success(`cat /dev/null` |> `xargs true`)
true
Ok, so xargs
seems perfectly happy with no input.
Maybe grep doesn’t like not getting any input?
julia> success(`cat /dev/null` |> `grep foo`)
false
Aha! grep
returns a non-zero status when it doesn’t get any input.
Good to know.
It turns out that grep
indicates whether it matched anything or not with its return status.
Most programs use their return status to indicate success or failure, but some, like grep
, use it to indicate some other boolean condition – in this case “found something” versus “didn’t find anything”:
julia> success(`echo foo` |> `grep foo`)
true
julia> success(`echo bar` |> `grep foo`)
false
Now we know why grep
is “failing” – and xargs
too, since it returns a non-zero status if the program it runs returns non-zero.
This means that our Julia pipeline and the “responsible” Ruby version are both susceptible to bogus failures when we search an existing directory that happens not to contain the string “foo” anywhere:
julia> dir = "tmp";
julia> int(readchomp(`find $dir -type f -print0` |> `xargs -0 grep foo` |> `wc -l`))
ERROR: failed process: Process(`xargs -0 grep foo`, ProcessExited(123)) [123]
in error at error.jl:22
in pipeline_error at process.jl:394
in pipeline_error at process.jl:407
in readall at process.jl:365
in readchomp at io.jl:172
Since grep
indicates not finding anything using a non-zero return status, the readall
function concludes that its pipeline failed and raises an error to that effect.
In this case, this default behavior is undesirable:
we want the expression to just return 0
without raising an error.
The simple fix in Julia is this:
julia> dir = "tmp";
julia> int(readchomp(`find $dir -type f -print0` |> ignorestatus(`xargs -0 grep foo`) |> `wc -l`))
0
This works correctly in all cases. Next I’ll explain how all of this works, but for now it’s enough to note that the detailed error message provided when our pipeline failed exposed a rather subtle bug that would eventually cause subtle and hard-to-debug problems when used in production. Without such detailed error reporting, this bug would be pretty difficult to track down.
Julia borrows the backtick syntax for external commands form Perl and Ruby, both of which in turn got it from the shell. Unlike in these predecessors, however, in Julia backticks don’t immediately run commands, nor do they necessarily indicate that you want to capture the output of the command. Instead, backticks just construct an object representing a command:
julia> `echo Hello`
`echo Hello`
julia> typeof(ans)
Cmd
(In the Julia repl, ans
is automatically bound to the value of the last evaluated input.)
In order to actually run a command, you have to do something with a command object.
To run a command and capture its output into a string – what other languages do with backticks automatically – you can apply the readall
function:
julia> readall(`echo Hello`)
"Hello\n"
Since it’s very common to want to discard the trailing line break at the end of a command’s output, Julia provides the readchomp(x)
command which is equivalent to writing chomp(readall(x))
:
julia> readchomp(`echo Hello`)
"Hello"
To run a command without capturing its output, letting it just print to the same stdout
stream as the main process – i.e. what the system
function does when given a command as a string in other languages – use the run
function:
julia> run(`echo Hello`)
Hello
The "Hello\n"
after the readall
command is a returned value, whereas the Hello
after the run
command is printed output.
(If your terminal supports color, these are colored differently so that you can easily distinguish them visually.)
Nothing is returned by the run
command, but if something goes wrong, an exception is raised:
julia> run(`false`)
ERROR: failed process: Process(`false`, ProcessExited(1)) [1]
in error at error.jl:22
in pipeline_error at process.jl:394
in run at process.jl:384
julia> run(`notaprogram`)
execvp(): No such file or directory
ERROR: failed process: Process(`notaprogram`, ProcessExited(-1)) [-1]
in error at error.jl:22
in pipeline_error at process.jl:394
in run at process.jl:384
As with xargs
and grep
above, this may not always be desirable.
In such cases, you can use ignorestatus
to indicate that the command returning a non-zero value should not be considered an error:
julia> run(ignorestatus(`false`))
julia> run(ignorestatus(`notaprogram`))
execvp(): No such file or directory
ERROR: failed process: Process(`notaprogram`, ProcessExited(-1)) [-1]
in error at error.jl:22
in pipeline_error at process.jl:394
in run at process.jl:384
In the latter case, an error is still raised in the parent process since the problem is that the executable doesn’t even exist, rather than merely that it ran and returned a non-zero status.
Although Julia’s backtick syntax intentionally mimics the shell as closely as possible, there is an important distinction:
the command string is never passed to a shell to be interpreted and executed;
instead it is parsed in Julia code, using the same rules the shell uses to determine what the command and arguments are.
Command objects allow you to see what the program and arguments were determined to be by accessing the .exec
field:
julia> cmd = `perl -e 'print "Hello\n"'`
`perl -e 'print "Hello\n"'`
julia> cmd.exec
3-element Union(UTF8String,ASCIIString) Array:
"perl"
"-e"
"print \"Hello\\n\""
This field is a plain old array of strings that can be manipulated like any other Julia array.
The purpose of the backtick notation in Julia is to provide a familiar, shell-like syntax for making objects representing commands with arguments.
To that end, quotes and spaces work just as they do in the shell.
The real power of backtick syntax doesn’t emerge, however, until we begin constructing commands programmatically.
Just as in the shell (and in Julia strings), you can interpolate values into commands using the dollar sign ($
):
julia> dir = "src";
julia> `find $dir -type f`.exec
4-element Union(UTF8String,ASCIIString) Array:
"find"
"src"
"-type"
"f"
Unlike in the shell, however, Julia values interpolated into commands are interpolated as a single verbatim argument – no characters inside the value are interpreted as special after the value has been interpolated:
julia> dir = "two words";
julia> `find $dir -type f`.exec
4-element Union(UTF8String,ASCIIString) Array:
"find"
"two words"
"-type"
"f"
julia> dir = "foo'bar";
julia> `find $dir -type f`.exec
4-element Union(UTF8String,ASCIIString) Array:
"find"
"foo'bar"
"-type"
"f"
This works no matter what the contents of the interpolated value is, allowing simple interpolation of characters that are quite difficult to pass as parts of command-line arguments even in the shell (for the following examples, tmp/a.tsv
and tmp/b.tsv
can be created in the shell with echo -e "foo\tbar\nbaz\tqux" > tmp/a.tsv; echo -e "foo\t1\nbaz\t2" > tmp/b.tsv
):
julia> tab = "\t";
julia> cmd = `join -t$tab tmp/a.tsv tmp/b.tsv`;
julia> cmd.exec
4-element Union(UTF8String,ASCIIString) Array:
"join"
"-t\t"
"tmp/a.tsv"
"tmp/b.tsv"
julia> run(cmd)
foo bar 1
baz qux 2
Moreover, what comes after the $
can actually be any valid Julia expression, not just a variable name:
julia> `join -t$"\t" tmp/a.tsv tmp/b.tsv`.exec
4-element Union(UTF8String,ASCIIString) Array:
"join"
"-t\t"
"a.tsv"
"b.tsv"
A tab character is somewhat harder to pass in the shell, requiring command interpolation and some tricky quoting:
bash-3.2$ join -t"$(printf '\t')" tmp/a.tsv tmp/b.tsv
foo bar 1
baz qux 2
While interpolating values with spaces and other strange characters is great for non-brittle construction of commands, there was a reason why the shell split values on spaces in the first place: to allow interpolation of multiple arguments. Most modern shells have first-class array types, but older shells used space-separation to simulate arrays. Thus, if you interpolate a value like “foo bar” into a command in the shell, it’s treated as two separate words by default. In languages with first-class array types, however, there’s a much better option: consistently interpolate single values as single arguments and interpolate arrays as multiple values. This is precisely what Julia’s backtick interpolation does:
julia> dirs = ["foo", "bar", "baz"];
julia> `find $dirs -type f`.exec
6-element Union(UTF8String,ASCIIString) Array:
"find"
"foo"
"bar"
"baz"
"-type"
"f"
And of course, no matter how strange the strings contained in an interpolated array are, they become verbatim arguments, without any shell interpretation. Julia’s backticks have one more fancy trick up their sleeve. We saw earlier (without really remarking on it) that you could interpolate single values into a larger argument:
julia> x = "bar";
julia> `echo foo$x`
`echo foobar`
What happens if x
is an array?
Only one way to find out:
julia> x = ["bar", "baz"];
julia> `echo foo$x`
`echo foobar foobaz`
Julia does what the shell would do if you wrote echo foo{bar,baz}
.
This even works correctly for multiple values interpolated into the same shell word:
julia> dir = "/data"; names = ["foo","bar"]; exts=["csv","tsv"];
julia> `cat $dir/$names.$exts`
`cat /data/foo.csv /data/foo.tsv /data/bar.csv /data/bar.tsv`
This is the same Cartesian product expansion that the shell does if multiple {...}
expressions are used in the same word.
You can read more in Julia’s online manual, including how to construct complex pipelines, and how shell-compatible quoting and interpolation rules in Julia’s backtick syntax make it both simple and safe to cut-and-paste shell commands into Julia code. The whole system is designed on the principle that the easiest thing to do should also be the right thing. The end result is that starting and interacting with external processes in Julia is both convenient and safe.


Cork City Council issues 'tidal flood warning' as Storm Sebastian heads for Ireland
Heavy rain and gale force winds are expected at times tomorrow which could bring a risk of flooding
by Sam RobertsCork City Council have issued a tidal flood warning as the remnants of Storm Sebastian head for Ireland.
Met Eireann has warned that heavy rain and gale force winds are expected at times tomorrow which could bring a risk of flooding in some parts of the country.
And the Rebel county will be hit particularly hard as the tail end of the former Tropical Storm Sebastien arrives on Irish shores.
A Cork City Council spokesman said: "Cork City Council wish to advise that there will be a period of very high astronomical Spring Tides from Tuesday 26th November until Wednesday 27th November.
"A tidal surge and periodic strong southerly/south-easterly winds are also predicted over the next few days. These factors combined will result in tide levels higher than the natural tide levels.
"From Tuesday morning until Wednesday evening, prior to and after these tides, there is a significant risk of tidal flooding in the City.
"Householders and Businesses should take Precautionary Measures to protect property."
Cork City Council have also highlighted particular low-lying areas that will be worst hit by the unsettled weather.
A spokesman added: "It is likely that flooding will occur in the low lying areas of the City Centre in particular on Morrison’s Quay, Fr. Mathew Quay, Fr. Mathew Street, Union Quay, Trinity Bridge, South Terrace, Lavitts Quay, Kyrls Street, Kyrls Quay, Crosses Green, Sharman Crawford St and Wandesford Quay.
"There is also a risk of flooding along Lapps Quay, McSwiney Quay, Albert Quay, Kennedy Quay, Proby’s Quay, French’s Quay, Lancaster Quay, Sullivan’s Quay, South Mall via side streets onto Oliver Plunkett St., and thence to Patrick Street, Grand Parade, Washington Street, and Lower Glanmire Road.
"The situation will continue to be monitored and further advice will be issued."
The council advised that a limited stock of gel-bags and sand-bags will be available for collection between 12pm and 6pm today, and 8am and 6pm tomorrow.
They added that road closures and traffic restrictions are also likely to be put in place.
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For the soup, melt the butter in a saucepan. When foaming, add the cumin seeds and after 30-40 seconds add the ginger and the onion. Gently fry for 1-2 minutes, or until the onion is soft and translucent. Add the haggis and fry for 3-4 minutes. Pour in the stock, bring to the boil and simmer for 5-6 minutes. Then remove from the heat.
Purée the contents until very smooth using a blender (if using a jug blender be careful to avoid splashes from the hot liquid). Add the cream and continue to blend. Return the soup to the pan and bring to a simmer. If the soup is too thick for your liking, dilute it with a little extra stock or water. Season with salt and pepper.
For the haggis and potato cakes, heat a tablespoon of oil or butter in a frying pan. When hot, add the ginger, garlic and green chillies. Gently fry until the garlic begins to colour slightly. Add the onion and gently fry until soft and translucent. Stir in the crushed haggis and gently fry for 3-4 minutes. Season with salt and pepper.
Then add the coriander seeds, fresh coriander and fresh mint. Set aside to cool completely.
For the tomato ketchup, blend all the ingredients together then taste and adjust the Tabasco to suit your palate. Place in a small bowl for serving. (If, preferred, you can also blend some mayonnaise into this for a creamier sauce).
For the crushed potatoes, put the potatoes in enough boiling salted water to cover the potatoes by at least an inch of water. Boil until the slices are tender. Drain and return the potatoes to the pan. Dry them over a low heat, using a spatula to stir and make sure they don’t stick to the bottom. Remove from the heat and mash them (or pass through a ricer if you have one).
Spread the potatoes on a tray and set aside to cool. When cooled, stir in the crushed cumin, chilli, salt and pepper. You can also add dried mango powder if you have any.
To finish the haggis and potato cakes, put the egg wash in shallow bowl and the breadcrumbs and flour on separate plates. Divide the crushed potatoes into equal portions (ideally 6) and divide the haggis mixture similarly.
Flatten each potato portion in the palm of your hand to roughly 1cm/½in thickness and place the haggis mixture in the centre of it. Fold over the potato to encase the haggis evenly and then flatten to form a thick cake roughly 2.5cm/1in thick. Shape it gently until you have no cracked edges and no filling leaking out. Sprinkling a little flour on the potato cakes to help you smooth the surface.
Roll the potato cakes in just enough flour to coat, dust off any excess. Dip in the beaten egg to coat then cover with breadcrumbs.
Preheat the oven to 100C/80C Fan/Gas ¼.
Heat 1-2 inches of oil in a deep-sided frying pan or casserole. When hot, fry the potato cakes until golden-brown all over. Transfer them to a baking tray lined with kitchen paper (to absorb any excess oil) and place in the warm oven. Warm the serving plates in the oven as well.
Fry the eggs in the same pan (you may need to remove some of the oil).
To serve, reheat the soup, add the chopped coriander and put into bowls. Put a dollop of spiced ketchup on each potato cake. Top with a fried egg and serve with a hot crusty roll or toast, some salad leaves and the soup.


|
Pebble Foundation Classes
0.2.0
C++ for Pebble
|
Using the Pebble accelerometer. More...
Typedefs | |
| typedef void(* | AccelDataHandler) (AccelData *data, uint32_t num_samples) |
| typedef void(* | AccelRawDataHandler) (AccelRawData *data, uint32_t num_samples, uint64_t timestamp) |
| typedef void(* | AccelTapHandler) (AccelAxisType axis, int32_t direction) |
| typedef void(* | AccelDataHandler) (AccelData *data, uint32_t num_samples) |
| typedef void(* | AccelRawDataHandler) (AccelRawData *data, uint32_t num_samples, uint64_t timestamp) |
| typedef void(* | AccelTapHandler) (AccelAxisType axis, int32_t direction) |
Enumerations | |
| enum | AccelAxisType { ACCEL_AXIS_X = 0, ACCEL_AXIS_Y = 1, ACCEL_AXIS_Z = 2, ACCEL_AXIS_X = 0, ACCEL_AXIS_Y = 1, ACCEL_AXIS_Z = 2 } |
| Enumerated values defining the three accelerometer axes. More... | |
| enum | AccelSamplingRate { ACCEL_SAMPLING_10HZ = 10, ACCEL_SAMPLING_25HZ = 25, ACCEL_SAMPLING_50HZ = 50, ACCEL_SAMPLING_100HZ = 100, ACCEL_SAMPLING_10HZ = 10, ACCEL_SAMPLING_25HZ = 25, ACCEL_SAMPLING_50HZ = 50, ACCEL_SAMPLING_100HZ = 100 } |
| Valid accelerometer sampling rates, in Hz. More... | |
| enum | AccelAxisType { ACCEL_AXIS_X = 0, ACCEL_AXIS_Y = 1, ACCEL_AXIS_Z = 2, ACCEL_AXIS_X = 0, ACCEL_AXIS_Y = 1, ACCEL_AXIS_Z = 2 } |
| Enumerated values defining the three accelerometer axes. More... | |
| enum | AccelSamplingRate { ACCEL_SAMPLING_10HZ = 10, ACCEL_SAMPLING_25HZ = 25, ACCEL_SAMPLING_50HZ = 50, ACCEL_SAMPLING_100HZ = 100, ACCEL_SAMPLING_10HZ = 10, ACCEL_SAMPLING_25HZ = 25, ACCEL_SAMPLING_50HZ = 50, ACCEL_SAMPLING_100HZ = 100 } |
| Valid accelerometer sampling rates, in Hz. More... | |
Functions | |
| struct | __attribute__ ((__packed__)) |
| A single accelerometer sample for all three axes. More... | |
| int | accel_service_peek (AccelData *data) |
| int | accel_service_set_sampling_rate (AccelSamplingRate rate) |
| int | accel_service_set_samples_per_update (uint32_t num_samples) |
| void | accel_data_service_subscribe (uint32_t samples_per_update, AccelDataHandler handler) |
| void | accel_data_service_unsubscribe (void) |
| void | accel_tap_service_subscribe (AccelTapHandler handler) |
| void | accel_tap_service_unsubscribe (void) |
| void | accel_raw_data_service_subscribe (uint32_t samples_per_update, AccelRawDataHandler handler) |
Variables | |
| AccelData | |
| AccelRawData | |
| AccelData | |
| AccelRawData | |
Using the Pebble accelerometer.
The AccelerometerService enables the Pebble accelerometer to detect taps, perform measures at a given frequency, and transmit samples in batches to save CPU time and processing.
For available code samples, see the feature-accel-discs example app.
| typedef void(* AccelDataHandler) (AccelData *data, uint32_t num_samples) |
Callback type for accelerometer data events
| data | Pointer to the collected accelerometer samples. |
| num_samples | the number of samples stored in data. |
| typedef void(* AccelDataHandler) (AccelData *data, uint32_t num_samples) |
Callback type for accelerometer data events
| data | Pointer to the collected accelerometer samples. |
| num_samples | the number of samples stored in data. |
| typedef void(* AccelRawDataHandler) (AccelRawData *data, uint32_t num_samples, uint64_t timestamp) |
Callback type for accelerometer raw data events
| data | Pointer to the collected accelerometer samples. |
| num_samples | the number of samples stored in data. |
| timestamp | the timestamp, in ms, of the first sample. |
| typedef void(* AccelRawDataHandler) (AccelRawData *data, uint32_t num_samples, uint64_t timestamp) |
Callback type for accelerometer raw data events
| data | Pointer to the collected accelerometer samples. |
| num_samples | the number of samples stored in data. |
| timestamp | the timestamp, in ms, of the first sample. |
| typedef void(* AccelTapHandler) (AccelAxisType axis, int32_t direction) |
Callback type for accelerometer tap events
| axis | the axis on which a tap was registered (x, y, or z) |
| direction | the direction (-1 or +1) of the tap |
| typedef void(* AccelTapHandler) (AccelAxisType axis, int32_t direction) |
Callback type for accelerometer tap events
| axis | the axis on which a tap was registered (x, y, or z) |
| direction | the direction (-1 or +1) of the tap |
| enum AccelAxisType |
Enumerated values defining the three accelerometer axes.
| enum AccelAxisType |
Enumerated values defining the three accelerometer axes.
| enum AccelSamplingRate |
Valid accelerometer sampling rates, in Hz.
| enum AccelSamplingRate |
Valid accelerometer sampling rates, in Hz.
| struct __attribute__ | ( | (__packed__) | ) |
A single accelerometer sample for all three axes.
A single accelerometer sample for all three axes including timestamp and vibration rumble status.
Data structure for one serialized key/value tuple
acceleration along the x axis
acceleration along the y axis
acceleration along the z axis
true if the watch vibrated when this sample was collected
timestamp, in milliseconds
acceleration along the x axis
acceleration along the y axis
acceleration along the z axis
The key
The type of data that the .value
fields contains.
The length of .value
in bytes
The value itself.
The different union fields are provided for convenience, avoiding the need for manual casts.
The byte array value. Valid when .type
is TUPLE_BYTE_ARRAY.
The C-string value. Valid when .type
is TUPLE_CSTRING.
The 8-bit unsigned integer value. Valid when .type
is TUPLE_UINT and .length
is 1 byte.
The 16-bit unsigned integer value. Valid when .type
is TUPLE_UINT and .length
is 2 byte.
The 32-bit unsigned integer value. Valid when .type
is TUPLE_UINT and .length
is 4 byte.
The 8-bit signed integer value. Valid when .type
is TUPLE_INT and .length
is 1 byte.
The 16-bit signed integer value. Valid when .type
is TUPLE_INT and .length
is 2 byte.
The 32-bit signed integer value. Valid when .type
is TUPLE_INT and .length
is 4 byte.
acceleration along the x axis
acceleration along the y axis
acceleration along the z axis
true if the watch vibrated when this sample was collected
timestamp, in milliseconds
acceleration along the x axis
acceleration along the y axis
acceleration along the z axis
| void accel_data_service_subscribe | ( | uint32_t | samples_per_update, |
| AccelDataHandler | handler | ||
| ) |
Subscribe to the accelerometer data event service. Once subscribed, the handler gets called every time there are new accelerometer samples available.
| handler | A callback to be executed on accelerometer data events |
| samples_per_update | the number of samples to buffer, between 0 and 25. |
| void accel_data_service_unsubscribe | ( | void | ) |
Unsubscribe from the accelerometer data event service. Once unsubscribed, the previously registered handler will no longer be called.
| void accel_raw_data_service_subscribe | ( | uint32_t | samples_per_update, |
| AccelRawDataHandler | handler | ||
| ) |
Subscribe to the accelerometer raw data event service. Once subscribed, the handler gets called every time there are new accelerometer samples available.
| handler | A callback to be executed on accelerometer data events |
| samples_per_update | the number of samples to buffer, between 0 and 25. |
| int accel_service_peek | ( | AccelData * | data | ) |
Peek at the last recorded reading.
| [out] | data | a pointer to a pre-allocated AccelData item |
| int accel_service_set_samples_per_update | ( | uint32_t | num_samples | ) |
Change the number of samples buffered between each accelerometer data event
| num_samples | the number of samples to buffer, between 0 and 25. |
| int accel_service_set_sampling_rate | ( | AccelSamplingRate | rate | ) |
Change the accelerometer sampling rate.
| rate | The sampling rate in Hz (10Hz, 25Hz, 50Hz, and 100Hz possible) |
| void accel_tap_service_subscribe | ( | AccelTapHandler | handler | ) |
Subscribe to the accelerometer tap event service. Once subscribed, the handler gets called on every tap event emitted by the accelerometer.
| handler | A callback to be executed on tap event |
| void accel_tap_service_unsubscribe | ( | void | ) |
Unsubscribe from the accelerometer tap event service. Once unsubscribed, the previously registered handler will no longer be called.


Runtime filters
Runtime filters used for filtering data by field (or SQL expression) where filter value will be accessible during runtime only. Similar results can be reached using LINQ, but may be field to filter on is not selected. Or the result we need is not a read-only dynamic dataset (see LINQ support and Dynamic queries), but feature rich repository, only filtered.
Runtime filters in XData are defined as static fields of data object typed as RuntimeFilter. Name of runtime filter is mast to be unique over data object mapping structure (with subqueries). When value of runtime filter is set and filter with same name is not found in mapping, it will be dynamically added to mapping structure.
To set value of runtime filter, use SetFilterValue method of repository. To clear one runtime filter value, set null value. To reset all runtime filters, use Reset method of repository.
Tip
When runtime filter value is set to null check filter null processing behavior assigned to this filter!
var rep = invoice.GetRepository();
var hist = dataScope.GetRepository<DocHistory>(rep.Layer, context: rep.Context)
.Reset()
.SetFilterValue(DocHistory.FilterByDocId, invoice.GetProperty<long>("DocId"))
.SetFilterValue(DocHistory.FilterByDocStateId, invoice.DocState.Key);
var newHist = hist.New();
newHist.HistoryDate = DateTime.UtcNow;
return hist.Submit(ref newHist);
Runtime filters can be described on place:
//Using column name
.SetFilterValue("DocId", doc_id)
//Using property expression
.SetFilterValue(x => DocNumber, docNumber)
//Using FilterName helper
.SetFilterValue(new FilterName("FilterByDocId"), doc_id)


Interface ServerManager
Manages the asset servers of the application.
Direct known implementers
Puli\Manager\Server\PackageFileServerManager
Namespace: Puli\Manager\Api\Server
Author: Bernhard Schussek<EMAIL_ADDRESS>Since: 1.0
Located at Api/Server/ServerManager.php
Author: Bernhard Schussek<EMAIL_ADDRESS>Since: 1.0
Located at Api/Server/ServerManager.php
public
|
|
public
|
|
public
|
#
removeServers( Webmozart\Expression\Expression $expr )
Removes all servers matching the given expression. |
public
|
|
public
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|
public
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|
public
|
#
findServers( Webmozart\Expression\Expression $expr )
Returns all servers matching the given expression. |
public
boolean
|
|
public
boolean
|
#
hasServers( Webmozart\Expression\Expression $expr = null )
Returns whether the manager has any servers. |


Once at the second step, they'd have to return to the home page, log in again, then re-enter their arrival and departure data.
LEARN MORE


About Us
We are a family owned business.
We established MAXX Machines at the end of 2009.
Our goal is to provide quality service to all vehicles at a price that anyone can afford!
We pride ourselves on our ability to service all vehicles, this includes:
1. Cars/Trucks [Including Diesels]
2. Motorcycles
3. Jet Skis [Personal Water Craft]
4. Go-Carts
5. ATVs
6. Boats
7. Weed eaters and Lawn Mowers
8. Foreign and American Vehicles [Chinese]
If you bring us a quote from a dealer or chain of service centers we can beat the quote every time.
We slash prices often by hundreds of dullars.
In addition to being a service and repair establishment, we offer the additional service of
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Come on down and give as a try! We are here to serve you! You will be kickin' yourself for not
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If you like this page, hate it, or would like to suggest changes,
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do send your feedback.
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visit our submit page to
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Accounts will generally be validated within 24 hours, and of
course will be disabled if there is abuse.
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On the first full day this page went live, 100,000 people visited this Rotten
News page. Of course that was the day after the WTC attacks, so that number is
somewhat distorted. Right now this page gets between 50,000 and 75,000 people
every day.
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We encourage everyone to link to this page; if you like it, others probably
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October 27, 2001
U.S. begging for your anti-terrorism ideas
Times | Submitted by: Crazy Quagga
Having run out of ideas after little more than a month, the Pentagon is
offering a reward to the general public to figure out a way to defeat
terrorism. "Donald Rumsfeld, the Secretary of Defence, is known to be
frustrated with his underlings’ inability to think "out of the box" in this new
kind of war, but appealing to every Tom, Dick and Harry for a good idea or two
may not do much to reassure the public that the country’s safety is in the best
possible hands." In other words, the people who dedicated their lives to
protecting this country have no clue what to do. It gives you a warm
feeling.... a warm, wet, yellow feeling in your pants, that is.
|
|
Read article...
|
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Coroner's tale of Ground Zero
Aurora Sentinel | Submitted by: Soylent
A coroner who works for FEMA gives this account of his work at Ground Zero.
"We were picking pieces of people out of other pieces of people," Dobersen said. "I think the largest part we received weighed maybe 10 or 15 pounds. That gives you an idea of the energy and force involved."
|
|
Read article...
|
|
Reporter terrorizes sewage plant
Nashville Scene | Submitted by: Soylent
"After warning that terrorist saboteurs might try to poison Metro water with
anthrax or smallpox, WTVF-Channel 5 reporter Rob Manning thought
he'd show viewers how easily that could happen by dramatically crawling under
a wire fence just a few hundred yards from the city's water supply."
|
|
Read article...
|
|
Pinheads of Gujarat
New York Times | Submitted by: anonymous
Pinheads in Pakistan. "The small-headed ones became known as chuhas — "rat
children" — and generations of them have been abandoned here by parents to take
their place in the legend." But there is also a more sinister side: "In the
past 15 years or so, the practice of reshaping a child's skull — so evil that
many people choose to call it rumor — has been snuffed out, said Mian
Ubaidullah, a Pakistani historian who studies the chuhas."
[subscription required, unfortunately]
|
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Read article...
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NOTE: this was originally written in 2022, but never finished. This was updated and published in 2023, but some links are now out-of-date, etc.
It’s quite difficult to find real, credible, information on the internet nowadays.
Something that has made this easier (for me, at least) has been to search specifically for content on .edu
domains, since that TLD is restricted to accredited institutions. This is as simple as prefixing your Google search with inurl:edu
.
I’ve been doing this for a while with no issues, so you can imagine my surprise when I recently tried to find information about some “fat burner” pills that were being sold on Amazon and searched for inurl:edu nobi fat burner
in the off-chance it was mentioned in some study/paper.
Dang! If UMass Med, Penn State, and UC Santa Cruz are recommending these things, then they must be safe and effective.
However, just to do my due diligence, I should probably at least click on the first link and see what it says about-
Oh, that’s odd.
This doesn’t look like a university website?
Maybe I clicked the wrong one, let me go back and copy and paste the URL.
Huh, a 404 page this time? Weird. Oh well.
If three university sites are recommending these entirely unregulated pills to me, then they must be good!
I decided to look a little bit deeper into the first search result.
First, I took the raw URL and curl
ed it, but I got a 404. For some reason, it didn’t redirect to the spam site.
My first instinct here was that something was checking if the client was the “expected” browser somehow. For example, it could be checking if the User-Agent
, Referer
, or Accept
headers matches what would be sent from Chrome, TLS fingerprinting, etc.
Thankfully it was one of my first guesses. Adding the Referer
of https://www.google.com/
did the trick:
Admittedly, at this point I was a tiny bit worried that this was something on my local machine, so I opted for the most optimal solution for checking: Sending my brother an unprompted discord message asking him to curl
a suspicious URL with no context. Sadly it turned out that he did, in fact, want context.
How unfortunate.
Regardless, I eventually convinced him to run it and confirmed it wasn’t an issue on my end.
Now at this point, I knew that it was happening on their server(s), and under which conditions it happened (coming from Google Search results), but I had no way of figuring out why it was happening. (e.g. something in their reverse proxy, some PHP code, etc)
The only indicator I had was the very unusual haircki
cookie that was being set when visiting the regular page. It could technically be legitimate, but it seemed extremely out-of-place for a uni website.
By sheer luck, a sourcegraph search turned up a result for haircki
, which lead me to a GitHub repo containing WordPress malware samples:
https://github.com/stefanpejcic/wordpress-malware/blob/master/01.06.2021/index.php
After briefly looking over that sample from another site, I feel confident in saying that it produces the same behavior of search engine link hijacking that I’m seeing on the umassmed.edu
site.
The answer here turned out to be less interesting than I thought, just another case of websites running vulnerable versions of WordPress and/or WP plugins.
Given what I’ve shown so far, you would be justified if your first comment was:
“You’ve only provided three example sites, I’d hardly call that representative of all .edu domains”
As such, here are the following institutions I’ve found with compromised sites / pdf hosting at the time of writing (2022-09-07):
Compromised sites:
Compromised PDF hosting:
Update (2023-07-29):
“Okay, sure, the sites were hacked. That doesn’t make the universities less credible. That’s just dumb.”
You know what, fair. Maybe it’s unreasonable to say that all .edu sites are non-credible because a few of them had poor maintenance/security. This was not something that the universities would ever willingly advertise, and it was out of their control.
In fact, while looking into these compromised sites, I found a great paper accessible via Penn State University’s CiteSeerX instance: https://citeseerx.ist.psu.edu/viewdoc/download?doi=<IP_ADDRESS>1.1013&rep=rep1&type=pdf
In this paper, they describe the exact behavior of these compromised sites only accessible via certain IPs/User-Agent
header(s)/Referrer
header(s).
It’s great to see this paper on accessible via Penn State University’s domain, as I found no instance of this malware on their site while searching.
It’s only slightly unfortunate that there was no need for any malware, as the fat burner pills are advertised in the Penn State University Collegian paper anyway:
Oh well.


A very simple that helps you realize where countrie, states, counties, cities or rivers actually are in the world.
Once you hit the button below, we'll pull the trigger and the stopwatch will start counting. That's it. Simple, isn't it?
Don't show me this again


Showing {{ showingOrganizations }} out of {{ organizationsCount }} results
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{{ phones[0] }}


class: title-slide center background-image: url(Parthum_Wheeler_Slides_files/EPA_seal.png), url(Parthum_Wheeler_Slides_files/NCEE_seal.png) background-position: 35% 95%, 61% 93% background-size: 10%, 20%

Understanding the Code of Federal Regulations through Natural Language Processing

Bryan Parthum and Will Wheeler
 U.S. EPA, National Center for Environmental Economics
 March 30, 2021
 --- class: remark-slide
Motivation

What is the goal?
 - The ability to quantify and classify the Code of Federal Regulations (CFR) - Think of this as capacity building. In order to ask interesting questions, we need to have the infrastructure in place to objectively read, process, and quantify federal documents. -- count: false
What do we mean by quantify and classify?
 -- count: false - There are many metrics, each are useful in their own way - Scoring within (\# words, rarity, etc.) - Scoring across (networks, dependencies, etc.) - Note: In the CFR, these measures have variation within parts, across parts, and across time ??? Upstream processing that will be used as inputs to other applications Methods: classification using Bag of Words (BoW), Term Frequency Inverse Document Frequency (TF-IDF) ---
Text as Data .font60[(Gentzkow et al. 2019)]

 .pull-60[
<bf>Quantify:</bf>
 .tab[]1\. Represent raw text `\(\mathcal{D}\)` as a numerical array `\(\bf{C}\)`
<bf>Classify:</bf>
 .tab[]2\. Map `\(\bf{C}\)` to predicted class `\(\widehat{\hskip-0.5pt \bf{V}}\)` of unknown class `\(\bf{V}\)`
Apply:
 .tab[]3\. Use `\(\widehat{\hskip-0.5pt \bf{V}}\)` in subsequent descriptive or causal analysis ] ??? 1. Reduce the dimensionality of the text 2. Linking the text to categories of interest ---
NLP in Practice

Economics:
 - Effect of regulations on GDP (Coffey et al. 2020) - Tracking evolution of methods in economics (Currie et al. 2020) - Understanding how people think about taxes (Stantcheva 2020; NBER) - Valuing climate amenities using social media posts (Baylis et al. 2018, Baylis 2020) - Predicting future citations using referee comments (Bandeh-Ahmadi, 2014) - Contract flexibility as a result of environmental regulations (Kosnik, 2014)
Government:
 - ***"U.S. government agencies to use AI to cull and cut outdated regulations"*** (Reuters, 10/16/2020) - Categorize public comments in rule-making (HHS and EPA 2014) - Task complexity in EPA's Standard Operating Procedures (Bird et al. 2009) ---
Why NLP and the CFR?
 .pull-left-c[

  ] .pull-right-c[

  ] ---
Products

1. Code and documentation
 - Likely all in R, although there might be Python integration (all open source) - Public and freely available on GitHub
2. NCEE Working Paper
 - Summarize our approach to quantifying the CFR - An application to several outcomes of interest ---
Applications

Regulatory outcomes
 - NPDES, SDWA, RCRA, CERCLA (Superfund), etc. - Permitting: violations/non-compliance, proportion of applications approved, complaints - Litigation: length, contract completeness - EPA regulations that fall under other agencies (USGS, USDA, etc.) - Regulation across media (are water regs consistent with air, etc.)1
Environmental outcomes
 - Surface water and air quality (WQI/AQI) - Drinking water quality, test outcomes, etc. - Specific toxics as endpoints (how can we show the different ways that lead is regulated)1 .font60[[1] Examples of outcomes that are internal to the CFR] ---
Econometrics

What does a regression look like?
 `$$y_{{\small \bf{V}}t}=\bf{X}\beta + \Phi(\bf{C}_{{\small \widehat{\hskip-0.5pt \bf{V}}}t})\delta + \varepsilon_{{\small \bf{V}}t}$$` - `\(\bf{C}\)` is upstream NLP processing of `\(\mathcal{D}\)` (quantify) - `\(\widehat{\hskip-0.5pt \bf{V}}\)` is predicted class using ML (classify) - `\(\Phi(\bf{C}_{{\small \widehat{\hskip-0.5pt \bf{V}}}t})\)` can consist of many metrics/features: - sum of word/phrase count (simple "token" score) - sum of TF-IDF scores in part/subpart - relational/network features of other parts `\(\in\mathcal{D}\)` - relational/network features of other documents `\(\notin\mathcal{D}\)` ---
Other Applications and Extensions

Other applications and extensions
 - Spatial distribution of Title 40 (map network/complexity to geographical units, demographics, industries) - Process ICRs - Complexity of EPA Guidelines - Leadership influence on network/complexity (i.e. `\(\bf{C}_{{\small \widehat{\hskip-0.5pt \bf{V}}}t}\)` becomes dependent variable) - Apply text methods to permits, consumer confidence reports, FOIA requests, and on and on - *[YOUR PROJECT HERE]* --- class: title-slide center background-image: url(Parthum_Wheeler_Slides_files/EPA_seal.png), url(Parthum_Wheeler_Slides_files/NCEE_seal.png) background-position: 35% 95%, 61% 93% background-size: 10%, 20% .pull-60[ .font140[ Thank you!

 Questions? ] ] .pull-left[ .font120[Bryan Parthum] <a<EMAIL_ADDRESS>src="Parthum_Wheeler_Slides_files/email.png" width="50" height="50">     ] .pull-right[ .font120[Will Wheeler] <a<EMAIL_ADDRESS>src="Parthum_Wheeler_Slides_files/email.png" width="50" height="50">     ] --- class: remark-slide
Examples from the CFR
 40 CFR § 60.4201: What emission standards must I meet for non-emergency engines if I am a stationary CI internal combustion engine manufacturer? .font90[ .pull-60[ (a) Stationary CI internal combustion engine manufacturers must certify their 2007 model year and later non-emergency stationary CI ICE with a maximum engine power less than or equal to 2,237 kilowatt (KW) (3,000 horsepower (HP)) and a displacement of less than 10 liters per cylinder to the certification emission standards for new nonroad CI engines in .blue[40 CFR 89.112], .blue[40 CFR 89.113], .blue[40 CFR 1039.101], .blue[40 CFR 1039.102], .blue[40 CFR 1039.104], .blue[40 CFR 1039.105], .blue[40 CFR 1039.107], and .blue[40 CFR 1039.115], as applicable, for all pollutants, for the same model year and maximum engine power. ] ] - subpart (a) of 8 (with 7 more sub-subparts) - 9 internal references - spiderweb of references from referenced parts --- count: false
Examples from the CFR
 40 CFR § 60.4201: What emission standards must I meet for non-emergency engines if I am a stationary CI internal combustion engine manufacturer? .font90[ .pull-60[ (a) Stationary CI internal combustion engine manufacturers must certify .l-blue[their] 2007 model year .l-blue[and] later non-emergency stationary CI ICE with .l-blue[a] maximum engine power less than .l-blue[or] equal .l-blue[to] 2,237 kilowatt (KW) (3,000 horsepower (HP)) .l-blue[and a] displacement .l-blue[of] less than 10 liters .l-blue[per] cylinder .l-blue[to the] certification emission standards .l-blue[for] new nonroad CI engines .l-blue[in] .blue[40 CFR 89.112], .blue[40 CFR 89.113], .blue[40 CFR 1039.101], .blue[40 CFR 1039.102], .blue[40 CFR 1039.104], .blue[40 CFR 1039.105], .blue[40 CFR 1039.107], .l-blue[and] .blue[40 CFR 1039.115], .l-blue[as] applicable, for all pollutants, .l-blue[for the] same model year .l-blue[and] maximum engine power. ] ] - Remove filler words (.l-blue[LIGHT BLUE]) --- count: false
Examples from the CFR
 40 CFR § 60.4201: What emission standards must I meet for non-emergency engines if I am a stationary CI internal combustion engine manufacturer? .font90[ .pull-60[ (a) Stationary CI internal combustion engine manufacturers .epa-green[must] certify .l-blue[their] 2007 model year .l-blue[and] later non-emergency stationary CI ICE with .l-blue[a] .epa-green[maximum] engine power .epa-green[less than] .l-blue[or] equal .l-blue[to] 2,237 kilowatt (KW) (3,000 horsepower (HP)) .l-blue[and a] displacement .l-blue[of] .epa-green[less than] 10 liters .l-blue[per] cylinder .l-blue[to the] certification emission standards .l-blue[for] new nonroad CI engines .l-blue[in] .blue[40 CFR 89.112], .blue[40 CFR 89.113], .blue[40 CFR 1039.101], .blue[40 CFR 1039.102], .blue[40 CFR 1039.104], .blue[40 CFR 1039.105], .blue[40 CFR 1039.107], .l-blue[and] .blue[40 CFR 1039.115], .l-blue[as] applicable, .epa-green[for all] pollutants, .l-blue[for the] same model year .l-blue[and] .epa-green[maximum] engine power. ] ] - Remove filler words (.l-blue[LIGHT BLUE]) - Weight action words (.epa-green[GREEN]) --- count: false
Examples from the CFR
 40 CFR § 60.4201: What emission standards must I meet for non-emergency engines if I am a stationary CI internal combustion engine manufacturer? .font90[ .pull-60[ (a) Stationary CI internal combust.gray[ion] engine manufactur.gray[ers] .epa-green[must] certif.gray[y] .l-blue[their] 2007 model year .l-blue[and] later non-emergency stationary CI ICE with .l-blue[a] .epa-green[maximum] engine power .epa-green[less than] .l-blue[or] equal .l-blue[to] 2,237 kilowatt (KW) (3,000 horsepower (HP)) .l-blue[and a] displace.gray[ment] .l-blue[of] .epa-green[less than] 10 liter.gray[s] .l-blue[per] cylinder .l-blue[to the] certificat.gray[ion] emission standard.gray[s] .l-blue[for] new nonroad CI engine.gray[s] .l-blue[in] .blue[40 CFR 89.112], .blue[40 CFR 89.113], .blue[40 CFR 1039.101], .blue[40 CFR 1039.102], .blue[40 CFR 1039.104], .blue[40 CFR 1039.105], .blue[40 CFR 1039.107], .l-blue[and] .blue[40 CFR 1039.115], .l-blue[as] applicable, .epa-green[for all] pollutant.gray[s], .l-blue[for the] same model year .l-blue[and] .epa-green[maximum] engine power. ] ] - Remove filler words (.l-blue[LIGHT BLUE]) - Weight action words (.epa-green[GREEN]) - Stemming (.gray[GRAY]) ---
Term Frequency-Inverse Document Frequency (TF-IDF)
 The TF-IDF score `\(\Psi\)`, for word `\(i\)`, of section `\(s\)`, in document `\(d\)`: `$$\Psi_{isd}=TF_{id} \times IDF_{id}\tag{1}$$`
 where Term Frequency `\(TF\)`: `$$TF_{isd} = log(1+freq_{is})\tag{2}$$`
 and Inverse Document Frequency `\(IDF\)`: `$$IDF_{id} = log(\frac{N_{s}}{N_{s}|_{i \in s}})\tag{3}$$` --- exclude: true


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Before Change
new_shape += (-3,)
elif isinstance(index, Symbol):
if new_shape != ():
new_shape += (-4,)
sliced = _npi.slice(result, begin, end, step)
result = _npi.reshape(sliced, new_shape)
if not is_symbol_tuple:
is_symbol_tuple = True
else:
raise IndexError("Only integer, slice, symbol or tuple of these types"
After Change
return _npi.advanced_indexing(self, key)
elif isinstance(key, tuple) and len(key) == 0:
return self
elif isinstance(key, tuple) and all(isinstance(k, Symbol) for k in key):
key = _npi.stack(*[i for i in key])
sliced = _npi.advanced_indexing_multiple(self, key)
return sliced


CAT PEOPLE (1982) A Film Review Copyright Dragan Antulov 2000
Whenever someone mentions "1980s" and "horror film" in the same sentence, the first association is usually FRIDAY 13TH, NIGHTMARE ON ELM STREET or myriad of similar low-budget "teenage slasher" films inspired by their respective successes. But, in the beginning of decade, horror films didn't exclusively belong to the world of B-cinema. At least some of them had high budgets and ambitious directors at their helm. One of such examples is CAT PEOPLE, 1982 horror by Paul Schrader, film that enjoyed quite popularity at the beginning of the decade only to completely vanish from moviegoer's memory in few years. That same fate was experienced by many great 1980s hits, but rarely was so unjustified like in the case of this film. The only explanation could be found in critics' snobbery. CAT PEOPLE was remake of classic 1942 B-horror, and, as such, it simply had to be inferior piece of work. Also, large quantities of violence, gore, sex and nudity didn't help either - many critics viewed Schrader's film as exploitative.
Plot of this movie begins when 21-year old Irena Gallier (played by Nastassja Kinski), who spent most of her life in orphanages and foster homes, arrives to New Orleans to finally reunite with her older brother Paul (played by Malcolm McDowell), who works as priest. Irena is so thrilled to find long lost family that she doesn't care about few bizarre and mysterious details of Paul's lifestyle, including long periods he spends outside home. During one of such periods Irena visits local ZOO and becomes obsessed with one of its exhibits - magnificent black panther. The animal was brought in the cage after attack on prostitute. Oliver Yates (played by John Heard), zookeeper who had caught the beast is still puzzled by many unexplained circumstances of the bizarre incident, as well as the origin of the panther. His attention, however, shifts towards beautiful young woman who spends all her time in front of panther's cage. He falls in love with Irena, who, although still a virgin, returns his feelings. When Paul returns home, he would show not only unusually high degree of insight into Irena's romantic life, but also his own unnatural jealousy. This revelation is just one of many shocking discoveries and events that would turn Irena's life into ultimate nightmare.
Many people who criticise Schrader for using too much sex and nudity in his version of CAT PEOPLE probably didn't pay attention to movie's tagline "An Erotic Fantasy For The Animal In Us All". While Tourner in his 1942 version had to rely on small budget and only hint things, Schrader was able to enjoy benefits of big budget, like location shooting or special effects by Albert Whitlock, as well as liberal censorship standards of pre-AIDS era that allowed more explicit erotica in mainstream Hollywood. All that enabled Schrader to use 40-year old original in order to explore one of the most common motives in various forms of art - connection between Eros and Tanathos. This motive, which was exploited as one of the most boring cliches of "teenage slasher" genre (which was, among many things, parodied in Craven's SCREAM), was brought to its purest and most logical incarnation in CAT PEOPLE. Unlike the killings of horny teenagers in cheap 1980s horrors, committed by sexless, passionless and machine-like monstrosities who are there to please puritanical desires of the audience, homicidal violence in CAT PEOPLE comes from the same source as sex itself - dark depths of our collective unconsciousness - and it is equally irrational and primordial in its nature, and the killers are even more motivated by carnal desires than the victims. Connection between sex and violence is even underlined with the imagery that twists around all movie cliches - in most of the other films, nakedness of the characters symbolises their vulnerability; in CAT PEOPLE nakedness symbolises their "natural state", in which they are more prone to indulge into their irrational and homicidal instincts and thus become even more dangerous than with their clothes on.
Schrader's ambitions in this film were matched by his talent as a director. CAT PEOPLE is much slower than most of the other horror films, but Schrader uses this great length to slowly yet methodically build characters and atmosphere. All the scenes in this film possess certain eerie quality - from the mythical, primordial past depicted in the prologue to the exotic architecture of New Orleans. Camera of John Bailey builds such atmosphere together with the a very good musical score by Giorgio Moroder, which was very popular in early 1980s, same as David Bowie's title song. However, the screenplay by Alan Ormsby is somewhat disappointing - many lines of dialogue are weak, and characterisation is incomplete, especially in the end.
However, the acting is superb, although most of the leading talents hardly managed to repeat the same success in the decades to come. Young Nastassja Kinski, whose career in next two decades would be tragic example of missed opportunities, simply shines in her first major Hollywood role. This role could be rather thankless, because most of the viewers (especially males) would pay more attention to her willingness to expose her body in front of camera, than her actual acting abilities. But those abilities are there nevertheless, and Nastassja Kinski is great in her portrayal of young woman, torn between virginity and carnal desires, between her need of normalcy and frightening dark forces within her unconsciousness. Her partner, John Heard, who would become of the most reliable Hollywood supporting actors in 1980s and 1990s, is more than good in one of his rare leading roles. But the greatest of them all is Annette O'Toole, whose character of Yates' colleague and former girlfriend Alice allowed both her and Schrader to play with genre conventions - in the beginning, while she establishes her relationship with Irena, she plays the part of sexually active and "experienced" Bad Girl vs. young, innocent Irena; in the end, the tables are turned and Alice is next-door type who is symbol of good and normalcy against Irena as Evil Seductress. Unfortunately, Malcolm McDowell, who plays potentially most interesting part of Irena's troubled brother fell victim to typecasting, and his Paul is almost indistinguishable from psychotic villains he had played in million other films.
All in all, despite its shortcomings, CAT PEOPLE is film that hardly deserved its slide into relative obscurity. This is very good piece of filmmaking and one of the rare examples of "erotic thriller" which gives good name to that ill-reputed genre.
RATING: 8/10 (+++)
Review written on June 16th 2000
Dragan Antulov a.k.a. Drax Fido: 2:381/100 E-mail<EMAIL_ADDRESS>E-mail<EMAIL_ADDRESS>E-mail<EMAIL_ADDRESS>Filmske recenzije na hrvatskom/Movie Reviews in Croatian http://film.purger.com
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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belongs to the author.
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"Connect with us for your business growth. "
Praesent commodo cursus magna, vel scelerisque nisl consectetur.
Lorem ipsum dolor sit amet, consectetur adipiscing elit.


Computes embeddings for the given ids and weights.
tf.compat.v1.nn.embedding_lookup_sparse( params, sp_ids, sp_weights, partition_strategy='mod', name=None, combiner=None, max_norm=None )
This op assumes that there is at least one id for each row in the dense tensor represented by sp_ids (i.e. there are no rows with empty features), and that all the indices of sp_ids are in canonical row-major order.
It also assumes that all id values lie in the range [0, p0), where p0 is the sum of the size of params along dimension 0.
params
: A single tensor representing the complete embedding tensor, or a
list of P tensors all of same shape except for the first dimension,
representing sharded embedding tensors. Alternatively, a
PartitionedVariable
, created by partitioning along dimension 0. Each
element must be appropriately sized for the given partition_strategy
.sp_ids
: N x M SparseTensor
of int64 ids where N is typically batch size
and M is arbitrary.sp_weights
: either a SparseTensor
of float / double weights, or None
to
indicate all weights should be taken to be 1. If specified, sp_weights
must have exactly the same shape and indices as sp_ids
.partition_strategy
: A string specifying the partitioning strategy, relevant
if len(params) > 1
. Currently "div"
and "mod"
are supported. Default
is "mod"
. See tf.nn.embedding_lookup
for more details.name
: Optional name for the op.combiner
: A string specifying the reduction op. Currently "mean", "sqrtn"
and "sum" are supported. "sum" computes the weighted sum of the embedding
results for each row. "mean" is the weighted sum divided by the total
weight. "sqrtn" is the weighted sum divided by the square root of the sum
of the squares of the weights.max_norm
: If not None
, each embedding is clipped if its l2-norm is larger
than this value, before combining.A dense tensor representing the combined embeddings for the
sparse ids. For each row in the dense tensor represented by sp_ids
, the op
looks up the embeddings for all ids in that row, multiplies them by the
corresponding weight, and combines these embeddings as specified.
In other words, if
shape(combined params) = [p0, p1, ..., pm]
and
shape(sp_ids) = shape(sp_weights) = [d0, d1, ..., dn]
then
shape(output) = [d0, d1, ..., dn-1, p1, ..., pm]
.
For instance, if params is a 10x20 matrix, and sp_ids / sp_weights are
python
[0, 0]: id 1, weight 2.0
[0, 1]: id 3, weight 0.5
[1, 0]: id 0, weight 1.0
[2, 3]: id 1, weight 3.0
with combiner
="mean", then the output will be a 3x20 matrix where
python
output[0, :] = (params[1, :] * 2.0 + params[3, :] * 0.5) / (2.0 + 0.5)
output[1, :] = (params[0, :] * 1.0) / 1.0
output[2, :] = (params[1, :] * 3.0) / 3.0
TypeError
: If sp_ids
is not a SparseTensor
, or if sp_weights
is
neither None
nor SparseTensor
.ValueError
: If combiner
is not one of {"mean", "sqrtn", "sum"}.


Components for quasi static machine models
| Name | Description |
|---|---|
| SymmetricMultiPhaseWinding | Symmetric winding model coupling electrical and magnetic domain |
| QuasiStaticAnalogWinding | Quasi static single phase winding neglecting induced voltage |
| RotorSaliencyAirGap | Air gap model with rotor saliency |
| SymmetricMultiPhaseCageWinding | Symmetrical rotor cage |
| SaliencyCageWinding | Rotor cage with saliency in d- and q-axis |
| PermanentMagnet | Permanent magnet model without intrinsic reluctance, represented by magnetic potential difference |


Reports static code problems detected by PHPStan via PHPStan executable file
The inspection requires PHPStan to be properly installed and set up in the IDE under Settings/Preferences | Languages & Frameworks | PHP | Quality Tools | PHPStan.
It is recommended to install PHPStan either as a Composer dependency or globally.
PHPStan needs to be configured fully via phpstan.neon, also with a level so that .../phpstan analyse YOUR_FILE is working:
# phpstan.neon parameters: level: 2


Rossetti began writing down and dating her poems from 1842, mostly imitating her favoured poets. From 1847 she began experimenting with verse forms such as sonnets, hymns and ballads; drawing narratives from the Bible, folk tales and the lives of the saints. Her early pieces often feature meditations on death and loss, in the Romantic tradition. She published her first two poems ("Death's Chill Between" and "Heart's Chill Between"), which appeared in the Athenaeum, in 1848 when she was 18. Under the pen-name "Ellen Alleyne", she contributed to the literary magazine, The Germ, published by the Pre-Raphaelites from January – April 1850 and edited by her brother William. This marked the beginning of her public career.
The purpose of this site is to show different animations of the same poem. Each animation has its own funny entry to display on the webpage. Sense the name of the page is is "Poor Ghost" there is a fun light ghost icon that floats up and down on each page.


40pc revenue’s return: Sabah will continue discussion with federal govt
KOTA KINABALU, Nov 20 — The Sabah state government will continue to discuss with the federal government regarding the refund of 40 per cent of revenue from Sabah, said Chief Minister Datuk Seri Mohd Shafie Apdal.
He said the state government believed it would be the best course of action in finding a solution to the issue despite numerous calls from the state assemblymen for Sabah government to take legal action against the federal government.
“The state government believes in the consultation approach and not confrontation,” he said when winding up the debate on the 2020 Sabah state’s budget at the state legislative assembly here today.
At the same time, Mohd Shafie reassured that the state government would continue to fight for Sabah’s rights as enshrined in Malaysia Agreement 1963 and the Federal Constitution.
“I would like to stress that the state government will carry out its responsibility as entrusted by the people in the state to continue to demand our rights,” he added. — Bernama


{% extends "form.html" %}
{% block title %}Create an account{% endblock %}
{% block description %}Register with TLDRify service here{% endblock %}
{% block form %}
{% endblock %}
{% block underform %}
Why shoud I?
Registering with TLDRify is not mandatory, but introducing yourself to this service gives you an ability to track and manage the links you create.


Nehemiah 3:9
And next unto them repaired Rephaiah the son of Hur, the ruler of the half part of Jerusalem.


--- layout: post title: "p5.js moire circle" date: 2016-11-21 23:43:00 categories: experiments custom_js: - p5/p5.min.js - p5/addons/p5.dom.js ---


General FAQ
Will you invest in and/or advise our startup?
I invest in and advise startups through Y Combinator. If you want investment, the way to get it is to apply for our next round of funding. If we fund you, you can have all the advice you want (if not more).
Sorry, but I can't give advice to every startup that writes to me. It's as much as I can do to keep up with the ones we've funded.
Can I mirror an essay on my site?
I'd rather you linked to it instead. I like to keep track of which ones people read, because it gives me ideas of what to write about next. I also like to be able to fix typos.
Can I include an essay in my school newspaper?
Sure, go ahead. Please include the url of the original though.
Can I put a translation into another language online?
Sure. Please include the url of the original, and send me the url of your translation so I can make a link to it.
How long does it take you to write an essay?
Usually about two weeks. What You Can't Say took a month, partly because the topic was so dangerous. How to Start a Startup took a week, because I started it a week before I had to deliver it as a talk. Writing, Briefly was the fastest, at just over an hour.
Why did the school authorities veto the plan to have you give the talk that became "What You'll Wish You'd Known?"
They didn't have anything against me specifically, but because I wouldn't be coming from any organization, they wouldn't have any guarantee I wouldn't say dangerous things. Whereas if I came to speak from IBM or the State Department, and told the students they should all go out and try heroin, I'd be fired.
Which shows one reason public schools lose. A private school would probably have taken the risk, but the administration at a public school has to consider all the fuss parents might make in the worst-case scenario.
Are any of your paintings online?
I don't really consider myself a painter. I was trained in painting, but I haven't worked seriously at it for years. For the curious there is an image online. The painting on the cover of ANSI Common Lisp is also one of mine.
Are you the photographer Paul Graham?
No, that's a different guy.
You say in "Taste for Makers" that setting text in sans serif fonts was a mistake. So why do you use them on your site?
I was talking about books. The resolution of computer screens is much lower.
Why is the text on your site so narrow? It wastes screen space.
The aim of web design is not to use all available screen space. It is legibility. Text is most legible with no more than 70 characters per line.


Automated scraping of jobs postings on likedin, jobs descriptions analysis, and automated generation of resumes based on skills required by each job.
Check it outAnalysis of the electricity consumption of the different household appliances of elderly people and detect the abnormal behaviors to predict early symptoms of Alzheimer and dementia.
Check it outSegmentation of water bodies in images captured by the Sentinel-2 Satellite. Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
Check it outA web application for loading, cleaning, exploring, and modeling data. Equiped with multiple classification and regression algorithms and data transformations. In addition to SMOTE, stacking, and cross-validation
Check it out


Note: If you're testing this locally (off of a file in your browser) -- then geolocation will not work. Put it in your dropbox to play w these features

123, Fake St, San Francisco, CA

 Name of Place
<PHONE_NUMBER>

More examples of maps can be found here.


The Iron Giant (1999) 3 1/2 stars out of 4. Featuring the voices of Jennifer Aniston, Harry Connick Jr., Eli Marienthal, Vin Diesel and Christopher McDonald.
"The Iron Giant" is one of those movies that seem to have come out of nowhere.
It is a warm and touching animated feature with roots in the science fiction films and mores of the 1950s, the period in which this feature takes place.
This entertaining family feature is based on a book by British Poet Laureate Ted Hughes, best known as the widower of poet Sylvia Plath.
It is a simple tale of friendship, love and understanding. Set in Maine in October of 1957, the story tells of 9-year-old Hogarth who, hearing a fisherman tell how his boat was sunk by a gigantic metal man, sets out in the woods to find this wondrous robot.
As the saying goes, be careful what you wish for, it may come true. Not only does Hogarth find the 50-foot-tall iron robot, but he also saves its life. The metal-eating robot turns out to really be a gentle giant, and Hogarth decides to find a place where he can hide it so as not to frighten the local residents.
being a time of paranoia in the United States, a government agent is sent to Maine to investigate rumors of flying saucers, Soviet invaders, giant robots or whatever he can find.
This is where "The Iron Giant" succeeds brilliantly. Like the live-action "October Sky," released earlier this year, this movie captures the fear and loathing for anything un-American that existed during this era.
Whatever is not American is automatically an enemy and must be destroyed, was the thinking of the day. And this theme of intolerance is carried over into "The Iron Giant."
It is a wholesome, family-oriented feature that will please and amuse parents and children. One sequence especially will have the baby boomers in the audience smiling.
One day at school, Hogarth and his class watch a movie giving them instructions on how to "duck and cover" in case of atomic attack. If you remember those air raid drills when you had to crawl under your desk, this scene will tickle you.
"The Iron Giant" is a story of friendship, tolerance and knowing who you are - even if you are made of iron. It's a story with a message, but the two are interwoven so thoughtfully that you are not hammered over the head and feel as if the filmmakers are preaching to you.
The animation is not as sophisticated as some newer features such as Disney's "Tarzan." "The Iron Giant" has a 1950s comic book look to it, which is apropos to the story.
The movie features the voices of Jennifer Aniston as Hogarth's mother; Eli Marienthal as Hogarth; Harry Connick Jr. as his friend, Dean; Christopher McDonald as government agent Kent Mansley; and, notably, Vin Diesel as the giant.
One complaint about the movie is that it is too white. True, it is set in Maine in 1957, yet there are no people of color anywhere. Every character is white. It would not have hurt the film in any way to put some ethnic faces on some of the townspeople.
Otherwise, Brad Bird, who adapted Hughes book and directed the film, keeps the pace lively so as the younger audience members do not get bored.
Bob Bloom is the film critic at the Journal and Courier in Lafayette, IN. He can be reached by e-mail at<EMAIL_ADDRESS>or at<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Text Models
There are a number of built-in compiled text models in Analytics Zoo TFPark for Natural Language Processing (NLP) tasks based on KerasModel.
After constructing a text model, you can directly call fit, evaluate or predict in a distributed fashion. See here for more instructions.
Remarks:
- You need to install tensorflow==1.15.0 on your driver node.
- Your operating system (OS) is required to be one of the following 64-bit systems: Ubuntu 16.04 or later and macOS 10.12.6 or later.
- To run on other systems, you need to manually compile the TensorFlow source code. Instructions can be found here.
Intent Extraction
This is a multi-task model used for joint intent extraction and slot filling.
This model has two inputs:
- word indices of shape (batch, sequence_length)
- character indices of shape (batch, sequence_length, word_length)
This model has two outputs:
- intent labels of shape (batch, num_intents)
- entity tags of shape (batch, sequence_length, num_entities)
from zoo.tfpark.text.keras import IntentEntity
model = IntentEntity(num_intents, num_entities, word_vocab_size, char_vocab_size, word_length=12, word_emb_dim=100, char_emb_dim=30, char_lstm_dim=30, tagger_lstm_dim=100, dropout=0.2, optimizer=None)
num_intents
: Positive int. The number of intent classes to be classified.num_entities
: Positive int. The number of slot labels to be classified.word_vocab_size
: Positive int. The size of the word dictionary.char_vocab_size
: Positive int. The size of the character dictionary.word_length
: Positive int. The max word length in characters. Default is 12.word_emb_dim
: Positive int. The dimension of word embeddings. Default is 100.char_emb_dim
: Positive int. The dimension of character embeddings. Default is 30.char_lstm_dim
: Positive int. The hidden size of character feature Bi-LSTM layer. Default is 30.tagger_lstm_dim
: Positive int. The hidden size of tagger Bi-LSTM layers. Default is 100.dropout
: Dropout rate. Default is 0.2.optimizer
: Optimizer to train the model. If not specified, it will by default to be tf.train.AdamOptimizer().
Model Save and Load
Save the IntentEntity
model to a single HDF5 file.
model.save_model(path)
Load an existing IntentEntity
model (with weights) from HDF5 file.
from zoo.tfpark.text.keras import IntentEntity
model = IntentEntity.load_model(path)
Named Entity Recognition
This model is used for named entity recognition using Bidirectional LSTM with Conditional Random Field (CRF) sequence classifier.
This model has two inputs:
- word indices of shape (batch, sequence_length)
- character indices of shape (batch, sequence_length, word_length)
This model outputs entity tags of shape (batch, sequence_length, num_entities).
from zoo.tfpark.text.keras import NER
model = NER(num_entities, word_vocab_size, char_vocab_size, word_length=12, word_emb_dim=100, char_emb_dim=30, tagger_lstm_dim=100, dropout=0.5, crf_mode='reg', optimizer=None)
num_entities
: Positive int. The number of entity labels to be classified.word_vocab_size
: Positive int. The size of the word dictionary.char_vocab_size
: Positive int. The size of the character dictionary.word_length
: Positive int. The max word length in characters. Default is 12.word_emb_dim
: Positive int. The dimension of word embeddings. Default is 100.char_emb_dim
: Positive int. The dimension of character embeddings. Default is 30.tagger_lstm_dim
: Positive int. The hidden size of tagger Bi-LSTM layers. Default is 100.dropout
: Dropout rate. Default is 0.5.crf_mode
: String. CRF operation mode. Either 'reg' or 'pad'. Default is 'reg'. 'reg' for regular full sequence learning (all sequences have equal length). 'pad' for supplied sequence lengths (useful for padded sequences). For 'pad' mode, a third input for sequence_length (batch, 1) is needed.optimizer
: Optimizer to train the model. If not specified, it will by default to be tf.keras.optimizers.Adam(0.001, clipnorm=5.).
Model Save and Load
Save the NER
model to a single HDF5 file.
model.save_model(path)
Load an existing NER
model (with weights) from HDF5 file.
from zoo.tfpark.text.keras import NER
model = NER.load_model(path)
POS Tagging
This model is used as Part-Of-Speech(POS)-tagger and chunker for sentence tagging, which contains three Bidirectional LSTM layers.
This model can have one or two input(s):
- word indices of shape (batch, sequence_length)
- character indices of shape (batch, sequence_length, word_length) (if char_vocab_size is not None)
This model has two outputs:
- pos tags of shape (batch, sequence_length, num_pos_labels)
- chunk tags of shape (batch, sequence_length, num_chunk_labels)
from zoo.tfpark.text.keras import SequenceTagger
model = NER(num_pos_labels, num_chunk_labels, word_vocab_size, char_vocab_size=None, word_length=12, feature_size=100, dropout=0.2, classifier='softmax', optimizer=None)
num_pos_labels
: Positive int. The number of pos labels to be classified.num_chunk_labels
: Positive int. The number of chunk labels to be classified.word_vocab_size
: Positive int. The size of the word dictionary.char_vocab_size
: Positive int. The size of the character dictionary. Default is None and in this case only one input, namely word indices is expected.word_length
: Positive int. The max word length in characters. Default is 12.feature_size
: Positive int. The size of Embedding and Bi-LSTM layers. Default is 100.dropout
: Dropout rate. Default is 0.5.classifier
: String. The classification layer used for tagging chunks. Either 'softmax' or 'crf' (Conditional Random Field). Default is 'softmax'.optimizer
: Optimizer to train the model. If not specified, it will by default to be tf.train.AdamOptimizer().
Model Save and Load
Save the SequenceTagger
model to a single HDF5 file.
model.save_model(path)
Load an existing SequenceTagger
model (with weights) from HDF5 file.
from zoo.tfpark.text.keras import SequenceTagger
model = SequenceTagger.load_model(path)


Next: The ``Side Effects'' Section of a Dictionary Entry, Previous: The ``Pronunciation'' Section of a Dictionary Entry, Up: Interpreting Dictionary Entries
List of references to other parts of this standard that offer information relevant to this operator. This list is not part of the standard.


Sewell
This page contains an index of all the individuals in the database with the surname of Sewell. Selecting the person’s name will take you to that person’s individual page.
| Given Name | Birth | Death | Partner | Parents |
|---|---|---|---|---|
| Adelaide Mary | 1874 | 1887 | ||
| Ann (Annie) | 1839-11-13 | 1926-04-09 | Bunter, Robert Hyle | Sewell, Thomas Jones, Elizabeth Lord- |
| Annie R. | 1872 | Hines, Thomas A. | ||
| Edward George | 1852-04-08 | 1910-08-05 | Sewell, Thomas Jones, Elizabeth Lord- | |
| Ellen | 1842-01-17 | 1912-01-26 | Nye, Thomas | Sewell, Thomas Jones, Elizabeth Lord- |
| Frederick George | 1877 | 1950 | O'Connor, Mary J. | |
| George & Ellen (Eleanor) | ||||
| Henry B. | Marshall, Jean Eileen | |||
| Herbert W. | 1875 | 1907 | ||
| John | 1854-07-31 | Sewell, Thomas Jones, Elizabeth Lord- | ||
| Lillian Esther | 1879 | 1895 | ||
| Minnie May | 1881 | 1959 | Hodges, Herbert Frederick S. | |
| Oliver John A. | 1883 | Sewell, William Thomas MacPherson, Isabella | ||
| Rebecca | 1870 | 1907 | ||
| Ruth | 1848-12-13 | 1874-08-28 | Booth, William (SchoolTeacher) | Sewell, Thomas Jones, Elizabeth Lord- |
| Thomas | 1807-06-30 | 1895-05-21 | Jones, Elizabeth Lord- | Sewell, George & Ellen (Eleanor) |
| Thomas | 1836-07-21 | 1893-07-24 | Hayden, Mary | Sewell, Thomas Jones, Elizabeth Lord- |
| Volney | 1883 | 1971 | ||
| William Thomas | about 1858 | MacPherson, Isabella | Sewell, Thomas Hayden, Mary |


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