Appologies but cow is causing too much activity on the wolispace.com server.
I have started work on 'trimming it down' so it is even less processor intensive.
Until then you may be able to experiance the joy of cow in on of the other servers (which may be shut down at a moments notice too!)
Please choose which cowiverse you want to log in to:
wolis personal server
wolispace.com (the original cow) sometimes shut down by web host
cow.bur.st (the first second instance of cow)
nationalegend.com (a test of bluehosts capabilities)
Please note: each server is a seperate cowiverse so even though you may have registered on one, you are not automatically registered on the others (you can shout between them sometimes - when the wind is blowing in the right direction)


rotten > Library > Biographies > Religion > Televangelists > Jim Bakker
Jim Bakker
The Lord giveth and taketh away fine toupees and Rolex watches.
|
Up until 1987, televangelist Jim Bakker and his wife Tammy Faye were living in extravagance. The way they told it, prosperity was a gift from God, and He had blessed them with an army of followers and immense personal fortunes. The PTL ministry had a burn rate of $500,000 per day. Everything seemed to be going perfectly.
Jim got snagged funneling $265,000 in hush money to virginal church secretary Jessica Hahn, in order to cover up their adulterous tryst. Suddenly the Bakkers became pariahs. All of their friends deserted them.  Swaggart went on CNN and told Larry King that Jim was a "cancer in the body of Christ."
Bakker's rival and business competitor Jerry Falwell was called in to rescue the PTL ministry along with its Heritage USA theme park, but Falwell had a much different goal. Years later, he described his brief stint as head of PTL this way:
"God sent me there to bring an abrupt end to the immorality and financial fraud of this 'religious soap opera' that had become an international embarrassment to the Christian gospel."
The Bakkers and their empire had blown through $158 million of their ministry's donations. In his self-serving confession I Was Wrong, Jim admitted squandering church money on luxury cars and six mansions. When he got caught, there were 47 separate bank accounts in Jim's name.
After wasting money on every conceivable amenity, they brainstormed some truly frivolous expenditures. $100 in cinnamon buns just to imbue their hotel suite with the smell. A dog house equipped with air-conditioning, which was too noisy for the mutt to sleep in. $60,000 in gold-plated bathroom fixtures.
When they finally wound up in court, Jim had what appeared to be a psychotic break. Court-appointed psychiatrist Dr. Sally Johnson examined Bakker and testified that he was vain, passive-aggressive, and easily manipulated, but nonetheless capable of standing trial. "Mr. Bakker has sufficient strength. He is strong enough to handle the trial. He has the capacity to get through it." Dr. Johnson had previously made the same assessment of John Hinckley and would later reach the same conclusion about Ted Kaczynski.
The jury found Bakker guilty on 24 counts, and he received a 45-year sentence along with a $500,000 fine. Bakker claimed from the beginning that his downfall had been orchestrated by enemies inside and outside his ministry. He never changed his tune:
"I sorrowfully acknowledge that seven years ago ... I was wickedly manipulated by treacherous former friends and colleagues who victimized me with the aid of a female confederate. They conspired to betray me into a sexual encounter at a time of great stress in my marital life. ... I was set up as part of a scheme to co-opt me and obtain some advantage for themselves over me in connection with their hope for position in the ministry."
Yeah, yeah. Good going, Jim. Blame everyone but yourself.
Timeline
| 2 Jan 1939 |
Born as James Orsen in Muskegon, Michigan. |
| 7 Mar 1942 |
Tammy Faye Bakker born, International Falls MN. |
| 1 Apr 1961 |
Marries Tammy Faye. And it's not April Fool. |
| 1964 |
Ordained as a minister. |
| 1974 |
PTL show begins. |
| 6 Dec 1980 |
Jim Bakker fucks church secretary Jessica Hahn at the Sheraton Sand Key hotel in Clearwater, FL. His line: "When you help the shepherd, you're helping the sheep." Later she receives $265,000 in hush money. |
| 19 Mar 1987 |
Jim Bakker resigns from PTL, the television ministry he founded. He claims he was "wickedly manipulated by treacherous former friends and colleagues who victimized me with the aid of a female confederate... Vulnerable as I was at the time, I was set up as part of a scheme to co-opt me and obtain some advantage for themselves over me in connection with their hope for position in the ministry."  |
| 26 May 1987 |
Jim Bakker goes on Nightline and announces that it had been a "terrible mistake" to relinquish control of his PTL ministry to Jerry Falwell. |
| 27 May 1987 |
During a 90-minute press conference, Jerry Falwell admonishes Jim Bakker: "I have sat across the table from men who have told me of your homosexual advances." |
| 27 May 1987 |
During a joint appearance on Nightline, Tammy Faye Bakker declares: "I've been married to this man for 26 years, and I can tell you one thing: he's not homosexual, or is he bisexual. He's a wonderful, loving husband." |
| May 1987 |
Defrocked as a minister. |
| Sep 1988 |
Sullied church secretary Jessican Hahn appears in a Playboy pictorial. |
| 5 Dec 1988 |
Jim Bakker indicted for fraud and conspiracy, after embezzling $158 million from the PTL ministry. |
| Jan 1989 |
Article "The Devil in Jim Bakker: His Homosexual Lover and Pimp Tells All" appears in Penthouse. |
| 28 Aug 1989 |
Jim Bakker's trial for fraud and conspiracy begins in Charlotte, NC. |
| 5 Oct 1989 |
Jim Bakker convicted of 23 counts of fraud and one count of conspiracy for stealing donations from the PTL ministry. |
| 24 Oct 1989 |
Televangelist Jim Bakker receives 45 years and a $500,000 fine for defrauding investors of $3.7M. Bakker had already been dismissed from his PTL ministry, so the criminal penalties probably just added insult to injury. |
| 1992 |
Tammy Faye Bakker divorces Jim Bakker. |
| 1 Jul 1994 |
Jim Bakker paroled to a halfway house run by the Salvation Army in Asheville, NC. |
| 1997 |
Jessica Hahn ranked among People's 25 Most Intriguing People of 1987. (God, why...) |
| Jun 2003 |
Jim Bakker returns to the televangelism racket, in The New Jim Bakker Show. |
Pornopolis |
Rotten |
Faces of Death |
Famous Nudes
|


marf.Preprocessing.FFTFilter
Class FFTFilter
java.lang.Object
|
+--marf.Preprocessing.Preprocessing
|
+--marf.Preprocessing.FFTFilter.FFTFilter
- All Implemented Interfaces:
- StorageManager
- Direct Known Subclasses:
- BandpassFilter, HighFrequencyBoost, HighPassFilter, LowPassFilter
- public abstract class FFTFilter
- extends Preprocessing
FFTFilter class
Implements filtering using FFT algorithm
Derivatives must set frequency response based on the type of filter they are.
- Author:
- Stephen Sinclair
|
Field Summary |
protected double[] |
freqResponse
Frequency repsonse to be multiplied by the incoming value |
|
Method Summary |
boolean |
filter(double[] sample,
double[] filtered)
Perform a filter by the following algorithm:
sample -> window -> FFT -> buffer
buffer * frequency response
buffer -> IFFT -> window -> sample
Window used is square root of Hamming window, because
the sum at half-window overlap is a constant, which
avoids amplitude distortion from noise. |
boolean |
preprocess()
FFTFilter implementation of preprocess()
|
void |
setFrequencyResponse(double[] response)
Sets frequency response. |
| Methods inherited from class java.lang.Object |
, clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
freqResponse
protected double[] freqResponse
- Frequency repsonse to be multiplied by the incoming value
FFTFilter
public FFTFilter(Sample poSample)
- FFTFilter Constructor
- Parameters:
poSample
- incoming sample
preprocess
public boolean preprocess()
throws PreprocessingException
- FFTFilter implementation of
preprocess()
It does call removeNoise()
and removeSilence()
if they were explicitly
requested by an app before applying filtering.
- Overrides:
preprocess
in class Preprocessing
- Returns:
- true if there was something filtered out
- Throws:
PreprocessingException
-
setFrequencyResponse
public final void setFrequencyResponse(double[] response)
- Sets frequency response.
Derivatives must call this method in their constructors.
- Parameters:
response
- desired frequency response- Throws:
PreprocessingException
-
filter
public final boolean filter(double[] sample,
double[] filtered)
throws PreprocessingException
- Perform a filter by the following algorithm:
sample -> window -> FFT -> buffer
buffer * frequency response
buffer -> IFFT -> window -> sample
Window used is square root of Hamming window, because
the sum at half-window overlap is a constant, which
avoids amplitude distortion from noise.
Also, start sampling at -responseSize/2, in order to avoid
amplitude distortion of the first half of the first window.
"filtered" must be at least as long as "sample"
- Parameters:
sample
- incoming sample analog datafiltered
- will contain data after the filter was applied- Returns:
- true if some filtering actually happened
- Throws:
PreprocessingException
-


Reboot Button
Reboot
Test
Welcome to
Electron
app running a button to restart the remote server.
By
RYAN LI
.


--- layout: page title: Lean Prover Zulip Chat Archive permalink: archive/144837PRreviews/23171458Sylow.html ---
In this PR I see
instance additive.fintype : Π [fintype α], fintype (additive α) := id
Now I'm a complete amateur, so I'dd like to learn. Does it matter for Lean whether the [fintype α]
is left or right of the :
in this statement?
As in, is the current statement in any sense different from
instance additive.fintype [fintype α] : fintype (additive α) := by delta additive; apply_instance
I'd say no
No, the location of the colon only affects the inside of the theorem, not the uses
so writing this would usually make it harder to write the proof, but in this case it's easy
Your proof is longer :-)
{% endraw %}


|
Lab3
|
#include <PE_Types.h>
Ethernet communication statistics
| uint32_t LDD_ETH_TStats::RxIEEEAlignmentError |
Frames received with alignment error
| uint32_t LDD_ETH_TStats::RxIEEECRCError |
Frames received with CRC error
| uint32_t LDD_ETH_TStats::RxIEEEDrop |
Count of frames not counted correctly
| uint32_t LDD_ETH_TStats::RxIEEEFIFOOverflow |
Receive FIFO overflow count
| uint32_t LDD_ETH_TStats::RxIEEEFrameOK |
Frames received OK
| uint32_t LDD_ETH_TStats::RxIEEEOctetsOK |
Octet count for frames received without error
| uint32_t LDD_ETH_TStats::RxIEEEPauseFrame |
Flow control pause frames received
| uint32_t LDD_ETH_TStats::RxRMONBroadcastPackets |
Received broadcast packets
| uint32_t LDD_ETH_TStats::RxRMONCRCAlignErrors |
Received packets with CRC or alignment error
| uint32_t LDD_ETH_TStats::RxRMONDropEvents |
Count of frames not counted correctly
| uint32_t LDD_ETH_TStats::RxRMONFragments |
Received packets smaller than 64 bytes with bad CRC
| uint32_t LDD_ETH_TStats::RxRMONJabbers |
Received packets greater than max. frame length with bad CRC
| uint32_t LDD_ETH_TStats::RxRMONMulticastPackets |
Received multicast packets
| uint32_t LDD_ETH_TStats::RxRMONOctets |
Octet count for frames recieved without error
| uint32_t LDD_ETH_TStats::RxRMONOversizePackets |
Received packets greater than max. frame length with good CRC
| uint32_t LDD_ETH_TStats::RxRMONPackets |
Received packet count
| uint32_t LDD_ETH_TStats::RxRMONPackets1024To2047Octets |
Received 1024 to 2047 byte packets
| uint32_t LDD_ETH_TStats::RxRMONPackets128To255Octets |
Received 128 to 255 byte packets
| uint32_t LDD_ETH_TStats::RxRMONPackets256To511Octets |
Received 256 to 511 byte packets
| uint32_t LDD_ETH_TStats::RxRMONPackets512To1023Octets |
Received 512 to 1023 byte packets
| uint32_t LDD_ETH_TStats::RxRMONPackets64Octets |
Received 64 byte packets
| uint32_t LDD_ETH_TStats::RxRMONPackets65To127Octets |
Received 65 to 127 byte packets
| uint32_t LDD_ETH_TStats::RxRMONPacketsGreaterThan2048Octets |
Received packets greater than 2048 byte
| uint32_t LDD_ETH_TStats::RxRMONUndersizePackets |
Received packets smaller than 64 bytes with good CRC
| uint32_t LDD_ETH_TStats::TxIEEECarrierSenseError |
Frames transmitted with carrier sense error
| uint32_t LDD_ETH_TStats::TxIEEEDeferralDelay |
Frames transmitted after deferral delay
| uint32_t LDD_ETH_TStats::TxIEEEDrop |
Count of frames not counted correctly
| uint32_t LDD_ETH_TStats::TxIEEEExcessiveCollision |
Frames transmitted with excessive collisions
| uint32_t LDD_ETH_TStats::TxIEEEFIFOUnderrun |
Frames transmitted with transmit FIFO underrun
| uint32_t LDD_ETH_TStats::TxIEEEFrameOK |
Frames transmitted OK
| uint32_t LDD_ETH_TStats::TxIEEELateCollision |
Frames transmitted with late collision
| uint32_t LDD_ETH_TStats::TxIEEEMultipleCollisions |
Frames transmitted with multiple collisions
| uint32_t LDD_ETH_TStats::TxIEEEOctetsOK |
Octet count for frames transmitted without error
| uint32_t LDD_ETH_TStats::TxIEEEPauseFrame |
Flow control pause frames transmitted
| uint32_t LDD_ETH_TStats::TxIEEESingleCollision |
Frames transmitted with single collision
| uint32_t LDD_ETH_TStats::TxIEEESQEError |
Frames transmitted with SQE error
| uint32_t LDD_ETH_TStats::TxRMONBroadcastPackets |
Transmitted broadcast packets
| uint32_t LDD_ETH_TStats::TxRMONCollisions |
Transmit collision count
| uint32_t LDD_ETH_TStats::TxRMONCRCAlignErrors |
Transmitted packets with CRC or alignment error
| uint32_t LDD_ETH_TStats::TxRMONDropEvents |
Count of frames not counted correctly
| uint32_t LDD_ETH_TStats::TxRMONFragments |
Transmitted packets smaller than 64 bytes with bad CRC
| uint32_t LDD_ETH_TStats::TxRMONJabbers |
Transmitted packets greater than max. frame length with bad CRC
| uint32_t LDD_ETH_TStats::TxRMONMulticastPackets |
Transmitted multicast packets
| uint32_t LDD_ETH_TStats::TxRMONOctets |
Octet count for frames transmitted without error
| uint32_t LDD_ETH_TStats::TxRMONOversizePackets |
Transmitted packets greater than max. frame length with good CRC
| uint32_t LDD_ETH_TStats::TxRMONPackets |
Transmitted packet count
| uint32_t LDD_ETH_TStats::TxRMONPackets1024To2047Octets |
Transmitted 1024 to 2047 byte packets
| uint32_t LDD_ETH_TStats::TxRMONPackets128To255Octets |
Transmitted 128 to 255 byte packets
| uint32_t LDD_ETH_TStats::TxRMONPackets256To511Octets |
Transmitted 256 to 511 byte packets
| uint32_t LDD_ETH_TStats::TxRMONPackets512To1023Octets |
Transmitted 512 to 1023 byte packets
| uint32_t LDD_ETH_TStats::TxRMONPackets64Octets |
Transmitted 64 byte packets
| uint32_t LDD_ETH_TStats::TxRMONPackets65To127Octets |
Transmitted 65 to 127 byte packets
| uint32_t LDD_ETH_TStats::TxRMONPacketsGreaterThan2048Octets |
Transmitted packets greater than 2048 byte
| uint32_t LDD_ETH_TStats::TxRMONUndersizePackets |
Transmitted packets smaller than 64 bytes with good CRC


|
||||||||||
| PREV NEXT | FRAMES NO FRAMES |
| Packages that use ListViewBuilder | |
|---|---|
| com.jgoodies.forms.builder | Contains optional builder classes of the Forms framework. |
| Uses of ListViewBuilder in com.jgoodies.forms.builder |
|---|
| Methods in com.jgoodies.forms.builder that return ListViewBuilder | |
|---|---|
ListViewBuilder |
ListViewBuilder.border(Border border)
Sets an optional border that surrounds the list view including the label and details. |
ListViewBuilder |
ListViewBuilder.detailsView(JComponent detailsView)
Sets an optional details view that is located under the list view. |
ListViewBuilder |
ListViewBuilder.filterView(JComponent filterView)
Sets an optional view that will be placed in the upper right corner of the built list view panel. |
ListViewBuilder |
ListViewBuilder.filterViewColSpec(String colSpec)
Changes the FormLayout column specification used to lay out the filter view. |
ListViewBuilder |
ListViewBuilder.headerLabel(String markedText)
Creates a header label for the given marked text and sets it as label view. |
ListViewBuilder |
ListViewBuilder.honorVisibility(boolean b)
Specifies whether invisible components shall be taken into account by this builder for computing the layout size and setting component bounds. |
ListViewBuilder |
ListViewBuilder.label(String markedText)
Creates a plain label for the given marked text and sets it as label view. |
ListViewBuilder |
ListViewBuilder.labelView(JComponent labelView)
Sets the mandatory label view. |
ListViewBuilder |
ListViewBuilder.listBar(JComponent... buttons)
Builds a button bar using the given buttons and sets it as list bar. |
ListViewBuilder |
ListViewBuilder.listBarView(JComponent listBarView)
Sets an optional list bar - often a button bar - that will be located in the lower left corner of the list view. |
ListViewBuilder |
ListViewBuilder.listExtrasView(JComponent listExtrasView)
Sets an optional view that is located in the lower right corner of the list view, aligned with the list bar. |
ListViewBuilder |
ListViewBuilder.listView(JComponent listView)
Sets the given component as the the mandatory list view. |
ListViewBuilder |
ListViewBuilder.listViewRowSpec(String rowSpec)
Changes the FormLayout row specification used to lay out the list view. |
ListViewBuilder |
ListViewBuilder.namePrefix(String namePrefix)
Sets the prefix that is prepended to component name of components that have no name set or that are are implicitly created by this builder, e.g. the (header) label. |
|
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| PREV NEXT | FRAMES NO FRAMES |


(Really) Simple Service Discovery Protocol For .Net
What is RSSDP ?
RSSDP is a 100% .Net implementation of the Simple Service Discovery (SSDP) protocol that is part of the Universal Plug and Play (UPnP) standard. SSDP allows you to discover devices and services on a (local) network.
RSSDP is designed primarily to publish and discover custom or 'basic' devices, and as such does not implement the full UPnP device architecture. If you are looking to build a device for which a full UPnP device schema exists, this is not the library for you (sorry! though I guess you can fork and extend if you like). If you are looking for a way to discover a custom service (such as a proprietary REST or SOAP service) from a device, RSSDP might be the solution for you.
Supported Platforms
Currently;
- .Net Framework 4.03+
- Windows Phone Silverlight (8.0+)
- Xamarin.iOS Unified
- Xamarin.Android (Only tested with emulator)
- WinRT (Windows Store Apps 8.1)
- UWP 10+ (Windows 10 Universal Programs)
- .NET Core (ASP.NET Core 1.0+, .NET Standard 1.3+)
Build Status
How do I use RSSDP?
We got your samples right here
There is a sample console applicaton included in the repository. If you don't want to "read the source, Luke", then here's some tips and examples to get you started. You can also check out our FAQ and our API Reference documentation.
One common gotcha to look out for: SSDP root devices must publish an xml document describing themselves and any embedded devices, and this document must be published on a url that can be accessed via an HTTP GET. RSSDP will return devices in search results and notifications regardless of whether this document is actually accessible (it is up to you to retrieve the document if you care, and handle any exceptions that occur doing so). However, many other SSDP device locators (such as Intel's Device Spy application) will not report devices if the url cannot be accessed, the document is invalid, or data in the document (such as the UUID) does not match the associated notification or search request. For this reason, if you are using another tool to locate devices published with RSSDP, ensure you are publishing a correct document on the url specified in the Location property of your root device, or else the device may not be found.
Install the Nuget package like this;
PM> Install-Package Rssdp
Or reference the Rssdp.Portable.dll assembly AND the assembly that matches your app's platform, i.e Rssdp.NetFX40.dll for .Net 4+.
Publishing a Device
Only three steps to do this. Create a device definition, create a publisher, add the device to the publisher;
using Rssdp;
// Declare \_Publisher as a field somewhere, so it doesn't get GCed after the method finishes.
private SsdpDevicePublisher _Publisher;
// Call this method from somewhere to actually do the publish.
public void PublishDevice()
{
// As this is a sample, we are only setting the minimum required properties.
var deviceDefinition = new SsdpRootDevice()
{
CacheLifetime = TimeSpan.FromMinutes(30), //How long SSDP clients can cache this info.
Location = new Uri("http://mydevice/descriptiondocument.xml"), // Must point to the URL that serves your devices UPnP description document.
DeviceTypeNamespace = "my-namespace",
DeviceType = "MyCustomDevice",
FriendlyName = "Custom Device 1",
Manufacturer = "Me",
ModelName = "MyCustomDevice",
Uuid = GetPersistentUuid() // This must be a globally unique value that survives reboots etc. Get from storage or embedded hardware etc.
};
}
//Note, you can use deviceDefinition.ToDescriptionDocumentText() to retrieve the data to
//return from the Location end point, you just need to get that data to your service
//implementation somehow. Depends on how you've implemented your service.
_Publisher = new SsdpDevicePublisher();
_Publisher.AddDevice(deviceDefinition);
Discovering Devices
Basically, just create an SsdpDeviceLocator object and call the search method. By default the method will search for all devices, but you can specify a search target string in the following formats;
- ssdp:all
- upnp:rootdevice
- uuid:<device's unique identifier>
- urn:<fully qualified device type>
The format of a fully qualified device type is; urn:<device namespace>:device:<device type>:<device version>
i.e
- uuid:CAA42739-8F87-4463-B747-6F6DDB301A06
- urn:schemas-upnp-org:device:Basic:1
Simple Search
Simple search is easy but requires you to wait for the full search to finish before getting any results back. This is asynchronous and returns a task which you can choose to wait on (or not), but you must wait for the task to complete before accessing the results.
using Rssdp;
//Call this method from somewhere to begin the search.
public async void SearchForDevices()
{
// This code goes in a method somewhere.
using (var deviceLocator = new SsdpDeviceLocator())
{
var foundDevices = await deviceLocator.SearchAsync(); // Can pass search arguments here (device type, uuid). No arguments means all devices.
foreach (var foundDevice in foundDevices)
{
// Device data returned only contains basic device details and location ]
// of full device description.
Console.WriteLine("Found " + foundDevice.Usn + " at " + foundDevice.DescriptionLocation.ToString());
// Can retrieve the full device description easily though.
var fullDevice = await foundDevice.GetDeviceInfo();
Console.WriteLine(fullDevice.FriendlyName);
Console.WriteLine();
}
}
}
Event Driven Search & Discovery via Notifications
Event driven search is the same as 'simple' search but instead of looking at the task return value, you subscribe to the DeviceAvailable and DeviceUnavailable events to handle results. These events are raised each time a search response is received, as well as whenever a status notification is broadcast from a device. By responding to the events you can process results sooner than waiting for all results to come back from a completed task. You can also monitor for new devices arriving on the network or existing devices disappearing without having to repeatedly call search. Notifications can also be used in conjunction with simple search, just call the StartListeningForNotifications method before searching and handle the events.
using Rssdp;
// Define _DeviceLocator as a field so it doesn't get GCed after the method ends, and it can
// continue to listen for notifications until it is explicitly stopped
// (with a call to _DeviceLocator.StopListeningForNotifications();)
private SsdpDeviceLocator _DeviceLocator;
// Call this method from somewhere in your code to start the search.
public void BeginSearch()
{
_DeviceLocator = new SsdpDeviceLocator();
// (Optional) Set the filter so we only see notifications for devices we care about
// (can be any search target value i.e device type, uuid value etc - any value that appears in the
// DiscoverdSsdpDevice.NotificationType property or that is used with the searchTarget parameter of the Search method).
_DeviceLocator.NotificationFilter = "upnp:rootdevice";
// Connect our event handler so we process devices as they are found
_DeviceLocator.DeviceAvailable += deviceLocator_DeviceAvailable;
// Enable listening for notifications (optional)
_DeviceLocator.StartListeningForNotifications();
// Perform a search so we don't have to wait for devices to broadcast notifications
// again to get any results right away (notifications are broadcast periodically).
_DeviceLocator.SearchAsync();
Console.ReadLine();
}
// Process each found device in the event handler
async static void deviceLocator_DeviceAvailable(object sender, DeviceAvailableEventArgs e)
{
//Device data returned only contains basic device details and location of full device description.
Console.WriteLine("Found " + e.DiscoveredDevice.Usn + " at " + e.DiscoveredDevice.DescriptionLocation.ToString());
//Can retrieve the full device description easily though.
var fullDevice = await e.DiscoveredDevice.GetDeviceInfo();
Console.WriteLine(fullDevice.FriendlyName);
Console.WriteLine();
}
IPV6 support
var devicePublisher = new SsdpDevicePublisher(IPAddress.IPv6Any.ToString());
var devicePublisher = new SsdpDevicePublisher("fe80::dc06:c198:7078:afdd");
var deviceLocator = new SsdpDeviceLocator("fe80::dc06:c198:7078:afdd");
For projects: IPv6 is not supported on WindowsPhoneSL or WinRT (but is for UWP).
Why RSSDP?
Aren't there already lots of SSDP implementations?
I needed to find a custom/proprietary service on local networks from a mobile device. I decided this had been done before and I shouldn't re-invent the wheel so I started looking for existing, standard protocols that did this. I decided Zeroconf and SSDP seemed like the best two, and Zeroconf looked like the more efficient, less overhead option. Unfortunately I also need a solution where
- I could publish a device. Many other libraries only focus on discovery.
- the publish component runs on (at least) .Net 4.0, without relying on any external services. Many other implementations are just wrappers around a Windows or Linux service, which I couldn't guarantee would be installed/enabled etc.
- the discovery component (at least) runs on Windows Phone and Xamarin.iOS. Preferably also .Net 4.0, Xamain.Android, WinRT and Compact Framework projects. A lot of other implementations don't support the Xamarin platforms.
- the API was consistent across platforms so I can write as little code with as little conditional compilation as possible (especially in Xamarin Forms projects).
- the library wasn't massive and didn't have huge numbers of dependencies, I want to keep my deployment footprint as small as possible.
- the library guided me (at least a little) towards publishing devices correctly, i.e correct device types, not leaving out required fields etc.
Sadly, I couldn't find a .Net implementation that met the criteria (I found some Node.Js and implementations in other languages that might have worked, but not in my environment/with my tools). Maybe I didn't look hard enough but that's where I ended up. Having failed on Zeroconf I went looking for SSDP implementations that met the same goals, and had exactly the same problem. I then looked at implementing each protocol and while Zeroconf looked better overall, it seemed less well documented and harder to implement. At the point where I decided this wheel needed reinventing** I chose SSDP.
** Have you ever thought about how many different, useful, kinds of wheel there are in the world? Train wheels won't work on a bicycle, and bicycle wheels won't work on a car etc. Often people who say don't re-invent the wheel haven't really considered how many variations of a wheel might be needed.
References
Reference materials used while writing this library, or that may be useful to people working with RSSDP who are not familiar with SSDP/UPnP device types and protocols.
Contributing
We'd love your assistance! Please see the CONTRIBUTING.md file for information on how to contribute.


Robust Linear Models¶
Robust linear models with support for the M-estimators listed under Norms.
See Module Reference for commands and arguments.
Examples¶
# Load modules and data
In [1]: import statsmodels.api as sm
In [2]: data = sm.datasets.stackloss.load()
In [3]: data.exog = sm.add_constant(data.exog)
# Fit model and print summary
In [4]: rlm_model = sm.RLM(data.endog, data.exog, M=sm.robust.norms.HuberT())
In [5]: rlm_results = rlm_model.fit()
In [6]: print(rlm_results.params)
[-41.0265 0.8294 0.9261 -0.1278]
Detailed examples can be found here:
Technical Documentation¶
References¶
- PJ Huber. ‘Robust Statistics’ John Wiley and Sons, Inc., New York. 1981.
- PJ Huber. 1973, ‘The 1972 Wald Memorial Lectures: Robust Regression: Asymptotics, Conjectures, and Monte Carlo.’ The Annals of Statistics, 1.5, 799-821.
- R Venables, B Ripley. ‘Modern Applied Statistics in S’ Springer, New York,
Module Reference¶
Model Results¶
RLMResults (model, params, ...) |
Class to contain RLM results |
Norms¶
AndrewWave ([a]) |
Andrew’s wave for M estimation. |
Hampel ([a, b, c]) |
Hampel function for M-estimation. |
HuberT ([t]) |
Huber’s T for M estimation. |
LeastSquares |
Least squares rho for M-estimation and its derived functions. |
RamsayE ([a]) |
Ramsay’s Ea for M estimation. |
RobustNorm |
The parent class for the norms used for robust regression. |
TrimmedMean ([c]) |
Trimmed mean function for M-estimation. |
TukeyBiweight ([c]) |
Tukey’s biweight function for M-estimation. |
estimate_location (a, scale[, norm, axis, ...]) |
M-estimator of location using self.norm and a current estimator of scale. |
Scale¶
Huber ([c, tol, maxiter, norm]) |
Huber’s proposal 2 for estimating location and scale jointly. |
HuberScale ([d, tol, maxiter]) |
Huber’s scaling for fitting robust linear models. |
mad (a[, c, axis, center]) |
The Median Absolute Deviation along given axis of an array |
hubers_scale |
Huber’s scaling for fitting robust linear models. |


Switch to Freeform?
After switching to Freeform, you won't be able to switch back. You also won't be able to export your lyrics, you'll only be able to copy/paste them as plaintext. You'll still get access to rhyming, however. Are you sure you want to switch to Freeform?


GStreamer is a fast and flexible video and image processing pipeline utility with a wide array of platform support. A Greengrass component based on GStreamer will provide the backbone of acquisition and can be easily configured to use different sources including mock or test sources.
Refer to the AWS IoT Greengrass V2 Component using Docker to run GStreamer to grab frames from an RTSP Stream project to build and deploy the component.
Prerequisites of Target Device
sudo docker run hello-world
ggc_user
to the docker groupsudo usermod -aG docker ggc_user
Follow the directions and return here after creating the component in your AWS Account.
Note It is possible to construct the docker image on your development host, but it is recommended to perform the Build and Deployment on the target device as this facilitates testing. Additionally, it may be desired to setup a RAM Disk for shared image storage, which must be done on the target device, along with the setting of user and group permissions.
If you do not have an RTSP Source to test with, you can the fakesrc
or multifilesrc
options described in the README.
Be sure to Deploy the component to your Greengrass core.
The default configuration of the component uses the GStreamer plugin fakesrc and delivers to a file of 0 bytes to /tmp/data/frame.jpg
. To change the output location or the source (using an RTSP or file source for example), change the deployment configuration for the component.
On the target device either over ssh
or on the local head:
export gg_home=/greengrass/v2
# or wherever you installed greengrass
# inspect installed components
sudo $gg_home/bin/greengrass-cli component list
# stop the frame grabber -- modify if you used a different component name
sudo $gg_home/bin/greengrass-cli component stop --names=com.example.gst-grabber
If you previously deployed the Sample Data component, frames will be installed in $gg_home//packages/artifacts-unarchived/com.example.sample_data/1.0.0/com.example.sample_data/frames
. If you are using some other source for sequentially numbered frames, set that next:
docker run -v /tmp/data:/data -v $frame_home:/frames -it --entrypoint /bin/bash gst
# this will open an interactive shell
# then start the test pipeline with
gst-launch-1.0 multifilesrc location="/frames/seq_%06d.jpg" index=1 loop=true caps="image/png,framerate=\(fraction\)12/1" ! multifilesink location="/data/frame.jpg"
export gcmd='multifilesrc location="/frames/seq_%06d.jpg" index=1 loop=true caps="image/png,framerate=\(fraction\)12/1" ! multifilesink location="/data/frame.jpg"'
docker run -v /greengrass/v2/packages/artifacts-unarchived/com.example.sample_data/1.0.0/com.example.sample_data/frames:/frames -v /tmp/data:/data gst $gcmd
docker run -v /greengrass/v2/packages/artifacts-unarchived/com.example.sample_data/1.0.0/com.example.sample_data/frames:/frames -v /tmp/data:/data --entrypoint gst-inspect-1.0 gst $gcmd
This will leave the pipeline running and cycling through the sample frames until you type Control-C
. It is also possible to change the entrypoint in the Dockerfile or override this in the component recipe through configuration variables. However, given that this is test data, it may be more useful to setup and tear down this pipeline manually.


This is the long story. Feel free to navigate yourself around using the bullets on the side.
2017-today I do this stuff. I love it.
2015-2017
Harmonic enables customers to produce and deliver high quality video. The delivery side, of which I was part,
involved moving lots of bits from one place to another at incredible speeds and accuracy. Impressive stuff,
done by some impressively smart people, but not really my cup of tea.
There was a period of several euphoric months, though, in which I was in a project that involved Software Design,
Web, Java and UI. This was absolutely heaven, I woke up every morning with a smile and couldn't wait to go
to work. I loved stirring the DOM and figuring out how it all worked.
Then it hit me. This is what I'd really like my life to look like.
This project was short lived, and with it my euphoria.
Which is good. Because here I am, doing exactly what I love.
2013-2015
Waves Audio is a music company. This automatically gives it special status in my book, as many of the people it
naturally draws to it are Musicians, a sort of subcategory of Magicians. Some of my best friends come from Waves.
I worked in the Plugins team, connecting algorithms with UI to create units which would plug into Digital Audio
Workstations. Lots of DAWs. Backward compatible to lots of versions. On various OSs. Backward compatible to lots of
versions of them. (Oh, so many variations.)
2007-2013
Biosense-Webster is an awesome company that is a leader in diagnosis and treatment of heart rhythm disorders. It
employs some of the most interesting and talented people I know, many of which I keep as friends.
As a Software Engineer I mostly did, well, surprise, hardcore software engineering. The internals. Designing and
implementing Object Oriented features and modules. Moving large data around in real time. Syncing algorithms.
Had the pleasure of actually seeing my work curing people in hospitals, which added the spices of mission and
great excitement.
Fascinating work which mostly involved the left part of my brain, and which I heartily enjoyed.
2002-2007
I spent five years at Tel Aviv University.
The first two years were spent doing complementary courses to an Engineering degree, while simultenously
raising a baby (both were successful).
The next two were spent doing the actual MSc: courses, research about deep-tissue stresses in people confined
to wheelchairs, writing a scientific article about it.
The last year was spent at my supervisor's lab, writing some more articles, at his generous invitation.
As it turns out, I have a fascination for the subject. And I'm pretty good at writing articles.
This was a period of fulfillment and discovery. The human body and its physiology was like a great adventure.
The Biomechanical field, into which I stepped almost by accident, turned out to be exciting and, surprisingly,
full of potential for Software Engineers like me.
Graduated with honors.
2000-2001
On my last year at BIU, I answered a friend's request and came to work at Gilian (which has since
then changed its name to Breach Security and then to Trustwave). The goal of this company is to fight
crime. Cybercrime. Hackers.
As I was bringing with me experience as a Graphic Designer, I was assigned to the GUI team and worked
on the Client side.
1998-2001 Studying at Bar Ilan University was great. My final project was a GUI system for a voice recognition program, using Java and a DB. Graduated with honors.
1997-2000
There was Internet before 2000...and I was there.
This was when HTML was only 3, CSS wasn't even born yet and JavaScript was nothing less than
a natural disaster. Yet, we made the Web beautiful and practical, even in those limited conditions.
Yellow Pages was good at selling, and it was leveraging this advantage to open a new department
for Websites, customized for each client.
I joined the team as a Graphic Designer and quickly found, to my delight, that many tools and techniques
were required for creating a good website. Writing scripts was challenging and fun, and I found myself
spending much time with the Backend guys, learning what they were doing. And then I opened a C++ book,
just out of curiosity, and a whole new world opened up for me.
On my horizon perched Software Engineering.
1996-1997
When I returned from India, a friend of mine invited me to his office.
Have you heard about Internet? he asked.
Well... vaguely...
And so he introduced me to the Web, and recommended it as a career for me. This sounds absurd today,
everybody knows about the Web. But back then it was newly discovered as a means for marketing.
With no experience whatsoever I landed a job, and then went on to Aquanet where I was totally on my own,
to learn everything by myself. New software. Design for the web. UX. The whole works.
This was a new world just waiting for me to explore it. And I did.
1995-1996
There is a difference between Touring and Traveling. Maybe it lies in the expectations one develops before
going somewhere. When you Tour, you take with you a list of expectations. When you Travel, your major
expectation is to discover something you didn't know. To be surprised.
I much prefer the latter, but it requires time.
So I bought myself a one way ticket for India and set myself to learn something I didn't know. Including
how to get along all by myself. I travelled for six months in India (and then, as a major surprise, two more
in the Philippines). Met all sorts of people. Been all sorts of places. Done all sorts of things. Surprised
myself again and again and again. I have India to thank for a significant part of my personality.
1992-1995
Carta is a publisher of books and maps.
I worked with authors, editors and translators. Some had ideas of what their book should look like. Some
didn't. I was responsible for conception and design of their ideas. If they had them. Otherwise, I'd bring the
ideas, too. Illustrations. Print.  And this has to be it. I came with great expectations and high hopes to Bezalel, Academy of Arts and Design. And was deeply disappointed. In Bezalel, in the people there, in myself, and in life in general. That's not to say Bezalel is not a great place. But it wasn't the right place and time for me.
1986-1988
Have you really read up to here or are you just skipping to the end?
IDF was a period of magic. Worked in shifts, translating English to Hebrew in real time, and did a great job
at having fun in Tel Aviv the rest of the time.


public interface RawSqlBuilder
Note that RawSql can also be defined in ebean-orm.xml files and be used as a named query.
RawSql
| Modifier and Type | Method and Description |
|---|---|
RawSqlBuilder |
columnMapping(String dbColumn,
String propertyName)
Set the mapping of a DB Column to a bean property.
|
RawSqlBuilder |
columnMappingIgnore(String dbColumn)
Ignore this DB column.
|
RawSql |
create()
Create the immutable RawSql object.
|
static RawSqlBuilder |
parse(String sql)
Return a RawSqlBuilder parsing the sql.
|
static RawSql |
resultSet(ResultSet resultSet,
String... propertyNames)
Create and return a RawSql object based on the resultSet and list of properties the columns in
the resultSet map to.
|
static SqlRow |
sqlRow(ResultSet resultSet,
String dbTrueValue,
boolean binaryOptimizedUUID)
Create and return a SqlRow based on the resultSet with dbTrueValue and binaryOptimizedUUID options.
|
RawSqlBuilder |
tableAliasMapping(String tableAlias,
String path)
Modify any column mappings with the given table alias to have the path prefix.
|
static RawSqlBuilder |
unparsed(String sql)
Return an unparsed RawSqlBuilder.
|
static RawSql resultSet(ResultSet resultSet, String... propertyNames)
The properties listed in the propertyNames must be in the same order as the columns in the resultSet.
static SqlRow sqlRow(ResultSet resultSet, String dbTrueValue, boolean binaryOptimizedUUID) throws SQLException
SQLException
static RawSqlBuilder unparsed(String sql)
static RawSqlBuilder parse(String sql)
The sql statement will be parsed so that Ebean can determine where it can insert additional WHERE or HAVING expressions.
Additionally the selected columns are parsed to determine the column ordering. This also means additional checks can be made with the column mapping - specifically we can check that all columns are mapped and that correct column names are entered into the mapping.
RawSqlBuilder columnMapping(String dbColumn, String propertyName)
For Unparsed SQL the columnMapping MUST be defined in the same order that the columns appear in the SQL statement.
dbColumn
- the DB column that we are mapping to a bean propertypropertyName
- the bean property that we are mapping the DB column to.RawSqlBuilder columnMappingIgnore(String dbColumn)
RawSqlBuilder tableAliasMapping(String tableAlias, String path)
For example modify all mappings with table alias "c" to have the path prefix "customer".
For the "Root type" you don't need to specify a tableAliasMapping.
Copyright © 2019. All rights reserved.


morsel
morsel is a library for creating streaming audio/video assets.
It can produce a series of fragmented mp4 files with HLS playlists.
It is compatible with Linux, macOS, iOS, and tvOS.
Supported playlist types:
- VOD (Video On Demand) Basic
- Event Basic
- Live (Sliding Window)
Uses
morsel can be used to produce audio and video assets suitable for streaming over the web using HLS (HTTP Live Streaming)
morsel can be used on a Linux server in the cloud using pupil to capture the audio/video over TCP.
It can also be used in an iOS / iPad app and capture data directly from the device’s camera / microphone.
It can also be used on your macOS Desktop.


--- ---
The Slide Show (S9) Ruby gem turns your slides in plain text into a web page that’s an all-in-one-page handout and a live slide show all at once.
$ slideshow build tutorial.txt => Preparing slideshow 'tutorial.html'... => Done.
Turn your web page into a slide show and your browser into full screen projection with a single push button (F11). Hit the space bar or the right arrow, down arrow or page down key to flip through your slides.
That’s all. It’s that simple.


Enumerable map is a great way to go through arrays or hashs. It has amazing short hand ways to iterate through them. With this short hand stuff you have to balance how easy the code is to read and how short it is. You do not want to scaurfice readability just to make the code look cool.
You can use this tool to help debug some code. It is a quick way to iterate through an array. Lets say you have an array and they all should be true. You can go into the code and add lambda syntax to check for true. "p array.map(&:odd?)". This will print out everything in the array for you to check. A lot faster then setting up a for each loop.
So here are the basic set up for non lambda.
arry.map { |num| num.to_i}
1: Calling the object. In this case an array.
2: Calling the .map on the array.
3: Open { starts what is like a for each loop.
4: |num| assigns each iteration to a variable name.
5: num.to_i is the where the heavy lifting happens. It is the action that is taken on each item in the array.These actions can be basic or advance.
6: Ends the "for each loop"


A variety of ordination and community analyses useful in analysis of data sets in community ecology. Includes many of the common ordination methods, with graphical routines to facilitate their interpretation, as well as several novel analyses.
| Version: | 1.8-0 |
| Depends: | R (≥ 2.10), mgcv, MASS, cluster |
| Suggests: | optpart |
| Published: | 2016-01-24 |
| Author: | David W. Roberts |
| Maintainer: | David W. Roberts <droberts at montana.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | http://ecology.msu.montana.edu/labdsv/R |
| NeedsCompilation: | yes |
| Materials: | ChangeLog |
| In views: | Environmetrics, Psychometrics |
| CRAN checks: | labdsv results |
| Reference manual: | labdsv.pdf |
| Package source: | labdsv_1.8-0.tar.gz |
| Windows binaries: | r-devel: labdsv_1.8-0.zip, r-release: labdsv_1.8-0.zip, r-oldrel: labdsv_1.8-0.zip |
| OS X binaries: | r-release: labdsv_1.8-0.tgz, r-oldrel: labdsv_1.8-0.tgz |
| Old sources: | labdsv archive |
| Reverse depends: | dave, fso, optpart |
| Reverse imports: | RAM |
| Reverse suggests: | vegdata |
Please use the canonical form https://CRAN.R-project.org/package=labdsv to link to this page.


>
NodeJS Learning Path
Basic
Intermediate
Advanced
NodeJS Training (BASIC)
This is the NodeJS Training learning path for beginner developers.
NodeJS
1) Introduction to NodeJS
NodeJS vs Javascript
Blocking and Non-Blocking
Introduction to concepts: Event Loop, Callbacks, Basic Routing and First Web Server
NodeJS as Web Server
2) Setting Environment
What is NVM?
What is NPM?
3) Core Modules
fs
HTTP
4) Web Frameworks
ExpressJS
ExpressJS Routing
PUG Templeate Engine
5) Asynchronous Programming
Promises, Async and Callbacks


Woot-woot, I have competed the last round of Flexbox Zombies! The last chapter was actually easier than I thought.


Preparation time
30 mins to 1 hour
Cooking time
over 2 hours
Serves
Serves 100
Dietary
Make your own wedding cake with our step-by-step recipe.
Equipment and preparation: For this recipe you will need a 15cm/6in, 23cm/9in and 30cm/12in round cake tin, thin cake boards of respective sizes, and 38cm/15in, 35cm/14in and 23cm/9in thick cake boards. You will also need eight dowelling rods and eight cake pillars and sugar paste flower moulds. All of these are available from specialist cake shops. Plus you will need a 3m/9ft 10in x 1.5cm/⅝in white satin ribbon.
For the sugar flowers, knead the yellow sugar paste until soft and pliable and roll it out until it is 5mm/¼in thick. Press the sugar paste into the flower moulds, then remove and set aside to dry. Repeat with the white sugar paste. (The flowers can be made up to a month in advance and stored in an airtight container.)
Preheat the oven to 150C/300F/Gas 2. Grease and line the 15cm/6in, 23cm/9in and 30cm/12in round cake tin.
Place a wide strip of brown paper around the outside of the cake tin and tie with string.
For the 30cm/12in tier, beat the butter and sugars until light and fluffy. (This takes about five minutes.) Add the eggs, a little at a time, until well combined. Stir in the dry ingredients, spices, zest, dried fruits, nuts (if using) and brandy. Spoon the mixture into the prepared tin and smooth the top with a palette knife.
Bake in the oven for four hours 10 minutes, or until a skewer inserted into the middle comes out clean.
Repeat steps 3 and 4 for the 23cm/9in tier and bake in the oven for three hours 20 minutes, or until a skewer inserted into the middle comes out clean.
Repeat steps 3 and 4 for the 15cm/6in tier and bake in the oven for 1 hour 45 minutes, or until a skewer inserted into the middle comes out clean.
When the cakes are cooked, remove them from the oven and set aside to cool for 15 minutes. Remove them from the tin and set aside to cool completely.
To ice the cakes, warm the apricot jam in a saucepan, then pass it through a sieve.
Level the top of the 15cm/6in fruit cake by cutting the top off with a serrated knife, paint the top with a little apricot jam, then turn it upside down onto the 15cm/6in thin cake board. Using a piece of string, measure the top and sides of the fruit cake (this is so that you roll the marzipan out to the right size). Paint the fruit cake all over with the apricot jam.
Dust a work surface with icing sugar and roll 500g/1lb 2oz of the marzipan out until it is large enough to cover the top and sides of the fruit cake (use the piece of string as a guide). Carefully lift the marzipan onto the cake and smooth it on with your hands. Trim off any excess marzipan, ensuring the cake board is also covered.
Paint the cake with cooled, boiled water. Knead 500g/1lb 2oz of the white icing until soft and pliable and roll it out until it is 5mm/¼in thick and large enough to cover the top and sides of the fruit cake (use the piece of string as a guide). Carefully lift the icing onto the cake and smooth it on with your hands. Trim off any excess icing.
Repeat steps 10, 11 and 12 for the 23cm/9in cake using 1kg/2lb 4oz each of marzipan and icing.
Repeat steps 10, 11 and 12 for the 30cm/12in cake using 2kg/4lb 8oz each of marzipan and icing. Leave the cakes overnight so that the icing can dry (do not store the cakes in the fridge).
Once the icing has dried, apply a thin line of edible glue around the bottom edge of each cake and carefully stick some ribbon around the cake, securing the join with a little extra glue.
To insert the dowelling rods, hold a dowelling rod at the side of the 23cm/9in cake and mark with a pencil where the icing comes to. Cut the rod and three others to the same length. Push the rods into the cake about 5cm/2in away from the sides, to form the four corners of a square in the centre of the cake. Measure, cut and insert the rods in the same way for the 30cm/12in cake.
 Spread a little glue over the board, then carefully lift the icing onto the cake board, smoothing it until flat. Trim the edges. Apply a thin line of glue around the edge of the cake board and carefully stick the ribbon around the cake, tying it in a bow at the front.
If you’re transporting the cakes to a venue, it’s best to transport them separately and assemble the cake at the venue. To assemble the cakes, first stick the 30cm/12in cake to the icing-covered 38cm/15in cake board: add a little glue to the middle of the 35cm/14in cake board, then position the 30cm/12in cake in the middle. Place four of the pillars on top of the largest cake, positioning each one over a dowelling rod. Top with the 23cm/9in cake, then position the remaining four pillars on top and finish with the 15cm/6in cake. Using the edible glue, arrange the sugar flowers on the top of the cake and around the side of the 23cm/9in and 30cm/12in cake.
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How to assemble a tiered wedding cake
You need JavaScript to view this clip.
How to marzipan a fruit cake


--- layout: page --- {% comment %} This layout file is used as a base for a gallery page. Usage: # works: # - title: Nice images # images: # - /image_one # - /image_two # ... # - title: More images # images: # - /another/image # ... # ... {% endcomment %}


{{ $featured := (.Resources.ByType "image").GetMatch "*featured*" }} {{ with $featured }} {{ $image := .Fill "1600x400" }}


Training Neural Networks,
Part 2
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 1 25 Jan 2016
A2 is out. It’s meaty. It’s due Feb 5 (next Friday)
You’ll implement:
Neural Nets (with Layer Forward/Backward API)
Batch Norm
Dropout
ConvNets
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 2 25 Jan 2016
Loop:
Sample a batch of data
Forward prop it through the graph, get loss
Backprop to calculate the gradients
Update the parameters using the gradient
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 3 25 Jan 2016
| Activation Functions |
|
|
|
||
|
Maxout
tanh tanh(x)
ELU
ReLU max(0,x)
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 4 25 Jan 2016
Preprocessing
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 5 25 Jan 2016
Reasonable initialization. (Mathematical derivation assumes linear activations)
Weight Initialization
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 6 25 Jan 2016
Batch Normalization
Normalize:
And then allow the network to squash the range if it wants to:
[Ioffe and Szegedy, 2015]
Improves gradient flow through the network
Allows higher learning rates
Reduces the strong dependence on initialization
Acts as a form of regularization in a funny way, and slightly reduces the need for dropout, maybe
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 7 25 Jan 2016
| Babysitting the |
|
||||||
| learning process | |||||||
Loss barely changing:
Learning rate is probably
too low
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 8 25 Jan 2016
Parameter update schemes
Learning rate schedules
Dropout
Gradient checking
Model ensembles
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 9 25 Jan 2016
Parameter Updates
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 10 25 Jan 2016
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 11 25 Jan 2016
simple gradient descent update
now: complicate.
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 12 25 Jan 2016
Image credits: Alec Radford
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 13 25 Jan 2016
What is the trajectory along which we converge towards the minimum with SGD?
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 14 25 Jan 2016
What is the trajectory along which we converge towards the minimum with SGD?
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 15 25 Jan 2016
What is the trajectory along which we converge towards the minimum with SGD? very slow progress along flat direction, jitter along steep one
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 16 25 Jan 2016
Physical interpretation as ball rolling down the loss function + friction (mu coefficient).
mu = usually ~0.5, 0.9, or 0.99 (Sometimes annealed over time, e.g. from 0.5 -> 0.99)
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 17 25 Jan 2016
Allows a velocity to “build up” along shallow directions
Velocity becomes damped in steep direction due to quickly changing sign
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 18 25 Jan 2016
vs
Momentum
notice momentum overshooting the target, but overall getting to the minimum much faster.
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 19 25 Jan 2016
Ordinary momentum update:
momentum
step
actual step
gradient
step
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 20 25 Jan 2016
Momentum update
momentum
step
actual step
gradient
step
Nesterov momentum update
|
||
| momentum |
|
|
|
||
| step | ||
|
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 21 25 Jan 2016
Momentum update
momentum
step
actual step
gradient
step
Nesterov momentum update
|
||
| momentum |
|
|
|
||
| step | ||
|
Nesterov: the only difference...
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 22 25 Jan 2016
Slightly inconvenient… usually we have :
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 23 25 Jan 2016
Slightly inconvenient… usually we have :
Variable transform and rearranging saves the day:
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 24 25 Jan 2016
Slightly inconvenient… usually we have :
Variable transform and rearranging saves the day:
Replace all thetas with phis, rearrange and obtain:
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 25 25 Jan 2016
nag =
Nesterov
Accelerated
Gradient
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 26 25 Jan 2016
AdaGrad update
[Duchi et al., 2011]
Added element-wise scaling of the gradient based on the historical sum of squares in each dimension
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 27 25 Jan 2016
Q: What happens with AdaGrad?
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 28 25 Jan 2016
Q2: What happens to the step size over long time?
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 29 25 Jan 2016
RMSProp update
[Tieleman and Hinton, 2012]
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 30 25 Jan 2016
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 31 25 Jan 2016
Cited by several papers as:
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 32 25 Jan 2016
adagrad
rmsprop
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 33 25 Jan 2016
Adam update
[Kingma and Ba, 2014]
(incomplete, but close)
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 34 25 Jan 2016
| Adam update |
|
|
| (incomplete, but close) |
momentum
RMSProp-like
Looks a bit like RMSProp with momentum
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 35 25 Jan 2016
| Adam update |
|
|
| (incomplete, but close) |
momentum
RMSProp-like
Looks a bit like RMSProp with momentum
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 36 25 Jan 2016
Adam update
[Kingma and Ba, 2014]
momentum
bias correction
(only relevant in first few iterations when t is small)
RMSProp-like
The bias correction compensates for the fact that m,v are initialized at zero and need some time to “warm up”.
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 37 25 Jan 2016
learning rate as a hyperparameter.
Q: Which one of these learning rates is best to use?
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 38 25 Jan 2016
learning rate as a hyperparameter.
=> Learning rate decay over time!
step decay:
e.g. decay learning rate by half every few epochs.
exponential decay:
1/t decay:
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 39 25 Jan 2016
second-order Taylor expansion:
Solving for the critical point we obtain the Newton parameter update:
Q: what is nice about this update?
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 40 25 Jan 2016
second-order Taylor expansion:
Solving for the critical point we obtain the Newton parameter update:
notice:
no hyperparameters! (e.g. learning rate)
Q2: why is this impractical for training Deep Neural Nets?
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 41 25 Jan 2016
Quasi-Newton methods (BGFS most popular): instead of inverting the Hessian (O(n^3)), approximate inverse Hessian with rank 1 updates over time (O(n^2) each).
L-BFGS (Limited memory BFGS):
Does not form/store the full inverse Hessian.
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 42 25 Jan 2016
Usually works very well in full batch, deterministic mode i.e. if you have a single, deterministic f(x) then L-BFGS will probably work very nicely
Does not transfer very well to mini-batch setting. Gives bad results. Adapting L-BFGS to large-scale, stochastic setting is an active area of research.
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 43 25 Jan 2016
Adam is a good default choice in most cases
If you can afford to do full batch updates then try out
L-BFGS (and don’t forget to disable all sources of noise)
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 44 25 Jan 2016
Evaluation:
Model Ensembles
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 45 25 Jan 2016
Train multiple independent models
At test time average their results
Enjoy 2% extra performance
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 46 25 Jan 2016
can also get a small boost from averaging multiple model checkpoints of a single model.
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 47 25 Jan 2016
can also get a small boost from averaging multiple model checkpoints of a single model.
keep track of (and use at test time) a running average parameter vector:
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 48 25 Jan 2016
Regularization (dropout)
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 49 25 Jan 2016
Dropout
“randomly set some neurons to zero in the forward pass”
[Srivastava et al., 2014]
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 50 25 Jan 2016
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 51 25 Jan 2016
How could this possibly be a good idea?
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 52 25 Jan 2016
How could this possibly be a good idea?
Forces the network to have a redundant representation.
has an ear
has a tail
is furry
has claws
mischievous
look
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6
X
cat
score
X
53 25 Jan 2016
How could this possibly be a good idea?
Another interpretation:
Dropout is training a large ensemble of models (that share parameters).
Each binary mask is one model, gets trained on only ~one datapoint.
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 54 25 Jan 2016
Ideally:
want to integrate out all the noise
Monte Carlo approximation: do many forward passes with different dropout masks, average all predictions
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 55 25 Jan 2016
Can in fact do this with a single forward pass! (approximately) Leave all input neurons turned on (no dropout).
(this can be shown to be an
approximation to evaluating the
whole ensemble)
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 56 25 Jan 2016
Can in fact do this with a single forward pass! (approximately) Leave all input neurons turned on (no dropout).
Q: Suppose that with all inputs present at test time the output of this neuron is x.
What would its output be during training time, in expectation? (e.g. if p = 0.5)
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 57 25 Jan 2016
Can in fact do this with a single forward pass! (approximately)
a
w0 w1
x y
Leave all input neurons turned on (no dropout).
during test: a = w0*x + w1*y during train:
E[a] = ¼ * (w0*0 + w1*0
w0*0 + w1*y
¼ * (2 w0*x + 2 w1*y)
½ * (w0*x + w1*y)
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 58 25 Jan 2016
Can in fact do this with a single forward pass! (approximately) Leave all input neurons turned on (no dropout).
a
w0 w1
x y
during test: a = w0*x + w1*y during train:
E[a] = ¼ * (w0*0 + w1*0 w0*0 + w1*y
¼ * (2 w0*x + 2 w1*y)
½ * (w0*x + w1*y)
With p=0.5, using all inputs in the forward pass would inflate the activations by 2x from what the network was “used to” during training! => Have to compensate by scaling the activations back down by ½
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 59 25 Jan 2016
At test time all neurons are active always
=> We must scale the activations so that for each neuron:
output at test time = expected output at training time
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 60 25 Jan 2016
drop in forward pass
scale at test time
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 61 25 Jan 2016
test time is unchanged!
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 62 25 Jan 2016
<fun story time>
(Deep Learning Summer School 2012)
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 63 25 Jan 2016
(see class notes...)
fun guaranteed.
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 64 25 Jan 2016
[LeNet-5, LeCun 1980]
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 65 25 Jan 2016
Hubel & Wiesel, 1959
RECEPTIVE FIELDS OF SINGLE NEURONES IN
THE CAT'S STRIATE CORTEX
1962
RECEPTIVE FIELDS, BINOCULAR INTERACTION
AND FUNCTIONAL ARCHITECTURE IN THE CAT'S VISUAL CORTEX
1968...
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 66 25 Jan 2016
Video time https://youtu.be/8VdFf3egwfg?
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 67 25 Jan 2016
Topographical mapping in the cortex: nearby cells in cortex represented nearby regions in the visual field
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 68 25 Jan 2016
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 69 25 Jan 2016
A bit of history:
Neurocognitron [Fukushima 1980]
“sandwich” architecture (SCSCSC…) simple cells: modifiable parameters complex cells: perform pooling
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 70 25 Jan 2016
Gradient-based learning applied to document recognition
[LeCun, Bottou, Bengio, Haffner 1998]
LeNet-5
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 71 25 Jan 2016
ImageNet Classification with Deep Convolutional Neural Networks [Krizhevsky, Sutskever, Hinton, 2012]
“AlexNet”
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 72 25 Jan 2016
Classification Retrieval
[Krizhevsky 2012]
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 73 25 Jan 2016
Detection Segmentation
[Faster R-CNN: Ren, He, Girshick, Sun 2015] [Farabet et al., 2012]
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 74 25 Jan 2016
NVIDIA Tegra X1
self-driving cars
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 75 25 Jan 2016
[Taigman et al. 2014]
[Simonyan et al. 2014]
[Goodfellow 2014]
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 76 25 Jan 2016
[Toshev, Szegedy 2014]
[Mnih 2013]
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 77 25 Jan 2016
[Ciresan et al. 2013] [Sermanet et al. 2011]
[Ciresan et al.]
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 78 25 Jan 2016
[Denil et al. 2014]
[Turaga et al., 2010]
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 79 25 Jan 2016
|
|
||
|
|
|
|
[Vinyals et al., 2015]
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 81 25 Jan 2016
reddit.com/r/deepdream
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 82 25 Jan 2016
|
|
|
Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition [Cadieu et al., 2014]
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 84 25 Jan 2016
Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition [Cadieu et al., 2014]
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 6 85 25 Jan 2016
|
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h
> Enumberables: Each v. MapJune 29, 2014
As a novice Ruby programmer-in-training, I'm challenged to write new methods every week. This often involves research difference pieces of my potential solution to understand how I might arrive at my desired end result. This week in my research, I came across #map. At first glance, it seemed to basically be the same as #each. Upon a closer look, there is a key difference between the two.
Each v. Map
The essential difference between each and map is that map will actually alter an array, wheras each requires each does not. Both #each and #map only function within the block in which their being used. Therefore, even though #map may make changes to the array, the changes are only within to the block within that method.
Block is used to ensure that content appears on the next line. Dimensions (width and height) can be specified. Divs are by default set to disply block.
Example codeFirst, define an array:
array = [1,2,3]
=> [1, 2, 3]
Next, lets define a method using each
def add_each(array)
array.each {|x| x+2}
end
add_each(array)
=> [1, 2, 3]
Let's check on our array. What is it defined as?
array
=> [1, 2, 3]
Now, lets define a method using each, but this time use puts
def add_each(array)
array.each {|x| puts x+2}
end
add_each(array)
=> 3
4
5
[1, 2, 3]
You see the method is returning the value of each of the numbers in our original array +2. However, it is still showing that array = [1,2,3]
array
=> [1, 2, 3]
Last, lets define a method using #map. We will use the exact same format as our first #each method.
def add_map(array)
array.map {|x| x+2}
end
add_map(array)
=> [3, 4, 5]
Now, the method is returning the array with the numbers +2. Let's check on our array, though. Will it return array = [3,4,5]?
array
=> [1, 2, 3]
Nope! That's because #each and #map are only working on the blocks within the method that they are being run. The changes are not to the value of the variable itself outside of the method.


robcbi: Conditionally Unbiased Bounded Influence Estimates
Conditionally unbiased bounded influence estimates as described in
Kuensch et al. (1989) <doi:10.1080/01621459.1989.10478791> in three special cases of
the generalized linear model: Bernoulli, Binomial, and Poisson distributed responses.
| Version: |
1.1-2 |
| Depends: |
R (≥ 3.2.0) |
| Imports: |
robeth, stats, graphics, utils |
| Published: |
2018-06-22 |
| Author: |
Alfio Marazzi |
| Maintainer: |
A. Randriamiharisoa <Alex.Randriamiharisoa at chuv.ch> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
no |
| CRAN checks: |
robcbi results |
Downloads:
Reverse dependencies:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=robcbi
to link to this page.


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The luggage 'safety' measure travellers make that might be doing more harm then good
You'll have either seen this at the airport or done this
by Aimee Robinson, Victoria JonesAnyone who has ever been at an airport will have seen luggage tightly wrapped up in plastic.
Or, you might have popped a lock on your suitcase before a flight.
But an anonymous airline worker has revealed this might not protect your bags at all.
Instead, they suggest that this could actually be a reason why a suitcase could be broken into in the first place, reports the Express.
The inside information was posted to a Reddit forum which begs the question: “People who work for airlines, what are secrets passengers don't know?”
The worker wrote: “It's literally not even a deterrent.
"Some might take it as a sign that the bag is worth searching.”
Meanwhile, another user chimed in saying: “Locks on zippered bags are useless.

“I’ve done this many times to identify bags that are tagless and locked.”


Buttons, links
Buttons are used to interact with users and make actions if pressed. In Material Design, buttons were always closely-designed and built, as they have to give the user the feeling of controll and power, over the website. Here are the buttons included in Material Design 18 library.
Link


Question 1
Create a webpage containing an image and three buttons.
On click of second button a change the existing image to a new image.
On click of the third button change it once again.
On click of first, the first image should come back.
First
Second
Third


Imagine you have bought a wearable appliance for fitness tracking from BeFit, Inc. Before starting using the BeFit device you are reading BeFit’s instructions on how to activate the device.
Thank You for choosing our device! BeFit Inc. ("BeFit", "we", "us", "our") designs products and tools that track everyday health and fitness to empower and inspire users to lead healthier, more active lives.
To use BeFit device ("Device") you have to activate it by visiting our website https://befit.com/activate and giving your consent for the processing of your data by BeFit.
When activating your Device, you will be presented with a consent request for data processing. This consent request provides you with the following information:
You can choose the functionality you would like to have on the Device, review what information has to be processed to provide this functionality and decide if you want to consent to the information processing to be able to use this functionality.
Click "Activate" button to see how activation would work. You will be presented with a short user guide first and then with the consent request.


Form
Submit
4th Binance Anniversary Special
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Your personal client system
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The technique used by a robot to detect the objects and estimating their poses to grab, which are randomly placed inside a box or a pallet is called bin picking.
My purpose with this project is to develop a system that is based on learning the appearance model of objects using convolutional neural networks (CNN) is proposed.
Using PyTorch deeplearning framework, a 6DoF Poses of objects in the bin is estimated and it has experimented with the usability of depth sensing cameras in operation room lighting. Creation of the 3D object models is done using Blender tool, which is for Generating synthetic training dataset with the help of Unreal Engine(UE4) and NVidia Deep Learning Data Synthesizer(NDDS) software. By Deep Learning the created model, the object poses with adequate accuracy required for semantic grasping by any robot can be obtained. The overall system is implemented using ROS framework.
I have placed my work in the Github repo : https://github.com/avinashsen707/AUBOi5-D435-ROS-DOPE


ProFx Signals is a Finance company. We produce cryptocurrency trading signals and provide them as a service to our members through a package purchase. Our goal is to provide the highest quality crypto trading signals possible. We also provide a very user-friendly platform that makes it easy for our members to take trades. This platform is called ProFx Trading System. ProFx Signals Passive System is the second platform that we introduce and focuses on lower entry and allows you to build a lasting passive income. We utilize the trading experience we have to generate profits and share these profits with our users.
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Our core corporate principals are simplicity and transparency. The ProFx Signal Passive System is one the easiest trading system ever created. Our affiliate compensation plan is one of the simplest plans you will ever see. No fine print. No hidden details. No smoke and mirrors. Everything is clear, simple and upfront. We are completely transparent in all that we do. It simply does not get any easier or transparent than that. We welcome you to our global family of ProFx Signals users and would be delighted to have you join us.


TENSION HEADACHE RELIEF- acetaminophen and caffeine tablet, film coated
WALGREEN CO.
Disclaimer: Most OTC drugs are not reviewed and approved by FDA, however they may be marketed if they comply with applicable regulations and policies. FDA has not evaluated whether this product complies.
Liver warning: This product contains acetaminophen. The maximum daily dose of this product is 6 caplets (3,000m mg) in 24 hours. Severe liver damage may occur if you take:
Allergy alert: Acetaminophen may cause severe skin reactions. Symptoms may include:
If a skin reaction occurs, stop use and seek medical help right away.
Caffeine warning: The recommended dose of this product contains about as much caffeine as a cup of coffee. Limit the use of caffeine-containing medications, foods, or beverages while taking this product because too much caffeine may cause nervousness, irritability, sleeplessness, and, occasionally, rapid heartbeat.
corn starch, crospovidone, D&C red #27 aluminum lake, FD&C blue #2 aluminum lake, FD&C yellow #6 aluminum lake, magnesium stearate, microcrystalline cellulose, polyethylene glycol, polyvinyl alcohol, povidone, sodium starch glycolate, stearic acid, talc, titanium dioxide
Well at
Walgreens
WALGREENS PHARMACIST RECOMMENDEDǂ
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Tension
Headache
Relief
Acetaminophen /Caffeine /
Pain Reliever
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Compare to Excedrin® Tension Headache active ingredientsǂǂ
TAMPER EVIDENT: DO NOT USE IF IMPRINTED SAFETY SEAL UNDER CAP IS BROKEN OR MISSING
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ǂǂThis product is not manufactured or distributed by Novartis Consumer Health, Inc., owner of the registered trademark Excedrin® Tension Headache.
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IBN_norm = TestDecimatorIBN(Fs, OSR, Fsignal, K, M, filter_coefficients, filter_lengths, sdm_data) This function tests each decimation stage after quantization and optimizing the filter coefficients. It exports the IBN after each deciamtion stage. Fs: Sampling frequency OSR: Over Sampling Ratio Fsignal: Signal frequency K: Number of decimation stages M: Vector holds the decimation factor at each stage filter_coefficients: Matrix of filter coefficients exported from 'decimation_filters' function, which represents the coefficients at each stage filter_lengths: Vector of filter lengths exported from 'decimation_filters' function, which represents the filter length at each stage sdm_data: Array holds the Delta-Sigma Modulator data
0001 function IBN_norm = TestDecimatorIBN(Fs, OSR, Fsignal, K, M, filter_coefficients, filter_lengths, sdm_data) 0002 0003 % 0004 % IBN_norm = TestDecimatorIBN(Fs, OSR, Fsignal, K, M, filter_coefficients, filter_lengths, sdm_data) 0005 % 0006 % This function tests each decimation stage after quantization and 0007 % optimizing the filter coefficients. It exports the IBN after each 0008 % deciamtion stage. 0009 % 0010 % Fs: Sampling frequency 0011 % OSR: Over Sampling Ratio 0012 % Fsignal: Signal frequency 0013 % K: Number of decimation stages 0014 % M: Vector holds the decimation factor at each stage 0015 % filter_coefficients: Matrix of filter coefficients exported from 'decimation_filters' function, 0016 % which represents the coefficients at each stage 0017 % filter_lengths: Vector of filter lengths exported from 'decimation_filters' function, which 0018 % represents the filter length at each stage 0019 % sdm_data: Array holds the Delta-Sigma Modulator data 0020 % 0021 0022 % Calculating the baseband frequency 0023 % Fb = Fs/(2*OSR); 0024 0025 filter_downsample = struct('filter_data', [], 'downsample_data', []); 0026 0027 for i = 1 : K, 0028 if i == 1, 0029 filter_downsample(i).filter_data = filter(filter_coefficients(i,1:filter_lengths(i)), [1], sdm_data); 0030 filter_downsample(i).downsample_data = downsample(filter_downsample(i).filter_data, M(i)); 0031 % IBN_norm(i) = LPIBN((filter_downsample(i).downsample_data)./(sum(filter_coefficients(i,:))), Fsignal, Fs/M(i), Fb, 0); 0032 tmpdata1(i) = plotFunction((filter_downsample(i).downsample_data)./(sum(filter_coefficients(i,:))),'OSR',OSR/prod(M(1:i)),'fsig',Fsignal,'stats',true,'no_nz_bins',20,'plot_fft',false,'fs',Fs/(M(i))); 0033 IBN_norm(i) = tmpdata1(i).P_IBN_dB; 0034 else 0035 filter_downsample(i).filter_data = filter(filter_coefficients(i,1:filter_lengths(i)), [1], (filter_downsample(i-1).downsample_data)./sum(filter_coefficients(i-1,:))); 0036 filter_downsample(i).downsample_data = downsample(filter_downsample(i).filter_data, M(i)); 0037 % IBN_norm(i) = LPIBN((filter_downsample(i).downsample_data)./(sum(filter_coefficients(i,:))), Fsignal, Fs/prod(M(1:i)), Fb, 0); 0038 tmpdata2(i) = plotFunction((filter_downsample(i).downsample_data)./(sum(filter_coefficients(i,:))),'OSR',OSR/prod(M(1:i)),'fsig',Fsignal,'stats',true,'no_nz_bins',20,'plot_fft',false,'fs',Fs/(prod(M(1:i)))); 0039 IBN_norm(i) = tmpdata2(i).P_IBN_dB; 0040 end 0041 end


Preparation time
overnight
Cooking time
over 2 hours
Serves
Serves 4
This slow-cooked beef casserole is accompanied by a robust red-wine and horseradish sauce and buttered new potatoes.
The day before you want to serve the steaks, place them into a large mixing bowl, pour over the wine and add the thyme. Cover the bowl and chill in the fridge overnight.
When the steaks have marinated, preheat the oven to 160C/140C Fan/Gas 3.
Heat the oil in a flameproof casserole over a medium heat. Add the carrots, celery and onion and fry for 6-8 minutes, or until softened and golden-brown.
Add the garlic, chopped tomatoes and Worcestershire sauce and stir well to combine.
Lay the marinated steaks on top of the vegetables and season, to taste, with salt and freshly ground black pepper. Pour over the marinade and the stock and bring the mixture to the boil.
Cut a circle of greaseproof paper slightly smaller than the diameter of the casserole. Place it inside the casserole to cover its contents. Transfer to the oven for 1½-1¾ hours, until the meat is very tender, but not falling apart. Remove the meat from the casserole, cover and set aside to rest. Keep warm.
Meanwhile, take a ladleful of sauce from the casserole and pour it into a bowl. Whisk in the creamed horseradish until well combined. Strain the mixture back into the casserole through a fine sieve, discarding the sieve contents.
Strain the whole casserole through a fine sieve, collecting the strained liquid in a clean saucepan. Simmer until it has thickened to the consistency of double cream. Season with salt and pepper, then stir in the butter and parsley. Set aside and keep warm.
Heat the oil in a deep-fat fryer to 180C/350F. Alternatively, heat the oil in a deep-sided, heavy-based saucepan until a breadcrumb sizzles and turns golden-brown when dropped into it. (Caution: Hot oil can be dangerous. Do not leave unattended.)
In a large bowl, mix together the flour and smoked paprika until well combined. Pour the milk into a bowl.
Dust the onion slices with the seasoned flour, then dip them into the milk. Finally, roll them in the seasoned flour again to coat.
Carefully lower the onion slices into the hot oil and fry for 1-2 minutes, or until crisp and golden-brown.
To serve, place one of the braised rib-eye steaks onto each of four serving plates. Spoon over a little of the sauce. Garnish with the deep-fried onions. Serve the new potatoes in a large bowl, dotted with the butter.


Traversers are classes that "walk" the scene graph maintain
any state that is needed as part of the traversal. For example the WriteTraverser
class maintains a FILE * which is the destination file of the export that is being
performed by the Traverser. As another example, the OpenGLTraverser maintains a GLX
context that represents the window into which the scene graph is being rendered.
Every node class has a function called Traverse which takes a Traverser pointer as an
argument and causes the Traverser to "render" that node (render in this sense
may mean many things). For more information
click here.
Each Traverser class is defined further below.
ActionTraverser class: This class is fundamental to the VEM. It provides for the generation of initial events that are
then propagated to any nodes that it may be ROUTEd to. At every frame, an
ActionTraverser is the first traverser in the browser's list of Traversers to be accessed.
The ActionTraverser will cause any sensors that should initiate an event on this
frame to fire. For example, a TimeSensor node encountered during the ActionTraversal
will generate fractionChanged events at each frame. These events are what drives an
animation. The ActionTraverser also converts user input (such as mouse movements)
into events (for example if the mouse is clicked over an Anchor node a new file will be
loaded).
D3DTraverser class: This class is a stand-in for a future traverser
which will render the scene graph to a Direct 3D window. This class is not yet
implemented.
OGLTraverser class: This class renders the scene graph to an OpenGL
window. To support multiple views of a scene graph in a single application, the
browser may have more than one OGLTraverser (each with it's own bound viewpoint). In
essence an OGLTraverser (or for that matter any RenderTraverser) is associated with a
single window.
RenderTraverser class: This is the base class for both the
D3DTraverser and the OGLTraverser (and for that matter for any other traverser that will
render the 3D scene). This class handles nodes that are common to all rendering, for
example the Switch node is handled here by selecting which child to render (which is the
same code regardless of the underlying graphics layer).
SerializeTraverser class: This un-implemented class will write
the .wrl file to disc in a proprietary binary format. This might be used for example
in a PC based game to protect 3D content from prying eyes. This would support both
export and import of a .wrl file in this binary format.
Traverser class: This is the base class for all Traversers. It
provides high level support for such things as traversing the children of grouping nodes.
ValidateTraverser class: This traverser "walks" the scene
graph looking for (and possibly reporting on) invalid data in the scene graph. This
class would be used primarily for debugging.
WriteTraverser class: This class exports a .wrl file in various
formats. It could serve as the basis for a pretty printer for example. It also
ensures that only non-default fields are written to the file, thus lessening the resulting
file size.


Configuration
is pure Java so there is no need for a
shadow to exist. The methods have been preserved but marked deprecated to prevent build breakages but in this
version implementation has been modified to simply call through to the Framework code which may in some cases cause
test failures due to the way the shadow diverged in behaviour from the Framework code. Some notes have been added
to help you migrate in these cases.
Some notes to help you migrate:-
setLocale(java.util.Locale)
only exists in API 17+ so calling this on earlier APIs will fail with NoSuchMethodException
setToDefaults()
overrides the frameworks natural defaults to set the flags for
Configuration.screenLayout
to include Configuration.SCREENLAYOUT_LONG_NO
and
Configuration.SCREENLAYOUT_SIZE_NORMAL
overrideQualifiers(java.lang.String)
and getQualifiers()
have no effect and can be removed.
@Deprecated @Implements(value=android.content.res.Configuration.class) public class ShadowConfiguration extends java.lang.Object
Configuration
.| Constructor and Description |
|---|
ShadowConfiguration()
Deprecated.
|
| Modifier and Type | Method and Description |
|---|---|
java.lang.String |
getQualifiers()
Deprecated.
Use
RuntimeEnvironment.getQualifiers() although there should be no reason to obtain this
since it is typically set in tests through Config.qualifiers() so you should use a constant in these cases. |
void |
overrideQualifiers(java.lang.String qualifiers)
Deprecated.
|
void |
setLocale(java.util.Locale l)
Deprecated.
|
void |
setTo(android.content.res.Configuration o)
Deprecated.
|
void |
setToDefaults()
Deprecated.
|
@Deprecated @Implementation public void setTo(android.content.res.Configuration o)
@Deprecated @Implementation public void setToDefaults()
@Deprecated @Implementation public void setLocale(java.util.Locale l)
@Deprecated public void overrideQualifiers(java.lang.String qualifiers)
@Deprecated public java.lang.String getQualifiers()
RuntimeEnvironment.getQualifiers()
although there should be no reason to obtain this
since it is typically set in tests through Config.qualifiers()
so you should use a constant in these cases.


Server and Device Discovery
Current status: Waiting for Steam Authentication...
Server IP Address:
Server Port:
Steam ID:
Player Token:
Back to homepage


Side Projects
Charades
I played a lot of charades with my friends and roommates in college. We had a bucket in our living room that anyone could drop a charades prompt into. During the Covid pandemic, we've' been keeping up over video calls, and wished that we had a way to replicate that. This is my attempt at re-creating our communal charades bucket. It's a pretty simple project, but it was nice to create something tangible for my friends.
You can view this project on Github
For this project, I used NextJS for the frontend and the API, and Redis to store data.


KittyCoin Club
Change network to Main Ethereum Network
Just open metamask and use dropdown at the top
Created by Nathan Glover - Attribution License 3.0 - 2018


Before Change
2000, self.STEERING_MID)
packet = "{0},{1}".format(str(output_steering).zfill(4),
str(output_throttle).zfill(4),)
self.write_pwm(packet)
if self.debug:
print("output_steering=%d, output_throttle=%d\r" %
(eval(packet)))
except OSError as err:
print(
"Unexpected issue setting PWM (check wires to motor board): {0}".format(err))
After Change
else:
print(f"Warning: steering = {output_steering}, STEERING_MID = {self.STEERING_MID}")
print(f"Warning: throttle = {output_throttle}, MAX_FORWARD = {self.MAX_FORWARD}, STOPPED_PWM = {self.STOPPED_PWM}, MAX_REVERSE = {self.MAX_REVERSE}")
print("Not sending PWM value to MM1")
except OSError as err:
print(
"Unexpected issue setting PWM (check wires to motor board): {0}".format(err))


|
SideCar
|
Go to the source code of this file.
Data Structures | |
| class | SideCar::Algorithms::ProcessingStat |
| Processing statistic for an Algorithm. More... | |
Namespaces | |
| SideCar | |
| Namespace for all entities specific to the 331 SideCar project. | |
| SideCar::Algorithms | |
| Namespace for entities related to SideCar algorithms. | |


This is the place for some experimental or advanced settings
Right now, it is misused to display the current options:
{{options | json}}
Options to configure the visual appearance of the speed-reader
Color for Selection:
Font:
Sample
Options to configure keybindings:
TBD
General options to tweak the speed-reader
Duration modifiers
Advanced and experimental settings


--- layout: page title: "Tag" permalink: /tag/ --- {% for tag in site.tags %} {{ tag | first }} {% if forloop.last == false %}


Before Change
:param terminal:
:return:
state = self.filter(state)
self.batch_steps += 1
self.current_episode["states"].append(state)
self.current_episode["actions"].append(self.last_action)
self.current_episode["rewards"].append(reward)
After Change
// Transform into np arrays, append episode to batch, start new episode dict
path = self.get_path()
self.current_batch.append(path)
print("Episode finished, length=" + str(len(path["states"])))
self.current_episode = defaultdict(list)
if self.batch_steps == self.batch_size:


--- title: /home/ayonga/.dropboxes/business/Dropbox/research/dzopt/frost/matlab/utils/mathlink/str2mathstr.m File Reference layout: doxygen comments: false sidebar: doxygen_sidebar topnav: topnav tags: false ---
Converts a Matlab string to a Mathematica string. More...
Functions | |
| function expr = | str2mathstr (char s,varragin varargin) |
| Converts a Matlab string to a Mathematica string. More... | |
Converts a Matlab string to a Mathematica string.
| function expr = str2mathstr | ( | char | s, |
| varragin | varargin | ||
| ) |
Converts a Matlab string to a Mathematica string.
Copyright (c) 2016, AMBER Lab All right reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted only in compliance with the BSD 3-Clause license, see http://www.opensource.org/licenses/bsd-license.php
| s | the character array |
| varargin | conversion options. str2mathstr ( ..., [ "ConvertString", ConvertString_value ] )
|
Referenced by general2math().


Combining mark text generator
Copy RL Override (202E)
Copy Zero Width Space (200B)
Write utf numbers without \U, with spaces inbetween each char
Copy custom symbols
Click on a button in the table to copy it:
Workspace:
Table offset:
Update table
Toggle numbers
more


emnlp emnlp2013 emnlp2013-188 emnlp2013-188-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Bowei Zou ; Guodong Zhou ; Qiaoming Zhu
Abstract: Scope detection is a key task in information extraction. This paper proposes a new approach for tree kernel-based scope detection by using the structured syntactic parse information. In addition, we have explored the way of selecting compatible features for different part-of-speech cues. Experiments on the BioScope corpus show that both constituent and dependency structured syntactic parse features have the advantage in capturing the potential relationships between cues and their scopes. Compared with the state of the art scope detection systems, our system achieves substantial improvement.


{% extends "frontend/base.html" %} {% load staticfiles %} {% block title %}Contact{% endblock %} {% block banner %}
{% endblock %} {% block content %}Please use the form below to contact me.
{% if post %} {% if success %}Thank you for your message!
I'll get back to you as soon as I can.
Couple of problems...
There was a problem or two with some of the information you submitted. Please correct the following and try again:


First name is required!
Invalid name!
Last name is required!
Address is required!
NIC is required!
Invalid NIC!


Trippy
Add a Pin
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Going Back!
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Log in to Perseids
In the top right corner, click "Account"
Scroll down and click "Usage Stats by User"
Find your name
Right-click on your name and select "Copy link address"
You can now copy your identifying url wherever you need


When we were looking to build a website, we had a certain vision in mind. As a LA band we wanted a clean, edgy and professional website. Browsing through the templates, we fancied the "TYPO light" template, and chose it as a base for our website.
Since we did not have a lot of initial content, the simple one-page structure suited our needs and allowed us a simple and clear display of our art.


About me
I'm currently pursuing my masters degree in computer science and have been working part time as a software developer for over three years now.
I'm focusing on combining innovative technologies like cloud architecture and machine learning with web engineering.
I program in Python, JavaScript and Java and use technologies like NodeJs, React and TailwindCSS.
If you want to check out what I am doing, check my social media accounts.


Black to play 61
Unlimited game
pips Black=142 White=106
(CorrectAnswer= 0/0, ErrorScore= 0)
The cube location here is crucial.
With Black owning the cube, the rollout results look very different (see Variant position below).
This is a relatively rare instance of a middle-game "cube provocation play" where the top play 13/9 6/5 actually induces the opponent to give an inefficient double, whereas the mundane 13/6 allows the opponent to leave the cube centered for more efficient use later.
With Black owning the cube, the rollout results look very different (see Variant position below).
This is a relatively rare instance of a middle-game "cube provocation play" where the top play 13/9 6/5 actually induces the opponent to give an inefficient double, whereas the mundane 13/6 allows the opponent to leave the cube centered for more efficient use later.
1.


the blog
tales from DBC
previous entries
- first
- second
- third
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- fifth
- sixth
- seventh
- eigth
- ninth
- ten
- eleven
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- fourteen
- fifteen
- sixteen
Version Control and Git
Version control is a method of tracking changes to a body of code. This allows coders to both keep up with the changes they've made, as well as revert to older versions of a chunk of code if they are stuck.
Git is a type of version control software that allows for a central, primary repository and as many "branches" off that repository as necessary. From this master repository, a developer can take files off of a larger project, change those files, and then merge them with the master repository.
GitHub provides an online space to store a central repository so that multiple people can work on a project at once. Let's draw an analogy to painting. Let's say three artists are working on one painting of a person. One artist is responsible for painting the head, one for the body, and one for the clothes.Now, "traditionally," these artists would have to work from the same painting, passing around the same piece of canvas and adding to it. If, however, these artists had a GitHub analog, they would each be able to download a copy of that painting to their own personal canvas, add to it, and then merge that painting with the primary canvas. This lets them work all at once. This is the primary advantage of GitHub.