text:
Q: ASP.NET Core 2.0 on IIS error 502.5 I install .net core 2.0 to my windows server 2008
I have two web project. Both of them work in my PC. But when I publish to my server one project work very well but other give that error

HTTP Error 502.5 - Process Failure
Application 'MACHINE/WEBROOT/APPHOST/KI-TAP.COM'
      with physical root 'C:\HostingSpaces\Reseller1\ki-tap.com\wwwroot\'
       failed to start process with commandline
       'dotnet .\ki-tap.dll', ErrorCode = '0x80004005 : 8000808c.

I update my windows server but it still gives the same error.
here is my program.cs (I didn't add code here. This is default)
  public class Program
    {
        public static void Main(string[] args)
        {
            BuildWebHost(args).Run();
        }

        public static IWebHost BuildWebHost(string[] args) =>
            WebHost.CreateDefaultBuilder(args)
                .UseStartup<Startup>()
                .Build();
    }

and here is my web.config (published and default code )
<?xml version="1.0" encoding="utf-8"?>
<configuration>
  <system.webServer>
    <handlers>
      <add name="aspNetCore" path="*" verb="*" modules="AspNetCoreModule" resourceType="Unspecified" />
    </handlers>
    <aspNetCore processPath="dotnet" arguments=".\ki-tap.dll" stdoutLogEnabled="true" stdoutLogFile=".\logs\stdout" />

  </system.webServer>
</configuration>

and here is my startup.cs(I added session configuration)
using System;
using System.Collections.Generic;
using System.Linq;
using System.Threading.Tasks;
using Microsoft.AspNetCore.Builder;
using Microsoft.AspNetCore.Hosting;
using Microsoft.Extensions.Configuration;
using Microsoft.Extensions.DependencyInjection;

namespace ki_tap
{
    public class Startup
    {
        public Startup(IConfiguration configuration)
        {
            Configuration = configuration;
        }

        public IConfiguration Configuration { get; }

        // This method gets called by the runtime. Use this method to add services to the container.
        public void ConfigureServices(IServiceCollection services)
        {
            services.AddMvc();
            services.AddSession();

        }

        // This method gets called by the runtime. Use this method to configure the HTTP request pipeline.
        public void Configure(IApplicationBuilder app, IHostingEnvironment env)
        {
            if (env.IsDevelopment())
            {
                app.UseDeveloperExceptionPage();
                app.UseBrowserLink();
            }
            else
            {
                app.UseExceptionHandler("/Home/Error");
            }

            app.UseStaticFiles();
            app.UseSession();

            app.UseMvc(routes =>
            {
                routes.MapRoute(
                    name: "default",
                    template: "{controller=Home}/{action=Index}/{id?}");
            });
        }
    }
}

Edited:
with @set suggestion, I run that command in windows server 2008 in cmd console

C:\Program Files (x86)\dotnet\dotnet myProjectPath\project.DLL

it gives the below error. What should I do?
 package: 'Microsoft.AspNetCore.Antiforgery', version: '2.0.1'
 path: 'lib/netstandard2.0/Microsoft.AspNetCore.Antiforgery.dll'
his assembly was expected to be in the local runtime store as the application
s published using the following target manifest files:
 aspnetcore-store-2.0.3.xml

A: You may have the same problem as described here in aspnet/IISIntegration repo. The solution is to add the following into .csproj file:
<PropertyGroup>
   <PublishWithAspNetCoreTargetManifest>false</PublishWithAspNetCoreTargetManifest>
</PropertyGroup>

And in general, the source of this problem is that the dotnet version on your local computer and the dotnet version on the server are different.

redpajama_set_name: RedPajamaStackExchange


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text:
Report: U.S. Mulls Scrapping UN Definition Of Palestinian 'Refugee'
Deborah Brand
TEL AVIV – Amid worsening relations with the Palestinians, the Trump administration is now considering rejecting the UN's definition of Palestinian "refugees" and cutting off all financial aid to the agency that caters to them, an Israeli TV report said Tuesday.
According to a Hadashot News TV report, the State Department is mulling an end to all of its $360-million annual aid to United Nations Relief and Works Agency (UNRWA) after it already froze a payment to the organization of more than $100 million in January. The report said the move was a "new ultimatum" to pressure the Palestinian Authority into returning to the negotiating table with Israel.
The White House is also considering formally declaring that it rejects UNRWA's mandate that stipulates that so-called Palestinian refugees from 1948 can perpetually pass their refugee status from generation to generation – the only refugees in the world that bear this status. Of the estimated half million Palestinians who fled or were pushed out of their homes after the State of Israel's establishment, only a few thousand remain. However, the number of refugees has ballooned to some 7.2 million of their descendants worldwide.
Breitbart's Aaron Klein reported on the manner in which UNRWA has altered the traditional definition of refugees. Klein documented:
The UNHRC, the international body that ministers to all refugees besides Palestinians, has a fairly sensible definition of what a refugee is: "A refugee is someone who has been forced to flee his or her country because of persecution, war or violence. A refugee has a well-founded fear of persecution for reasons of race, religion, nationality, political opinion or membership in a particular social group."
In other words, the UNHRC defines a refugee as someone who was forced to flee his or her home and cannot return for fear of persecution.
UNRWA, however, defines a Palestinian "refugee" entirely differently. A Palestinian "refugee" is any person whose "normal place of residence was Palestine during the period 1 June 1946 to 15 May 1948 and who lost both home and means of livelihood as a result of the 1948 conflict." So UNWRA counts as "refugees" any local Arab who lived in Palestine for as little as two years, knowing that scores of Arabs immigrated to the area during those years in search of employment amid talks of creating a future Jewish state.
UNRWA states that "Palestine refugees are persons who fulfill the above definition and descendants of fathers fulfilling the definition."
This means that even if original Palestinian "refugees" long ago immigrated to another country and became citizens of that country, they and their descendants are still considered "refugees" according to UNWRA. The very definition flies in the face of what a refugee is supposed to be. It is also in direct contrast to the Convention on Refugees, which dictates that a person who "has acquired a new nationality, and enjoys the protection of the country of his new nationality" is exempted from the status of refugee.
UNRWA's definition of a "refugee" doesn't mention UNHCR's "well-founded fear of being persecuted." Indeed, the Palestinians have no fear of being persecuted by Israel, and would not be considered a "refugee" under ordinary international criteria.
Meanwhile, one of the sticking points in peace talks has been the Palestinian demand for the "right of return" – i.e., that so-called refugees would be able to return to their original homes – something that Israel will never accept because it would mean the end of the Jewish nature of the country.
On Thursday, Trump warned the U.S. would stop aid if the Palestinians continued to refuse to enter talks with Israel.
"That money is not going to them unless they sit down and negotiate peace, because I can tell you that Israel does want to make peace, and they're going to have to want to make peace too, or we're going to have nothing to do with it any longer," he said at Davos.
UNRWA chief Pierre Krahenbuhl on Tuesday said the U.S. move "has a political dimension that I think should be avoided." He called on other countries to send aid to the tune of $800 million to assist Palestinian refugees in Syria, Gaza and the West Bank.
U.S. Ambassador to the UN Nikki Haley has been vocal about her support for cutting aid to the Palestinian Authority in response to its "pay-for-slay" program awarding terrorists and their families salaries, as well as its general attitude to the U.S., which includes severing ties after President Donald Trump recognized Jerusalem as Israel's capital. Other officials are reportedly opposed to the cuts. Among them are Secretary of State Rex Tillerson and Defense Secretary James Mattis, who believe that doing so would only worsen the situation in the region, especially in neighboring Jordan, which is home to hundreds of thousands of so-called Palestinian refugees.
Earlier this month, Trump sparked outrage from the Palestinian leadership when he asked why the U.S. should make "any of these massive future payments" to the Palestinians.
Trump tweeted, "We pay the Palestinians HUNDRED OF MILLIONS OF DOLLARS a year and get no appreciation or respect. They don't even want to negotiate a long overdue peace treaty with Israel" and added "with the Palestinians no longer willing to talk peace, why should we make any of these massive future payments to them?"
"We have taken Jerusalem, the toughest part of the negotiation, off the table, but Israel, for that, would have had to pay more," he continued.
In a two hour address to the Palestine Liberation Organization, Palestinian President Mahmoud Abbas slammed Trump over the tweet, saying, "May your house come to ruin."
The White House is also considering shuttering the Washington offices of the PLO, the TV report said.
Israel / Middle EastNational SecurityPoliticsDonald Trumpforeign aidnetanyahuPalestinian AuthorityPalestinian refugeesPalestiniansrefugeesSecretary of State Rex TillersonU.S. Ambassador to the U.N. Nikki HaleyUNHCRUNRWA

redpajama_set_name: RedPajamaCommonCrawl


text:
Credit Cards »News & AdviceNews»Innovations and Payment Systems»8 FAQs about EMV credit cards
Innovations and Payment Systems
8 FAQs about EMV credit cards
Chip? PIN? Signature? Do old cards work? Answers to frequently asked questions
by Sienna Kossman
Legal, Regulatory, and Privacy Issues
The way you use a credit card is changing as the U.S. catches up with the rest of the world and moves to cards. Here, in Q&A format, is what you need to know about the transition.
It's been almost two years since the nationwide shift to EMV officially began. At this point, many of the best credit cards contain this feature.
EMV — which stands for Europay, Mastercard and Visa — is a global standard for cards equipped with computer chips and the technology used to authenticate chip-card transactions. In the wake of numerous large-scale data breaches and increasing rates of counterfeit card fraud, U.S. card issuers have migrated to this new technology to protect consumers and reduce the costs of fraud.
"These new and improved cards are being deployed to improve payment security, making it more difficult for fraudsters to successfully counterfeit cards," says Julie Conroy, research director for retail banking at Aite Group, a financial industry research company. "It's an important step forward."
For merchants and financial institutions, the switch to EMV means adding new in-store technology and internal processing systems, and complying with new liability rules. For consumers, it means learning a new payment processes.
Want to know more about the ongoing transition and your EMV chip-equipped credit card? Here are eight frequently asked questions to help you understand the changes.
Popular EMV questions
Why are EMV cards more secure?
How do I use an EMV card to make a purchase?
Is card dipping the only option?
Will I have to sign or enter a PIN?
If fraud occurs, who will be liable?
Is the transition to EMV complete?
Will my chip card work everywhere?
Can I use my chip card outside the U.S.?
1. Why are EMV cards more secure than traditional cards?
It's that small, metallic square you'll see on new cards. That's a computer chip, and it's what sets it apart the new generation of cards.
The magnetic stripes on traditional credit and debit cards store contain unchanging data. Whoever accesses that data gains the sensitive card and cardholder information necessary to make purchases. That makes traditional cards prime targets for counterfeiters, who convert stolen card data to cash.
"If someone copies a mag stripe, they can easily replicate that data over and over again because it doesn't change," says Dave Witts, president of U.S. payment systems for Creditcall, a payment gateway and EMV software developer.
Unlike magnetic-stripe cards, every time an EMV card is used for payment, the card chip creates a unique transaction code that cannot be used again.
If a hacker stole the chip information from one specific point of sale, typical card duplication would never work "because the stolen transaction number created in that instance wouldn't be usable again and the card would just get denied," Witts says.
EMV technology will not prevent data breaches from occurring, but it will make it much harder for criminals to successfully profit from what they steal.
Experts hope it will help significantly reduce fraud in the U.S., which has doubled in the past seven years as criminals have shied away from countries that already have transitioned to EMV cards, Conroy says.
"The introduction of dynamic data is what makes EMV cards so effective at bringing down counterfeit card rates in other countries," she says.
Counterfeit fraud rates have already decreased in the U.S. as a result of EMV adoption, according to Mastercard and Visa. In March 2017, chip-enabled merchants saw a 58 percent drop in counterfeit fraud compared to a year earlier, according to Visa. Mastercard noticed a difference even earlier: It recorded a 54 percent decrease in counterfeit fraud costs among its EMV-ready merchants from April 2015 to April 2016.
How EMV chips are made
Microcomputers on your credit cards
A look behind the scenes at an ultra-secure credit card factory where small but complex smart card chips are made.
2. How do I use an EMV card to make a purchase?
Just like magnetic-stripe cards, EMV cards are processed for payment in two steps: card reading and transaction verification. However, with EMV cards you no longer have to master a quick, fluid card swipe in the right direction. Chip cards are read in a different way.
"Instead of going to a register and swiping your card, you are going to do what is called 'card dipping' instead, which means inserting your card into a terminal slot and waiting for it to process," Conroy says.
When an EMV card is dipped, data flows between the card chip and the issuing financial institution to verify the card's legitimacy and create the unique transaction data. This process isn't as quick as a magnetic-stripe swipe.
"It will take a tiny bit longer for that transmission of data to happen," Witts says. "If a person just sticks the card in and pulls it out, the transaction will likely be denied. A little bit of patience will be involved."
While chip card transactions may take a bit longer than mag stripe transactions, total card processing time will vary between merchants and eventually speed up as the new payment environment is improved.
"It will vary depending on the merchant, the equipment and the point-of-sale system," Ferenczi said. "I think that time lag overtime will be reduced for those longer transactions."
For more information about current EMV transaction speeds, see "The big chip card switch: a year of living with EMV."
Wait, what's the card called?
The new EMV cards in the U.S. might be called any of the following terms:
Chip card
Smart-chip card
Chip-enabled smart card
Chip-and-choice card (PIN or signature)
EMV smart card
3. Is card dipping the only option?
Not necessarily. EMV cards can also support contactless card reading, also known as near field communication. Instead of dipping or swiping, NFC-equipped cards are tapped against a terminal scanner that can pick up the card data from the embedded computer chip.
"Contactless transactions are more consumer-friendly because you just have to tap," said Martin Ferenczi, president of Oberthur Technologies, the leading global EMV product and service provider. "Around the world, there is a move to make EMV cards dual-interface, which means contact and contactless. However, in the U.S., most financial instructions are issuing contact cards."
Dual-interface cards and the equipment needed to scan them are expensive. Right now, the first step is to successfully integrate EMV cards into the U.S. shopping scene. Dual interface will arrive later, although they are in production and rolling out slowly now, according to Ferenczi.
"Dual-interface cards, which in 2016 represented less than 5 percent of total EMV cards in the U.S., will have significant growth in 2017," Ferenczi said. "Issuers want to offer their cardholders the added level of speed and convenience provided by tapping."
4. Will I still have to sign or enter a PIN for my card transaction?
Yes and no. You will have to do one of those verification methods, but it depends on the verification method tied to your EMV card, not if your card is debit or credit. Chip-and-PIN cards operate just like the checking-account debit card you have been using for years.
Entering a PIN connects the payment terminal to the payment processor for real-time transaction verification and approval. However, many payment processors are not equipped with the technology needed to handle EMV chip-and-PIN credit transactions. So it is not likely you will have to memorize new PINs anytime soon, according to Conroy.
"There aren't going to be many issuers requiring a PIN," she says. "A vast majority will be issuing chip-and-signature cards, which aren't all that different from how credit cards work now."
As with a magnetic-stripe credit card, you sign on the point-of-sale terminal to take responsibility for the payment when making a chip-and-signature card transaction.
U.S. chip-and-PIN cards will be transitioned in slowly, according to Ferenczi.
"The card production demand today is really based on chip-and-signature cards," he says. "It will probably take two to three years to fully convert to chip-and-PIN."
Despite a slow transition overall, those who get chip-and-PIN cards will be able to use them right away.
"If a terminal doesn't have the ability to accept a PIN, it will then step down to accepting a signature," says Randy Vanderhoof, executive director of the Smart Card Alliance. "There will always be a secondary option."
Chip cards, by the numbers
855 million: Estimated number of chip cards issued to U.S. consumers so far, per U.S. Payments Forum estimates
85%: Estimated percentage of all U.S. credit cards issued with chips at the end of July 2017, per CPI Card Group
60%: Percentage of U.S. debit cards issued with chips by the end of July 2017, per CPI Card Group
$2-$4: Approximately cost of issuing a new EMV card, per First Data
15 million: Estimated total number of point-of-sale terminals that have to be upgraded to accept chip cards, per Javelin Research & Strategy
$6.75 billion: Estimated cost of replacing the 15 million POS terminals with chip card-compliant machines, per Javelin Research & Strategy
50-52%: Estimated percentage of merchant locations currently ready to process chip card payments, per Visa, U.S. Payments Forum and Strawhecker Group estimates
1.81-2.3 million: Estimated number of U.S. point-of-sale terminals enabled to accept chip card payments, per Mastercard and Visa estimates.
5. If fraud occurs after EMV cards are issued, who will be liable for the costs?
If an in-store transaction is conducted using a counterfeit, stolen or otherwise compromised card, consumer losses from that transaction fall back on the payment processor or issuing bank, depending on the card's terms and conditions.
Since the Oct. 1, 2015 deadline created by major U.S. credit card issuers Mastercard, Visa, Discover and American Express, the liability for card-present fraud has shifted to whichever party is the least EMV-compliant in a fraudulent transaction.
Consider the example of a financial institution that issues a chip card used at a merchant that has not changed its system to accept chip technology. This allows a counterfeit card to be successfully used.
"The cost of the fraud will fall back on the merchant," Ferenczi says.
The change is intended to help bring the entire payment industry on board with EMV by encouraging compliance to avoid liability costs.
Today, any parties not EMV-ready could face much higher costs in the event of a large data breach.
Until recently, automated fuel dispensers had until 2017 to make the shift to EMV. However, a December 2016 Visa and Mastercard agreement now gives pay-at-the-pump gas terminals until October 2020 to become EMV-compliant.
"Given the migration challenges for implementing EMV in the petroleum environment, Visa's and Mastercard's modification of the liability shift dates will be beneficial to the retail petroleum industry and the U.S. chip migration," Vanderhoof explained in an emailed statement.
So for now, gas stations do not fall under the existing EMV fraud liability shift rules. ATMs still have two fraud liability shift dates: Mastercard's that passed in October 2016 and Visa's in October 2017. Until both dates pass, ATMs will follow existing fraud liability rulings.
See ATMs changing to accept EMV chip cards for more ATM migration information.
6. So since Oct. 1, 2015, is the transition to EMV technology complete?
Although the deadline was strong encouragement for all payment processing parties to become EMV-compliant as soon as possible, not everyone has made the transition yet.
"Most countries that migrated to EMV have four or five banks that issue cards and a couple of acquirers that provide a service to merchants," said Jason Oxman, chief executive officer of the Electronic Transactions Association. "We have 13,000 financial institutions in the U.S. that issue credit and debit cards, and we have 5 million merchant locations in the U.S. that accept debit and credit cards. So it's a massive undertaking."
EMV debit cards have rolled out at a slower pace. Approximately 25 percent of U.S. debit cards were issued as EMV chip cards by the end of 2015, according to Mercator Advisory Group's Sarah Grotta, director of Debit Advisory Service. That ratio rose to 33 percent by the end of June 2016, and today, approximately 60 percent of issuer's debit cards are EMV chip-equipped, according to CPI Card Group estimates.
EMV debit cards are slowly being issued because banks have to prep their software to accept those new cards as well, according to Ferenczi.
The majority of chip cards in the hands of cardholders today have come from larger issuers auch as Bank of America and Chase, according to the Federal Reserve Bank of Chicago. The cost of this EMV transition is causing smaller banks to convert their cards more slowly.
The EMV credit card rollout, however, is progressing steadily. More than 449 million Visa chip cards have been issued in the U.S. overall so far, as of June 2017. About 68 percent of Mastercard-branded credit and debit cards have been issued with chips so far, as of Oct. 1, 2016. For all U.S. credit cards, CPI Card Group estimates approximately 85 percent of cards now have chips on them. Overall, 70 percent of cards (debit and credit) have been converted to EMV, per CPI Card Group.
If some of the cards in your wallet have yet to be issued with chips, don't fret.
"Different companies have different rollout strategies," says Doug Johnson, vice president of risk management policy for the American Bankers Association. Some will base their actions on card expiration dates; others will work to get chip cards into the consumer's hands as soon as possible.
"I think the progress we are seeing today is promising," said Chiro Aikat, senior vice president of product delivery (EMV) at Mastercard.
What about mobile payment readers?
EMV-compatible card readers for Android and iOS devices that can read contactless mobile payments and process dipped chip cards. Merchants can purchase the devices for $49.
7. If I want to use my chip-card at a retailer that doesn't support EMV technology yet, will it work?
Yes. The first round of EMV cards — many of which are in consumers' hands — will be equipped with both chip and magnetic-stripe functions so consumer spending is not disrupted and merchants can adjust.
See Related
10 tips to improve your credit score in 2020
How to send, receive money using Venmo
If you find yourself at a point-of-sale terminal and are not sure whether to dip or swipe your card, have no fear. The terminal will walk you through the process.
"For example, if you enter a card into the chip reader slot but the reader isn't activated yet, it will come up with an error and you'll be prompted to swipe the card in order to use it," Vanderhoof says.
And vice-versa.
"If a consumer tries to swipe a chip card instead of inserting it, an error will appear and they will be prompted to insert the card for chip processing instead," Vanderhoof says.
If chip-card readers are not in place at a merchant at all, your EMV card can be read with a swipe, just like a traditional magnetic-stripe card.
"You can still conduct transactions, you just lose that extra level of chip security," Johnson says.
See Mag stripe begins its farewell tour for details about the move away from magnetic stripe cards.
While many large retailers, such as Walmart, Target and Costco, have upgraded their POS terminals and have activated them for chip card acceptance, many U.S. retail locations are still not EMV-ready.
Visa says 50 percent of U.S. stores (approximately 2.3 million merchant locations) now accept chip cards, as of June 2017. Mastercard has tallied 2.3 million chip-active merchant locations on its network, representing 38 percent of all U.S. merchants back in October 2016. Industry experts expect the merchant migration to slowly continue over the next few years, especially as the remaining liability shift dates get closer.
"I expect to see a slow and gradual increase of merchant moving over, just like we've seen up until today, as each merchant makes an assessment if this expenditure makes sense for them and their particular business," said Mallory Duncan, senior vice president and general counsel at the National Retail Federation.
In July 2017, U.S. Payments Forum estimated 45 to 50 percent of U.S. credit and debit card transactions were chip-on-chip.
See "7 merchant tips to understanding EMV fraud liability shift" for answers to additional liability shift questions.
8. Will I be able to use my EMV card when I travel outside the country?
The U.S. is the last major market still using the magnetic-stripe card system. Many European countries moved to EMV technology years ago to combat high fraud rates. That shift has left many U.S. consumers who have magnetic-stripe cards looking for other forms of payment when they travel.
Since many foreign merchants are wary of magnetic-stripe cards, consumers who hold some type of chip card may run into fewer issues than those without one, according to Ferenczi.
"Just the existence of the chip will likely make European merchants more willing to accept transactions that they wouldn't have likely accepted if a customer presented a mag-stripe card," he say, which is good news for traveling U.S. cardholders.
However, chip-and-PIN cards are the norm in most other countries that support EMV technology. So consumers with chip-and-signature cards may find some merchants who are unwilling or unable to process their card, even though it does have an embedded chip.
But despite any difficulties in the transition, Ferenczi says the change is a step in the right direction.
"Nobody likes to think that his or her card is being secretly used for other purposes," he says. "So I think regardless, there is a level of comfort knowing that it will be far more difficult to counterfeit EMV cards."
See related:EMV holdouts: Why merchants are slow to make chip-card switch?, Cardless cash ATMs aim to reduce fraud, The big chip card switch: a year of living with EMV
In Innovations and Payment Systems
You may soon be able to buy lottery tickets at grocery checkout
States have to approve shoppers using smartphones linked to credit or debit cards to purchase Powerball and Mega Millions chances.


text:
\section{Introduction}

The need for change point detection arises in many contexts; such as computer network intrusion detection~\citep{Yamaishj2002}, meteorological events~\citep{Reeves2007},  and speech recognition~\citep{Rybach2009}. Change point detection is challenging due to a number of factors including noise in the underlying data, nonlinearity in the time-series, and the (generally) unknown generating distributions.

The setting we study here involves systems that generate data we can observe as a time-series. The exact parameters and model of the systems involved are generally unknown but we are aware that the system transitions from one state to another. The change point detection problem is determining when (in time) the transition between states occurs.

\begin{figure}[h]
	\centering
	\includegraphics[width=1\columnwidth]{signal_states_transition.png}
	\caption{Scalp EEG recording of a patient experiencing a seizure. The time series captures the transition of the patient from the non-seizure state (State A) to the seizure state (State B). The change point detection problem is determining the time at which the transition occurs.}
	\label{sig_states}
\end{figure}

\subsection{Problem Definition}

The variant of the change point detection (CPD) problem considered here is stated as follows. Given a set of time series $\{X_i\}_{i=1}^n$ where each $X_i$ is a set of observations, or feature vectors, at discrete time steps $X_i=\{x_1,x_2,...x_{T_i}\}$ with $x_t \in \mathbb{R}^d$, we assume each $X_i$ is generated by a system that undergoes a transition from state $A$ to state $B$ as shown in Figure~\ref{sig_states}. The CPD problem is determining the time at which the transition occurs in the time series $X_i$ represented by a time $\tau_i$. Suppose $P_A(x)$ and $P_B(x)$ are the unknown probability distributions of the observations over the space $\mathcal{X}=\mathbb{R}^d$ and that $P_A(x) \ne P_B(x)$. A change point is defined as a time $\tau$ such that:

\[\{x_1,x_2,...,x_\tau\} \sim  P_A (x)\]
\[\{x_{\tau+1},x_{t+2},...,x_{T_i} \} \sim P_B(x)\]

If the distributions $P_A(x)$ or $P_B(x)$ are known or easy to estimate, the CPD problem can be reduced to a simple hypothesis testing framework. Because this is not often the case, an array of methods have been developed for the CPD problem.

The rest of this paper is organized as follows. In Section~\ref{cpd_prior}, we review change point detection methods. In Section~\ref{cpd_dre} density ratio estimation and its application to change point detection is discussed in more detail. Section~\ref{cpd_ddre} introduces density ratio estimation using deep neural networks and two novel objective functions for training networks for density ratio estimation. The empirical results are presented in Section~\ref{cpd_results}. We discuss the results and conclude in Section~\ref{cpd_disc} and Section~\ref{cpd_conc} respectively.

\section{Related Work}
\label{cpd_prior}
Early work on CPD posed the problem as comparing the probability distributions of time series over successive intervals to detect changes~\citep{Basseville1994}. A hypothesis testing framework was typically adopted with the null hypothesis of "no change" and the alternative hypothesis of "a change occurred". Examples of this approach include the cumulative sum (CUSUM) algorithm~\citep{Basseville1994}, the generalized likelihood ratio~\citep{Gustafsson1997}, and likelihood detectors~\citep{Kuncheva2013}. The CUSUM algorithm calculates a cumulative sum of deviations from a baseline model over a time series and detects a change point when the sum passes a threshold. The generalized likelihood ratio test assumes a linear state space model and uses the likelihood ratio instead of the likelihood for hypothesis testing. Likelihood detectors estimate the probability distributions with Gaussian mixtures. These methods work well in single or low dimensional spaces, but are unable to detect change points in high dimensional time series.

Parametric methods relying on model assumptions such as auto regressive (AR) models ~\citep{Yamaishj2002} and subspace models~\citep{Ide2007} have also been proposed. The change finder ~\citep{Yamaishj2002} measures deviations from an AR model with linearity assumptions to detect outliers and change points. Subspace methods~\citep{Ide2007} detect change points using distances between the subspaces of past and present windows estimated with principal component analysis. These methods demonstrate success for change point detection but are ineffective when model assumptions are violated.

Kernel methods for estimating the probability densities over consecutive intervals are an example of non-parametric models for change point detection. The kernel Fischer discriminant ratio has been proposed to detect change points~\citep{Mika1999,Harchaoui2009}. Kernel Mean Matching~\citep{Gretton2009} matches the moments of the distributions in a reproducing kernel Hilbert space with a kernel, such as the Gaussian kernel, and measures the distances between the distributions over separate intervals. These methods rely on the choice of kernel function instead of a pre-defined model but are still inaccurate when the feature space is high-dimensional~\citep{Vapnik1998}.

Attempts to circumvent density estimation in high dimensional spaces with non-parametric methods have focused on using kernels to estimate the ratio of the distributions instead of the probability distributions themselves. This line of research, called density ratio estimation, is motivated by the idea that since knowing the densities of two intervals implies knowing the density ratio but that knowing the ratio does not imply knowing the individual densities, estimating the ratio is an easier problem~\citep{Kawahara2012}. The KL divergence based importance estimation procedure (KLIEP) ~\citep{Suigyama2007,Kawahara2012} is one example of this approach. KLIEP estimates the density ratio of two consecutive samples using Gaussian kernels and detects change points when the density ratio exceeds a threshold. A similar approach is proposed with the unconstrained least squares importance fitting (uLSIF) and relative uLSIF~\citep{Liu2013}.

Virtual classifier based methods use classification algorithms to detect change points in time series~\citep{Desobry2005,Hido2008}.
This method has been extended to feature selection~\citep{Yamada2013} using the Hilbert Schmidt Independence Criterion for feature ranking. These methods show promise for dealing with change point detection in high-dimensional spaces. Drawbacks include the prohibitive computational cost for training and testing a classifier for each candidate change point as well as a strong dependence on the feature space of the time series.

In this work, we improve density ratio estimation by using deep neural networks as the density ratio function approximator. This approach leverages the layered representation learning capabilities of deep networks to learn better approximations to the density ratio. The improved density ratio estimation results in better performance in change point detection.

\section{Density Ratio Estimation}
\label{cpd_dre}
The ratio of two probability densities arises in many contexts such as Monte Carlo importance sampling and covariate shift detection. The ratio is useful for change point detection when it is taken between the distribution of the feature vectors $x$ over the state space $\mathcal{X} = \mathbb{R}^d$ conditioned on the state $B$ and the state $A$: \[ \beta = \frac{P(x|B)}{P(x|A)} \]

For a time series, the density ratio $\beta_t$ of a sample $x_t$ measures the likelihood the sample comes from the distribution of state $B$ vs. the distribution of state $A$. When viewed as a weight corresponding to each sample, a high value for $\beta_t$ means the sample is likely to be from $B$ and a value close to zero indicates the sample is from $A$. In the ideal case, if a change from state $A$ to state $B$ occurs in a time series $X_i$ then the $\beta$ values act like a step function over time where the step occurs at the change point.

The challenge with this approach is that $\beta$ is not easy to compute. Directly estimating the probability densities $P(x|A)$ and $P(x|B)$ is difficult because of the general difficulty of density estimation in high dimensions. Since we only require the density ratio, estimating the conditional probability densities is actually over-solving the problem. We can avoid this by attempting to estimate the density ratio directly with a parameterized function and optimizing the parameters to match the true density ratio. Suppose we estimate the density ratio with a function $f(x;\theta)$ with parameters $\theta$: $ f(x;\theta)\approx \frac{P(x|B)}{P(x|A)} $

The parameters $\theta$ can be optimized by noting that $P(x|B)=P(x|A)f(x;\theta)$ and minimizing the KL divergence between $P(x|B)$ and $P(x|A)f(x;\theta)$, as in the KLIEP method~\citep{Suigyama2007}:

\[
\min_\theta{\mbox{KL}(P(x|B),P(x|A)f(x;\theta)}
\]
\[ \min_\theta{ \sum_{x\in\mathcal{X}}{P(x|B)\log{\frac{P(x|B)}{P(x|A)}}}-\sum_{x\in\mathcal{X}}{P(x|B)\log{f(x;\theta)}} }\]

Since the first term does not depend on $\theta$, this can be rewritten as a minimization over the second term:

\[ \min_\theta{ -\sum_{x\in\mathcal{X}}{P(x|B)\log{f(x;\theta)}} }\]

Equality constraints are used to ensure $P(x|A)f(x;\theta)$ represents a valid probability distribution:

\begin{equation*}
	\sum_{x\in\mathcal{X}}{P(x|A)f(x;\theta)} = 1
\end{equation*}

The function used to approximate the density ratio can be any parameterized function, such as a weighted linear combination of kernel functions:
\[f(x;\theta)=\sum_{i=1}^b{\theta_iK(x_i,x)}\]
In the case of functions which are linear in the parameters $\theta$ the optimization problem is convex. For a neural network approximator $f(x;\theta)=\sigma(\theta_1 \sigma(\theta_2 x))$ (with nonlinear activation function $\sigma(\cdot)$) the optimization is not straightforward.

Estimating the density ratio for change point detection on a dataset of time series requires using the empirical estimates for the summations expressed above as well as splitting each time series into reference and evaluation segments. Each time series is split by assuming the system starts generating the time series in state $A$. Thus, the first $n_{r}$ samples of each time series are labeled as being generated in state $A$. The remaining $n_{e}$ samples of each time series are unlabeled, belonging either to state $A$ or state $B$. This setting is referred to as semi-supervised or positive/unlabeled data in which only the reference samples are labeled (i.e. $n_r$ is given). The task is to determine the label of the samples in the evaluation segment as well as previously unseen samples.

After each time series is split into reference and evaluation segments, the optimization problem can be expressed as:

\begin{equation}
	\begin{split}
		\min_\theta{ -\frac{1}{n_{e}} \sum_{i=1}^{n_{e}}{\log{f(x;\theta)}} } \\
		f(x_j;\theta) > 0 \forall j=1...n_{r} \\
		\frac{1}{n_{r}} \sum_{j=1}^{n_{r}}{f(x_j;\theta)} = 1  \\
	\end{split}
	\label{kliep_eq}
\end{equation}

Density ratio estimation provides a number of advantages over other methods when applied to change point detection. Each sample in the evaluation segment is given a weight that represents how different the sample is from the samples in the reference distribution without testing multiple possible change points. This is similar to methods for detecting covariate shift between training and test datasets, where each sample in the training dataset is given a weight signifying how similar or different it is to the test dataset distribution.

A drawback to the density ratio estimation methods presented here is that, as referenced earlier, it is not easy to optimize the parameters of the density ratio approximator when the objective is a non-linear function of the parameters $\theta$. This is the case when using a deep neural network to approximate the density ratio. The motivation to use a deep neural network for the density ratio approximator is that the layers in the neural network allow learning representations of the data at the same time as optimizing the objective. In the next section, we describe modifications to the above formulation that allows approximation of the density ratio with a deep neural network.

\section{Deep Density Ratio Estimation}
\label{cpd_ddre}
One of the attractive qualities of the virtual classifiers method for change point detection is that it allows the optimization of both the feature space and the change point through the use of deep neural networks. However, this comes at a large computational cost because of the need to test multiple candidate change points. Density ratio estimation on the other hand, can directly weight evaluation samples based on their similarity to a reference distribution. The drawback to this is that the feature space flexibility is limited. We attempt to combine the strengths of both of these methods using deep neural networks to approximate the density ratio.

The main challenge to this approach is selecting an appropriate objective function to train the network to approximate the density ratio. The objective given in Equation~\ref{kliep_eq} cannot be directly optimized by a network because of the constraints. Training a good neural network based approximator to the density ratio requires an objective function that satisfies the constraints and can be optimized using minibatch stochastic gradient descent.

\subsection{LSIF Objective}

This approach has been explored in~\citep{Nam2015} using a modified version of the least squares importance fitting (\textbf{LSIF})~\citep{Liu2013} objective function to train a convolutional network to approximate the density ratio. This formulation of the objective function is given by:

\begin{equation}
	\frac{1}{n_{e}} \sum_{j=1}^{n_{e}}{f(x_j^{e};\theta)^2} - \frac{2}{n_{r}} \sum_{i=1}^{n_{r}}{f(x_i^{r};\theta)}
	\label{cnn_lsif}
\end{equation}

In the formulation given in ~\citep{Nam2015}, the network is trained with pairs of reference and evaluation samples. However, this results in minibatch sizes too small to train on large datasets. We show in our experiments that using larger minibatch sizes with this objective function results in poor performance for change point detection.

We propose two new formulations of the density ratio estimation objective that allow approximating the density ratio with a deep neural network (DNN).

\subsection{DSKL Objective}

Our first formulation uses the KL-divergence in both directions, also referred to as the JS-divergence, to avoid the equality constraints. This is motivated by considering the behavior of the density ratio estimator when the equality constraint in Equation~\ref{kliep_eq} is dropped:

\[ \min_\theta{ -\frac{1}{n_{e}} \sum_{i=1}^{n_{e}}{\log{f(x;\theta)}} }  \]
\[ f(x_j;\theta) > 0 \forall j=1...n_{r} \]

Since the objective only depends on the evaluation samples, $f(x;\theta)$ is free to assign arbitrarily large values to the density ratio for the evaluation samples with no regard to the effect on the reference samples. To avoid this, we consider minimizing the KL-divergence between $P(x|B)$ and $P(x|A)f(x;\theta)$ and the KL-divergence between $P(x|A)$ and $\frac{P(x|B)}{f(x;\theta)}$. This yields:

\[ \min_\theta{ \mbox{KL}(P(x|B),P(x|A)f(x;\theta))+ \mbox{KL}(P(x|A),\frac{P(x|B)}{f(x;\theta)}) } \]

\[ \min_\theta{ \sum_{x\in\mathcal{X}}{P(x|B)\log{\frac{P(x|B)}{P(x|A)f(x;\theta)}}}
+\sum_{x\in\mathcal{X}}{P(x|A)\log{\frac{P(x|A)f(x;\theta)}{P(x|B)}} }  }
\]

Dropping the terms irrelevant to the optimization because they do not depend on $\theta$:

\[
\min_\theta{
	-\sum_{x\in\mathcal{X}}{P(x|B)\log{f(x;\theta)}}
	+\sum_{x\in\mathcal{X}}{P(x|A)\log{f(x;\theta)}}
}
\]

Replacing the summations with the empirical estimates and assuming a reference and evaluation split yields the \textbf{DSKL} objective:

\begin{equation}
	-\frac{1}{n_{e}}\sum_{i=1}^{n_{e}}{\log{f(x_i;\theta)}}
	+\frac{1}{n_{r}}\sum_{j=1}^{n_{r}}{\log{f(x_j;\theta)}}
	\label{dskl_obj}
\end{equation}

The function $f(x;\theta)$ is an approximator to the density ratio, and therefore it does not represent a valid probability distribution. The density ratio is nonzero but unbounded from above. In this minimization problem, we are concerned with both samples where $f(x;\theta)<1$ (where the probability distribution in the denominator is larger than the distribution in the numerator) and where $f(x;\theta)>1$.

The objective function in Equation~\ref{dskl_obj} can be minimized using full-batch gradient descent to optimize the parameters of the neural network. When using minibatch gradient descent or stochastic gradient descent the minibatch size must be optimized as a hyperparameter. Approximately equal numbers of reference and evaluation samples should be included in each batch.

\subsection{BARR Objective}

The second formulation uses a different approach to handle the constraints in Equation~\ref{kliep_eq}. A Lagrange multiplier is used to incorporate the equality constraint into the objective function as a barrier term. The original objective function can then be rewritten as the \textbf{BARR} objective:

\begin{equation}
	-\frac{1}{n_{e}}\sum_{i=1}^{n_{e}}{\log{f(x_i;\theta)}}
	+ \lambda \left| \frac{1}{n_{r}}\sum_{j=1}^{n_{r}}{f(x_j;\theta)} - 1  \right|
	\label{barr_obj}
\end{equation}

The Lagrange multiplier $\lambda$ plays the familiar role of a regularizer in this formulation. Choosing the value for $\lambda$ can be guided by the same heuristics used to choose regularization parameters for the model. Optimizing this objective by minibatch or stochastic gradient descent does not guarantee the equality constraint will be satisfied, especially when $\lambda$ is small. For our experiments, the value of $\lambda$ was fixed at 10.

We propose using a neural network to approximate the density ratio and tune the parameters of the network using each of the objective functions. This will allow learning a feature space at the same time as estimating when the system transitions from one state to the next.

\section{Results}
\label{cpd_results}
In our experiments, we compared the change point detection performance of the proposed method, called the \textbf{deep density ratio estimator (DDRE)} trained using the objective functions given in Equations~\ref{cnn_lsif},~\ref{dskl_obj}, and ~\ref{barr_obj}, to two other popular density ratio estimation methods, KLIEP~\citep{Suigyama2007} and rULSIF~\citep{Liu2013} as well as other change point detection algorithms. We give a brief description of each change point detection (CPD) method tested in the appendix.

The metric used to compare methods is average detection lag, also known as average run length. For a given set of true change points $\{t\}_{i=1}^N$ and a set of corresponding predicted change points $\{\tau\}_{i=1}^N$, the average detection lag is $ADL = \frac{1}{N}\sum_{i=1}^{N}{|t_i-\tau_i|}$.

\subsection{Simulations}
Monte Carlo simulations were carried out to estimate average detection lag for three methods, KLIEP, rUSLIF, and DDRE. A gaussian distribution was used to model the underlying time series and the change point was modeled by a change in the parameters of the distribution. This experiment required two generating distributions; $P(x|A)$ and $P(x|B)$, the pre-change and post-change distributions respectively. We varied the dimensionality of the feature space ($D$) from 10-100. Each experiment was repeated for 20 iterations.

We used a multivariate gaussian distribution $\mathcal{N}(\mu_1, \Sigma_1)$ with $\mu_1 \in \mathbb{R}^D$ and $\Sigma_1 \in \mathbb{R}^{D \times D}$ randomly selected from a uniform distribution ($\Sigma_1$ was constrained to be a symmetric positive definite matrix) for $P(x|A)$. $P(x|B)$ was generated by perturbing the parameters of $P(x|A)$ by random amounts.

\subsubsection{Simulation results}
The deep density ratio estimator (DDRE) used in our experiments consisted of 5 fully connected layers with ReLU activations ~\citep{Nair2010} and 500 units in each layer except the last layer. The final output activation of this network is also a ReLU because the density ratio takes values in the range $[0,\infty)$. Dropout ~\citep{Srivastava2014} and L-2 regularization of $0.01$ was applied at every layer. The network was trained using the DSKL loss function (Equation~\ref{dskl_obj}). Mini-batch stochastic gradient descent was used with a batch size of 200 and an initial learning rate of 1e-3. In each experiment the network was trained until the validation loss stopped decreasing.

The average detection lags (ADL) for each method are shown in Figure~\ref{sim_res}, a lower ADL is better. Each of these figures shows the estimated ADL of KLIEP, rULSIF, and DDRE. We observed that the performance gap between DDRE and the other methods increased with the dimension of the feature space, indicating DDRE functions better than other methods in high dimensional spaces. This is due to the deep neural networks automatic feature extraction capabilities.

\subsection{EEG Dataset}

In the epileptic brain, seizures are characterized by abnormal firing of large networks of neurons. Since seizures can occur as random times, detecting the onset of a seizure is useful for patients, caregivers, clinicians, and even neuro-stimulation devices. The electrical activity of the brain can be monitored externally using a device containing electrodes placed on the scalp, called an electroencephalogram (EEG). The most widely used configuration of the electrodes on the scalp is known as the 10-20 system.

The EEG dataset contains 121 recordings with 22 channels sampled at 256 Hz each containing a single lead seizure. The EEG data is part of a publicly available dataset recorded at the Mount Sinai Hospital \footnote{\url{http://www.dsrc.rpi.edu/?page=databank}}. The data is provided in the European Data Format~\citep{Kemp2003}. Statistics of the dataset are summarized in the appendix.

\begin{figure}[h]
	\centering
	\begin{subfigure}{0.48\linewidth}
		\centering
		\includegraphics[width=\linewidth]{Simulation1.pdf}
		\caption{Shown are the simulation results for each of the three density ratio estimation methods. The deep density ratio estimator (DDRE) outperforms the other methods (lower ADL is better). Error bars show one standard deviation.}
		\label{sim_res}
	\end{subfigure}
	\begin{subfigure}{0.48\linewidth}
		\centering
		\begin{tikzpicture}[scale=0.95]
		\foreach \y in {1,1.4,1.8,2.2,2.6}
		\draw[fill=blue, very thick] (1,\y ) rectangle (2.5,\y+.3);
		\foreach \y in {1,1.4,1.8,2.2,2.6}
		\draw[left color=blue, right color=red, very thick] (2.5,\y ) rectangle (5,\y+.3);

		\draw (0.7,0.9) -- (0.5,0.9) -- (0.5,2.91) -- (0.7,2.91);
		\draw (0.4,1.905) node[left] {Train} -- (0.5, 1.905);
		\draw (1.2,2.9) node[above right] {ref};
		\draw (3,2.9) node[above right] {eval};

		\draw[shade, very thick] (1,.5 ) rectangle (5,.5+.3);
		\draw[shade, very thick] (1,.1 ) rectangle (5,.1+.3);
		\draw (0.7,0.09) -- (0.5,0.09) -- (0.5,0.86) -- (0.7, 0.86);
		\draw (0.4,.5) node[left] {Test} -- (0.5, .5);
		\end{tikzpicture}
		\caption{Diagram showing how the database of time series is split. Each time series contains a single change point. The reference segments represent sections of the time series known to be in state A. The evaluation segments represent sections of the time series with unknown state labels.}
		\label{setup}
	\end{subfigure}
	\caption{ }
\end{figure}

The data in each of our experiments consisted of a dataset of $N=121$ time series, each of which contains an actual change from a state $A$ (nonseizure) to a state $B$ (seizure). For each experiment the dataset of time series is first split into a test set (20\%) and a training set (80\%). Since all the recordings contain a minimum of 60 minutes of nonseizure activity before the seizure, the first 20\% of samples in each time series in the training set are used to create the reference segments. The remaining samples in each time series in the training set are allocated as the evaluation segment. The model is then trained on the reference and evaluation samples using cross-validation to select parameters. The resulting model is used to estimate the density ratios of the samples in the test set. This setup is shown in Figure~\ref{setup}.

When using the density ratio estimation approach, the model outputs the estimated density ratio for each sample in each time series in the test set. These are transformed into a change point prediction for each time series by fitting a sigmoid function such as the logistic function to the ratio values with parameters $k$ and $x_0$: $ y = -\frac{1}{1 + e^{-k(t-x_0)}} $
The fitted value of the parameter $x_0$ is used as the predicted change point. This method was adopted as the thresholding function for all the density ratio estimation methods. This is not necessary for window based change point detection methods as the algorithm directly returns the most likely change point.

\begin{figure}[h]
	\centering
	\begin{subfigure}[t]{0.48\linewidth}
		\centering
		\includegraphics[width=0.85\linewidth]{adl_box_plot.pdf}
		\caption{Average detection lag (ADL) scores (in seconds) for each method. 30 bootstrap resampling runs were used to construct box plots of the ADL. The red horizontal line shows the median ADL for each method.}
		\label{seizure_res}
	\end{subfigure}
	~
	\begin{subfigure}[t]{0.48\linewidth}
		\centering
		\includegraphics[width=0.75\linewidth]{minibatch_sizes.pdf}
		\caption{The effects of minibatch size on average detection lag for the three objective functions LSIF, BARR, and DSKL. The LSIF and BARR objectives were unstable for small minibatch sizes (10)}
		\label{minibatch}
	\end{subfigure}
	\caption{Empricial results on EEG dataset}
\end{figure}

\section{Discussion}
\label{cpd_disc}
We compare the performance of density ratio based change point detection methods in detecting seizure onset on this dataset. All methods except DCDRE were trained on 396 signal features extracted from the EEG. The features consisted of three energy measures (average energy, Teager-Kaiser energy, and line-length) computed on six frequency bands for each channel as described in~\citet{Cook2013b}. The convolutional neural network (CNN) was trained directly on the wavelet transform of the EEG signal.

The average detection lag for each method over 30 bootstrap resampling runs is shown in Figure~\ref{seizure_res}. The kernel based density ratio estimation methods (rULSIF and KLIEP) and window based change point detection methods (*CPD) achieve 31-42 median ADL (lower is better). Using kernel functions to approximate the density ratio did not show much improvement over window based change point detection algorithms.

DDRE-LSIF and DDRE-BARR, two deep density ratio estimation methods, perform much worse at 51-52 ADL. DDRE-DSKL outperforms the other density ratio estimation methods and window based change point detection method with a lower ADL of 25.
Figure~\ref{minibatch} shows the effect of minibatch size on ADL. Unlike the other two density ratio estimation objectives LSIF and BARR, the DSKL objective treats reference and evaluation samples equally allowing the network to update the density ratio on the evaluation samples using the (given) information about the reference set.

In all cases, the deep convolutional density ratio estimator (DCDRE) outperformed the other methods (16 ADL) but exhibited high variance across bootstrapping runs. This improvement is due to the ability to incorporate the feature extraction capabilities of the convolutional filters into density ratio estimation. The high variance of the deep density ratio estimators compared to the kernel based density ratio estimators is caused by the tendency of deep neural network optimization to converge to local optima.

\section{Conclusion}
\label{cpd_conc}
In this work, we develop novel objective functions for density ratio estimation using deep neural networks and compare their performance on a seizure detection task. Our proposed method uses a modified objective function suited to minibatch gradient descent training. We compare our method to two state of the art kernel-based density ratio estimation methods as well as change point detection methods. The results show a clear advantage to deep neural network based density ratio estimation methods especially when convolutional networks are used to learn feature representations.

\medskip
\small
\bibliographystyle{plainnat}

redpajama_set_name: RedPajamaArXiv


In the UK we are having an unbelievable spring, Loads of clear, sunny and warm days, it's more like high summer. If this is global warming I say "bring it on"!
Naturally it brings some problems with it, the current one being blizzards. There is so much pollen and other stuff coming off the trees that we are in an almost constant "snowstorm". We even had to wash the boat down today and found that it is dark green underneath.
The video I added last time was, it seems, well received. I have added to the video equipment and now have an HD video as well.
However, back to the plot, cruising. We are heading south down the Grand Union canal towards London at a gentle pace, intending to arrive there just before our plane takes off for the North American countries we are visiting in May. Having been on this stretch of water many times I am trying desperately not to repeat myself too much.
First we have to say goodbye to Milton Keynes, always a strange place. They sort of let their imaginations run into strange places, especially at the town centre. I'm not sure what they smoke there but I'd like to know where I could get some.
I'm not sure if this is a seat or a warning not to steal their library books but I've never seen anyone sitting on it.
Then there are the concrete cows that Milton Keynes is so famous for. These are the originals, there is a second herd out in a field somewhere. Actually I'm not sure that you can have a herd of concrete, perhaps we need a new collective noun.
Actually I much prefer these, much more cuddly, except for that mean looking one third from the right.
And then I was waiting for a bus and noticed these skid marks, I just could not work out what had happened. Anyone have any clues. It was a dual carriageway with the traffic going from right to left, at least in theory.
And then there are regional accents around, some people a little further north pronounce "bus" more as "buzz". The local bus company noticed and this is the result.
But, as usual I digress, back to the canal!
There are ducklings everywhere, everyone counts them so here's a dozen, to us they are known as either waterwalkers (because of the way they appear to run over the water) or simply bite sized.
and of course Milton Keynes version of Stonehenge, right by the canal. It makes you wonder how they got the stones there, did people drag them for miles on rollers , or did they just use a tipper truck?
This is also the time of year for sunken boats. Those who for any reason cannot look after their boat during the winter can find it is a little lower in the water than normal.
I get the feeling that fun fair type roller coasters are going out of fashion and those same steel fabricators are now churning out footbridges. These seem to be springing up all over the place.
I guess they are much easier for the disabled, and dogs, than this one.
Then there was the boat paint job that really amazed me! Not too many people would consider that their paint job should include the windows. I guess artists are just dedicated to their art. Mind you I'm pretty sure that is a beer keg on the roof. There may be an explanation.
Ok, now for the fun bit, the videos.
Firstly, you may have noticed that our boat is longer than the canal is wide. So how do we turn the boat? This video shows how it is done. There are places called Winding Holes where boats can turn. I assure you it;s much more fun if the wind is blowing. A boat turns on a point close to it's centre which means that you need room to swing the aft end, the boat moored on the left as we approach causes a major obstruction.
Secondly it is true that you often meet boats on blind bends, this is a blind bend with a bridge. Please note that boats should pass "port to port", or put more simply we are suposed to drive on the right.
The clicking noise is me moving from forward to reverse or vice versa. The screeching noise when the first boat draws level is the sound of of branches removing their paintwork.
And a short video I named "Duck".
One of the things I really love about the videos is the sound of the birdsong. We mostly don't notice it as it is just part of life for us, but luckily it comes over very clearly in the videos.
Lastly a link to an article about legging, used to get boats through tunnels in the days of horse drawn boats. The horse was led over the hill and the men "legged" the boat through the tunnel.
Next notes will be called "cruise control notes" as we will be visiting the US and Canada and I get to play with a car again, it will be a novelty. My favourite trick is to simply switch on the cruise control in order to accelerate away from a toll booth, well it makes life more interesting.
Don't worry we have booked The Beech Nuts into a very nice marina for while we are away where it can chat to the other boats to it's hearts content.
Also (hopefully) a report on Steven's cycle trip to Paris which was a great success. (That just means that he came back wiser than he left).


text:
Erich Graf von Bernstorff-Gyldensteen (26 June 1883 – 6 October 1968) was a German count and sport shooter who competed in the 1912 Summer Olympics.

He won the bronze medal in the clay pigeons team event. He also competed in the trap competition he finished 17th.

References

External links
profile

1883 births
1968 deaths
Counts of Germany
German male sport shooters
Shooters at the 1912 Summer Olympics
Olympic shooters of Germany
Olympic bronze medalists for Germany
Olympic medalists in shooting
Medalists at the 1912 Summer Olympics

redpajama_set_name: RedPajamaWikipedia


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"Behold the light of despair, the glare of anguish". It is so difficult to know where to start with this extraordinary work by composer Philip Venables. It is all too easy to say that it is an opera about depression and suicide and to express admiration for the bleak and uncompromising musical vision that Venables brings to Sara Kane's final play. It uses a musical language that is new to me. It combines singing, speaking, pre-recorded sounds and words, conventional modern orchestration and unconventional uses of instruments. The 'conversations' between different percussion instruments – as the words they 'speak' to each other are projected onto the white wall that forms the back of the performance area – are extraordinarily powerful. And we have six fine singers who occasionally sound like refugees from a forgotten opera by Strauss but who use their voices in many different ways – whispering, panting, keening, shouting – to bring to the audience this multi-layered message about suffering and psychosis.
As the action begins there is, disconcertingly, muzak playing from speakers while the orchestra, which is situated above the back of the stage, sit motionless. Six women emerge onto the stage, dressed identically, wearing blue jeans, black vests, and grey cardigans. There is no obvious main character – indeed, Kane made no direction about the names or numbers of performers. But for much of the piece the superb Gweneth-Ann Rand carries the burden of portraying a woman in the grip of depression, expressing the anger, despair, and terror that Venables distils from Kane's play. Some of it seems to be a very specific record of a psychiatric regime that tries desperately to find medical solutions to a condition that Kane seems to imply is beyond cold scientific solutions. The five other performers are either aspects of the sufferer or her friends …. or medical staff …. or figures from myth. All six singers are excellent – and the way they work as an ensemble as actors and singers is spell-binding. The spells may be evil but they are powerful.
There is a coherence and rhythm to the production that is difficult to pin down but credit goes to director Ted Huffman and conductor Richard Baker for guiding a performance that provides no easy dramatic or musical clichés to hang on to but is never less than gripping. The players are from the Chroma Ensemble who make the complex score seem rhythmic and even melodious at times. The look of the piece, which involves a simple white box with three doors and a range of high tech visual effects, are a tribute to the inventiveness brought to the production by the creative team – Hannah Clark, D M Wood, and Pierre Martin.
Some excellent points about this splendid piece which I saw last night. Firstly you mention the different kinds of sound world (using white noise as a cadence was fascinating for example). There was also a pre-classical side to the music. In one ensemble I was certain it heard the ABA form tho' I'm happy to stand correct and indeed the strophic form as well. But more importantly this is one of the few operas which takes on the learnings and vision of the post Beckett theatre with a beautiful humanistic spareness underneath the order which western sings must necessarily bring. But to do this Venables seems to be using C18 techniques like German melodrama as well as natural speech and art song. Venables strikes me as one of the few modern composers who elevates the dramatic requirements of his work over the desire to produce novel "sound".


Architectural Visualisation demo developed in 2016 in Unity for VR.
The demo allowed you to interact with various objects, change the color of the walls, carpets, floor, etc. The main idea was to let the user experience on how his house might look.
When we added the day & night cycle we could really see the beautiful light appearing on the window. We even made the posibility of opening/closing the courtains, and you could really see the room getting brighter/darker. Also, when the night comes everything goes dark gradually really setting a good vibe across the room. Then we implemented the ability to open the lights… and when it got to dark you could walk to the light switch, press a button and BAM! it was light in the room again.
Overall this was a pretty fun experience. It's really nice to look at a model of a real life object and see how you can make it interactible. It starts from a boring model to a model that can do stuff.


I have an issue with server 2008r2 and windows 7x86 - I recently upgraded from xp on a few machines and have been unable to get uvnc working correctly. I'm pushing uvnc with VNCremoteinstall.
I'm on domain connected between 2 locations with xp machines that work great..
I know i'm leaving something out..Thanks so much for your help!
Check out Item number 2 in the first post on this thread.
That seems to have corrected both - Thanks man your awesome!


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text:
Before Pontiac's desegregation began in August 1971, the Ku Klux Klan blew up 10 buses belonging to the Pontiac school system.
They Want Us Dead: US Education is a Practice of Inherent Violence
by CAM M
Published Thursday - November 30, 2020
From origins of exploited free labor from enslaved Africans to the eery connection between profit over people and corporate greed, the US education system is a white supremacist technology, tool, and weapon that interconnects and maintains a tradition to colonial orders of the status quo. The violence of the U.S. education system has operated for centuries by upholding white settler colonial interests in exploiting the labor of African communities around the world, fueling student complacency through a neo-liberal agenda that promotes economic mobility through class traitor politics as a unsustainable tactic to shift the material conditions of African people. This global system of power that threatens the path to Black liberation is also mirrored as a blueprint for universities around the world. This article is a call out to institutions of higher education for being byproducts of racialized terror who are actively working to maintain socio political domination through white-supremacist modalities. The examples used in the article will speak to the complexities and insidious bourgeois agenda setting in US institutions, through Black historical scholarship and contemporary scholarship, and visible in Historical Black Colleges and Universities (HBCUs).
Black upward mobility began as a tactic to shift the conditions of Black life in America but has consistently led to the prioritizing of white capital interests, becoming African greed, juxtaposed to moving towards Black liberation efforts. Many Black leaders have promoted Black intellectualism through legitimacy in academic education spaces. Still, this tactic towards Black upward mobility is not a powerful strategy if our genuine desire is global African liberation. Early Black exceptionalism rhetoric and politics in media and popular culture instilling a continuum of misogynoir and respectability politics impacts public interests through American exceptionalism propaganda. Much of the general desire toward Black economic mobility has always been rooted in white supremacy centered ideologies like respectability politics and agenda-setting propaganda that portrayed Africa as a poor and under resourced continent, when we know Africa to be a place of abundant resources and the birthplace of white obsessions like coffee. Throughout curriculums the Black radical thought an African global solidarity praxis felt hidden and in opposition of the American dream. This continued strategy for Black communities to escape racial terror by exploitative economic wealth practices like capitalism promotes a neo-liberal solution that centers Black middle-class communities as neutral and exempt from the harms of racial violence from centuries of exploitation beginning in African countries from white colonizers who even now continue to leave the people and land exploited. Governments worldwide use international-military practices, exploited labor, theft of resources, and white supremacy agenda-riden education systems that reinforce Black violence. Imperialist propaganda like ROTC (reserve officers' training corps) programs and military recruitment on high school and college campuses target Black and Brown (non-Black Latinx) communities to support one of the largest polluters in the world, the United States of America military. This continued fight for economic validation in Black communities continues through the idea of educational mobility, to push Black youth to accumulate hoards of debt, even though campuses continue to be contention spaces of racism and violence.
This essay is not stating that institutions like historically Black colleges and universities have not at all impacted our radical politics and fight towards Black liberation. Gloria Richardson, a former member of SNCC, Kwame Ture, *the architect of the term "Black Power", and Toni Morrison all attended Howard University and have been monumental to Black radical thought and radical imagination. But their radical efforts existed as less than the majority of the masses. Instead, this essay is a call to action for professors, students, and administration to do some internal reflections, urge classes to study, and develop communism education models, while many others should disband and relinquish their wealth and spaces to African liberation efforts. Academic spaces, especially those who make claims to promote diversity and inclusion, should interrogate their own policies from academic probation to zoom structures which promote exclusivity and ableism and see the very apparent resemblance in violent white supremacy practices that include stifling the efforts of African solidarity through abusive practices.
The solution to Black oppression in a global context will never reach resolution by changing the face of the person in a ruling class position. Barack Obama is a constant example of the harms that come from Black folks who do engage in positions of power and replicate the same state-funded violence that previous white oppressors have been a part of. Academic related violence impacts gender-oppressed communities like those who are women, femme, gender non-comforming, gender non-binary, and agender communities. Patriarchal violence is constantly violent and visible on college campuses, including exclusively Black spaces. Obama launched his "It's On Us" campaign against sexual assault on U.S. campuses in 2014, which lacked actual bystander intervention and seemingly made the bystander responsible for combatting sexual assault instead of pushing masculine people, especially fraternity members to engage with the harmful ideologies of patriarchy and patriarchal violence. Academia has fueled gender-based violence, as well as the influence of elitist Black scholarship.
Dubois did not just promote harmful colorists and classists discourse, but he also advocated for early eugenics practices with Planned Parenthood founder, Margaret Sanger. DuBois promoted the Talented Tenth which had many implications on the experiences of Black life, including colorism (paper bag test, which many argue was a myth but the photos never lie), classism, and misogynoir. Dubois served on the "Negro Project" board for Margaret Sanger and proudly stated that her early eugenics birth control efforts were "science and sense applied to the bringing of children into the world.", in his October 1922 Crisis opinion piece that also referred to his own wife as a "servant", among other "bootstraps " rhetoric. Ten years later, DuBois apologized for his early and energetic support for the sterilization of Black birthing people. Sharing similar but different misogynoir ideologies, Marcus Garvey's Black nationalist association, passed a resolution that shamed birth control and argued it went against "natural functions". This harmful rhetoric is often taught to a new class of Black study students each year, and continues to fuel the hotep language against reproductive rights. Early eugenics and sterilization efforts are white supremacy genocide tactics, and DuBois alignment with violence against Black birthing folks should be more widly addressed and used as an example of our constant need to seek deeper understanding and center the fight against patriarchal violence in every space. Access to abortion care is every persons right, and accessible abortions strengthens the abilities for communities to be liberated. There seems to be a pattern in the actions that show early attempts of misogynoir and elitism as legitimate ideologies to move toward. A contemporary example of the complexity and infectious of the abuse of Black academics is from Nigerian author Chimamanda Ngozi Adichie, who has served as a guest professor and speaker at colleges and universities around the world. Adichie recently doubled down on her anti-Black and transphobic politics, in her defense of JK Rowling's disgusting comments. What Adichie did was use her position as a Black intellectual to run to the defense of white ignorance and white-hetero eugenic culture, while also weaponizing her proximity to power and the state towards American individualism. Gender oppressed communities are impacted by intersections of violence in academia spaces, including being sexually assaulted or killed by campus police and face exploitation by Black women intellectuals and instructors.
And with a global pandemic about to reach its 3rd peak, most college campuses have chosen profit over people and continue to overcharge tuition, require students to attend in-person class, and continue the same capitalistic-imperial behaviors via zoom calls by requiring students to complete hybrid classes and adding additional policing policies like requiring students wear shoes while at home on zoom calls. At University of Michigan, in September 2020, Black graduate students led a strike against The Graduate Employees' Organization in support of safer conditions for students during a global pandemic and were met with a potential lawsuit by the University for asking for safe classroom environments. In a recent statement after a COVID-19 breakout on campus, the group said "We don't want to be right-we want to be safe." When students use their rights to combat violence at the academic level, they are met with threats from the state, even if it may be an attempt to keep all students safe. But these issues do not just happen at PWIs (predomantly white institutions), HBCUs are also colonized spaces and continue to reinforce colonialism and white power dominance.
Since the inception of HBCUs, these institutions have been an integral part of the long history and fight toward upward mobility for Black folks establishing legitimacy within academic and Black political power. HBCUs for centuries have been educational spaces for many Black leaders and acted as containers to promote Black liberation. The ugly reality is that these historically Black colleges and universities as institutions have not helped to further the Black radical agenda (though students have worked to continue to preserve radical politics of previous scholars) or support the efforts of student led groups who have tried to push these institutions through student organizing toward deeper understanding to show up for all Black communities. Howard University (named after a colonial white military officer), is one of the most well known HBCUs and prides itself of its ability to strengthen students through a social justice practice but years later, after the HU Resist student occupation, many of those demands still have not been met. These Howards students, who organize for Black liberation were asking for resources like adequate mental health services and for professors to use students correct pronouns. And many may still be unaware of the Koch Brothers donating $25 million to the UNCF in 2014, which went to numerous HBCUs including Howard. With connections to Donald Trump and the climate pollution crisis, the Koch Brothers are white supremacist bad actors who continue to fuel military defense, alt-right presidental campaigns, who defended Brett Kavanaugh while survivors spoke out against him, and have paid millions in discrimination and sexual assault lawsuits from employees. Howard still supports the Koch Brothers industries through their insidious partnership with Georgia-Pacific whose logo can be found on almost every bathroom fixture around the campus. A white supremacy company with funding and merchandise ties to HBCUs should worry us all, and with the recent donation of $40 millions dollars from MacKenzie Scott (Jeff Bezos ex-wife), we should all be concerned about economic interests growing in HBCU investments from white capitalists.
Issues like patriarchal violence exist because of the capital-interests that fail to address the conditions of violence gender-oppressed students consistently face. These instances continue to happen at Universities in Africa, in countries like Ethiopia, South Africa, and Tanzania. With Jessica Ott titling an article "The university of patriarchy" and describes the problem as "rooted in the patriarchal structure inherent in the University of Dar es Salaam and Tanzanian universities more broadly, which reflects societal power relations." And while colleges and universities continue following the US education systems response to combating harm, gender-oppressed folks will face the most vulnerability in academic spaces.
And now all around the nation, students in the U.S. have organized rallies and protest to voice their solidarity with African siblings around the world who are fighting against military forces, policing, and SARS who have all worked together to push Anti-Black violence with the priority for the white ruling class, and those who abandon Black liberation for a fictitious shot at Black bourgeois without reckoning with their own exploitative behaviors in the complex of global exploitation and genocide. And with little US media coverage, which is a tactic in itself to silence global movements, youth in Nambia and South Africa have taken to the streets to speak against femicide and sexual violence. HBCUs have not publicly stood against and of the global terror of imperialism and policing, and showcase their silence as complacency. While most universities (HBCUs) still prompt ROTC and campus police involvement, even after the national call to defund the police, Black radicals are very aware of what the prioritized interests universities like Howard University fall on. Ironically enough, Black youth who join the military to escape poverty in the U.S. could be sent to the Congo to provide U.S. military aid to further exploit African land.
While any education system continues to honor anti-Black violence over Black radical scholarship, it must be held accountable, resources redistributed, and abolished for its longtime role as a tool for white supremacy. The current reality of the global education system that has centered US education as a model of success targets the most marginalized communities, like those gender-oppressed, working class, and poor communities who continue to be the most impacted by state-sanctioned violence in the academy. All extensions of academia and university culture, including; Black scholars and scholarship, HBCUs, Black capitalist wealth propaganda, and police presence on campuses all connect to the harmful practices that are reductive and undermining to the Black radical movement and the fight for Black liberation.
How did the U.S. become the richest nation in the world, where its educational system has hoarded wealth mirroring the same harmful practices of capital-imperial structures, while African communities (even those who receive degree(s)) continue to be exploited by the state? In a nation like the U.S., where ironically enough degrees continue to deplete in value, we should be moving towards a liberation politic that is rooted in global solidarity and not individualism or exceptionalism. From capitalist-labor exploitation like overworked and underpaid employees (including interns) on college campuses, to abusive hierarchy practices, anti-Blackness will continue to be inherent in academia everywhere. Black organizations and HBCUS should be educating, mobilizing, and politicizing Black students to combat institutional and state funded violence that many if not most college campuses are already intermingling in. Black studies departments must end the push of non-violent co-optation onto students and students should not collaborate with school officials (through committees that serve as distractions) instead of demanding what students currently need, like proper funding for Black studies departments instead of threats of closure(erasure) and cuts every year. African students should be building communities together and gaining skills in transformative justice and Black radical organizing practices.
In historical moments of continual violence by the government towards Black communities in the African diaspora, education spaces should empower youth to combat white supremacy, patriarchy, and oppressive conditions instead of promoting presidential election voting or economic mobility in constant instances where Black life is under attack. And while white militia presence continues to exist on all campuses, the threat of violence is visibly apparent. Universities should redistribute their wealth, support local community mutual aid groups with plans to abolish their infrastructures to adopt communist models of learning and building community power, and give their land to indigenous communities for land and food sovereignty.
Editors note: *although popularized by Kwame Ture, "Black Power" was originally coined by Mukasa Ricks
View all posts by CAM M
The hood leads the revolution
by Gazi
Published Thursday - September 17, 2020
For Toyin Salau
by Onyesonwu Chatoyer
Published Thursday - June 25, 2020
Aren't We Tired Of The Obamas Yet?
Published Thursday - May 9, 2020
Posted in Blog, Opinion PiecesTagged black educators, Education, Higher Educatoin, Neo-Colonialism, Political Education
My Wise Country Cousin on Unca Jim Clyburn an de Sharp Rite TurnBlack Misleaders Back Susan Rice as Top Diplomat


text:
Jiří Kodet (* 6. Dezember 1937 in Prag; † 25. Juni 2005 ebenda) war ein tschechischer Schauspieler.

Leben
Der Sohn der Schauspielerin Jiřina Steimarová und Bruder von Evelyna Steimarová begann seine Theaterkarriere in Pardubice, bevor ihn sein Kollege Jan Kačer nach Ostrava und schließlich nach Prag holte. Seit den 50er Jahren spielte Kodet in einigen Dutzend Filmen und Serien, unter anderem in Jiří Menzels Liebe nach Fahrplan (1966). In den 90er Jahren wurde er für seine Rolle in Pelíšky mit dem Český lev für die beste Hauptrolle ausgezeichnet. Dem deutschen Publikum war er auch aus den Serien Pan Tau (Rolle eines Piloten) und Das Krankenhaus am Rande der Stadt bekannt. In der tschechoslowakischen TV-Serie Die Besucher (1983) spielte er den Karussell-Ede und in der TV-Kinderserie Die Märchenbraut einen Redakteur beim Fernsehen. Zuletzt spielte Kodet die Rolle des Regisseurs František Vláčil in Sentiment (2003).

Filmografie (Auswahl)
 1960: Das höhere Prinzip (Vyšší princip)
 1963: Von etwas anderem (O necem jinem)
 1964: Das Attentat (Atentat)
 1966: Liebe nach Fahrplan (Ostre sledované vlaky)
 1975: Der Tag, der die Welt veränderte (Atentat u Sarajevu)
 1976: Ein bißchen schwanger (Hra o jablko)
 1980: Signum Laudis
 1980: Nur so ein bißchen vor sich hinpfeifen (	Jen si tak trochu písknout)
 1981: Bulldoggen und Kirschen (Buldoci a tresne)
 1982: Die salzige Rose (Slona roza)
 1982: Flüchtige Begebenheiten (Plache priheby)
 1982: Himbeercocktail (Malinovy koktejl)
 1983: Das Blaue vom Himmel (Modre z nebe)
 1983: Tödliche Motive (Smrt talentovanémo sevce)
 1985: Razzia (Zátah)
 1986: Die Operation meiner Tochter (Operace me dcery)
 1986: Eine zauberhafte Erbschaft (Carovné dedictví)
 1987: Der Narr und die Königin (Šašek a královna)
 1993: Accumulator 1
 1999: Kuschelnester (Pelíšky)

Weblinks

 Ausführliche Biografie von Jiří Kodet (cs.)
 "Sentiment" (2003) (cs.)

Filmschauspieler
Theaterschauspieler
Darstellender Künstler (Tschechoslowakei)
Darstellender Künstler (Prag)
Träger der tschechischen Verdienstmedaille
Český-lev-Preisträger
Tscheche
Geboren 1937
Gestorben 2005
Mann


BMW isn't opposed to offering a Competition Package for its M-branded SUV models, including the X5 M and X6 M.
During an interview with Auto Guide on the floor of the LA Auto Show, BMW M boss Frank van Meel said the popularity of Competition Package variants of other M models means the firm's SUVs could get the same treatment.
"Well Competition is a strong growing market in demand, and that goes for every M. So that's also a feature we're going to use more often in the future. And that does not exclude SUVs," van Meel said.
BMW first introduced its Competition Package to the previous-generation M5 and M6 a few years ago. Since then, both the BMW M3 and M4 have been offered in Competition Package guise and both models have proven popular.
If the company does decide to equip one of its M models with the same performance upgrades, it would likely do so on the next-generation X5, tipped to arrive in 2018. Like other Competition Package-equipped models, it could receive engine modifications, suspension tweaks, and new branding.


Mongolian Beef – JUST like P.F. Chang's Mongolian Beef if you drain the excess sauce and serve it simi-dry OR keep the excess sauce and spoon it over rice – the sauce is SO good.
Baked Catfish: 2 tblsp. minced fresh parsley 1 tsp. salt ¾ tsp. paprika ½ tsp. dried thyme ½ tsp. dried oregano ½ tsp. dried basil ½ tsp. ground black pepper 4 whole catfish fillets Combine: Juice of one lemon, 2 tblsp. melted butter, ¼ tsp. garlic powder and pour over catfish. Non-stick cooking spray Preheat the oven to 350 degrees. Bake 15-20 min.
Baked Catfish: 2 tblsp. minced fresh parsley 1 tsp. salt ¾ tsp. paprika ½ tsp….
The best fluffy vanilla cake you will probably ever eat.
I just made these!! They are super easy and taste awesome!! I will definitely be making them again!
Easy baked chicken dinner. Can use different veggies to change it up! 1 pkg. chicken breasts, 1 pkg. Italian dressing, new potatoes, veggies, 1 stick of butter. Place chicken in 9×13, put veggies on one side, potatoes on other. Sprinkle Italian seasoning, pour 1 stick melted butter on top. Cover with foil, bake at 350 degrees for one hour. Simple as that!
You mix in a box of vanilla instant pudding with a can of pumpkin and then fold in the cool whip. Easy as pie and tastes so good.
Patriotic Puppy Chow ~ There's nothing more American than red, white and blue. This puppy chow screams 4th of July and tastes like vanilla and strawberries. In my opinion there's nothing better.
Lemon Raspberry Cake w Cream Cheese Frosting= this is almost like mine and Casey's wedding cake!
BBQ time!! Super cute cupcake tutorial – Mini bbq GRILL tops. Steaks and hamburgers made with candy melts and kabobs with gummy bears. Too funny! This would be fun for Dad's Bday or Father's Day!!


text:
Government Power Was His Mortal Enemy
Each week, Mr. Reed will relate the stories of people whose choices and actions make them heroes. See the table of contents for previous installments.
"Sometimes standing against evil is more important than defeating it," wrote novelist N.D. Wilson. "The greatest heroes stand because it is right to do so, not because they believe they will walk away with their lives. Such selfless courage is a victory in itself."
In the last six of his 49 years of life, brought to an untimely end by tuberculosis, the classical liberal Frenchman Frédéric Bastiat produced an astonishing volume of books and essays in defense of free markets and free people. He towered over the smug intellectuals and politicians of his native France, most of whom were mentally mired in the country's ancient traditions of statist central planning of the economy.
"Life, liberty, and property do not exist because men have made laws," he reasoned. "On the contrary, it was the fact that life, liberty, and property existed beforehand that caused men to make laws in the first place."
Bastiat also gave us perhaps the world's most succinct description of the redistributive apparatus of government: "The State is the great fiction through which everyone lives at the expense of everyone else."
The world in the 21st century is beset with economic fallacies that are, for the most part, modern versions of those that Bastiat demolished 16 decades ago.
If a posthumous Nobel Prize were to be awarded to just one person for crystal-clear writing and masterful storytelling in economics, no one would be more deserving of it than Bastiat. Here is the great pity of his short time on this earth: while he lived and ever since, his own country never possessed the collec…
Being courageous is being scared, but taking the next step anyway.
One is apt to overestimate beauty when it is rare
-Mark Twain


Work with a team of experienced research engineers and project managers to assist in engine and emissions experiments, data acquisition, and analysis of results. Create reports (utilizing PowerPoint and MS Word) summarizing assigned tasks and data collected. Attend weekly meetings for assigned projects. Collaborate and Communicate with project managers on tasks status. Thrive in a team environment while supporting test data collection and analysis of test data. This is a year-round part-time, summer, or co-op position.
Must be pursuing a BS degree in Mechanical Engineering or related with an accredited university. Must have at least a 3.0 GPA. Must be approaching Junior status. Fundamental engineering training, with additional experience/knowledge in automotive systems is preferred. A strong interest in areas such as transportation, future automotive technology, and engines/powertrains, as demonstrated through course selection, student projects, or activities outside of school that required technical study and applications within the automotive field is required. Experience in engine rebuilding, engine electronics, and/or engine control systems is preferred. Demonstrated leadership in engineering coursework is also desired. A valid/clear driver's license is required.