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Abrazo will present seminars on weight-loss surgery this week.
Photo courtesy Abrazo
Weight-loss surgery seminars at Abrazo
By Peoria Times Staff
Abrazo Arrowhead Campus holds a surgical weight-loss informational seminar at 6 p.m. Wednesday, Jan. 15, in the Abrazo Arrowhead Campus Physician Plaza Palo Verde Room, 18699 N. 67th Avenue, Glendale.
The speaker is Dr. William Arnold, a bariatric surgeon.
Obesity increases health risks because of the diseases and conditions that are commonly associated with it, such as type 2 diabetes, hypertension, and cardiovascular disease, among others.
According to the National Institutes of Health, rates of obesity are continuing an upward trend:
• Almost 3 in 4 men (73.7%) were considered to be overweight or have obesity.
• About 2 in 3 women (66.9%) were considered to be overweight or have obesity.
• Obesity was higher in women (about 40%) than men (35%).
• Extreme obesity was higher in women (9.9%) than men (5.5%).
Abrazo and other healthcare organizations note that when diet, exercise and medications have failed, it may be time to consider weight-loss surgery, also known as bariatric surgery. Weight-loss surgery may provide effective, lasting relief from severe obesity for appropriate patients, according to Yolanda Farmer, regional director of bariatric and general surgery at Abrazo Health.
"Weight-loss surgery is designed to help you achieve and maintain a healthy weight, and it can help alleviate many other obesity-related conditions like heart disease, diabetes, sleep apnea, depression and more," said Farmer.
Weight-loss surgery is considered safe, but like any surgery, it does have risks, notes Farmer. "Those who are considering surgical weight-loss are encouraged to consult with their personal physician about the risks and benefits," she said.
The National Institutes of Health, as well as the American College of Surgeons and the American Society for Metabolic and Bariatric Surgery also recommend that surgery be performed by a board-certified surgeon with specialized experience/training in bariatric and metabolic surgery, and at a center that has a multidisciplinary team of experts for follow-up care.
Abrazo Health offers bariatric weight-loss programs serving the Valley and beyond with compassionate weight-loss team members and bariatric physicians who offer personalized weight-loss wellness plans.
For more information about surgical weight-loss procedures, information seminars, support groups and more, visit AbrazoHealth.com.
Weight-loss Surgery
Abrazo Arrowhead Campus Physician
Abrazo Abrazo Arrowhead Campus
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Finding the right place for a loved one can seem impossible. So, Benevilla is hosting placement specialists who will share more information on the important process.
Luke Forward - Enhancing the Lives and Morale of our Service Men and Women
LUKE FORWARD THEN – READY FOR THE FUTURE
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Николай Матвеевич Барсов (1859—?) — русский военный  деятель, полковник  (1916). Герой Первой мировой войны.

Биография
В 1879 году  после окончания Вяземской гимназии вступил в службу. В 1880 году после окончания Алексеевского военного училища  произведён прапорщики и выпущен в 71-й резервный батальон. В 1881 году произведён в подпоручики и переведён в 191-й Дрогичинский пехотный резервный  полк. В 1885 году произведён в поручики,  в 1891 году  в штабс-капитаны, в  1900 году в капитаны, ротный командир 3-го Варшавского крепостного пехотного полка.

С 1909 года подполковник, командир батальона Смоленского 25-го пехотного полка. С 1914 года участник Первой мировой войны, в составе своего батальона, был ранен. В 1916 году произведён в полковники, штаб-офицер Смоленского 25-го пехотного полка. 15 апреля 1916 года в бою у высоты Фердинандов Нос был ранен и остался на поле сражения где был взят в германский плен.

Высочайшим приказом от 2 июля 1915 года за храбрость награждён Георгиевским оружием :

Награды
 Орден Святого Станислава 3-й степени с мечами и бантом (1909; Мечи и бант — ВП 15.01.1917)
 Орден Святой Анны 3-й степени с мечами и бантом (ВП 05.02.1913; Мечи к ордену ВП — 1915)
 Орден Святого Станислава 2-й степени с мечами (ВП 07.01.1915)
 Орден Святой Анны 2-й степени с мечами (ВП 07.01.1915)
 Орден Святого Владимира 4-й степени с мечами и бантом (ВП 1915)
 Георгиевское оружие (ВП 2.07.1915)
 Высочайшее благоволение (ВП 13.07.1916)

Примечания

Литература

 Волков С. В. Генералы и штаб-офицеры русской армии. Опыт мартиролога. В 2-х тт.  2012 г.
 Клепов М. Ю. Офицеры - Георгиевские кавалеры Первой мировой войны. Мартиролог. : "Минувшее", 2015 г.

  по военному ведомству//Разведчик/Русский Инвалид за 1915 и 1916 гг.
 РГВИА: Ф.400. Оп.12. Д.3310. Л.1916; Ф.2122. Оп.2. Д.116. Л.493-497 (1915 г.)
 РГВИА: Ф.409. Оп.1. п/с 875 (1907 г.)

Ссылки

 Барсов Николай МатвеевичГеоргиевские кавалеры Великой войны

Полковники (Российская империя)
Выпускники Вяземской гимназии
Выпускники Алексеевского военного училища
Военнопленные Первой мировой войны (Россия)

redpajama_set_name: RedPajamaWikipedia


It's been quite a ride for No. 51 John Drury Dr.: From a sketch and floor plan found in the pages of Small House Designs by Canada Mortgage and Housing Corp., to a real wood frame, drywall and hardwood floors, to family gatherings with laughing children … to vacancy, peeling paint and, soon, demolition.
Put into service in 1953 to shelter generations of Canadian Forces personnel, this handsome, chocolate-brown, post-and-beam, shallow-pitch roofed home beside the former Downsview military base performed its duties with honour, asked only for routine maintenance, and then, like a good soldier, accepted its fate with dignity.
It's not alone, either: Every one of the little semis and big brick split-levels in William Baker Park – two winding streets attached to the base by asphalt umbilical cords – will also face the wrecking ball early next year. The majority of the more than 80 houses were built at the same time as No. 51, with eight more constructed in 1971, five in 1980, and one in 1981. They have been vacant since October 2012, when the last family moved away.
Canadian Forces Housing Authority, which has managed the Keele and Sheppard area neighbourhood since 1996, began divesting itself of Toronto properties in 2009. Now, with ownership recently transferred to Canada Lands Co. (CLC) – the arm's length Crown corporation responsible for Downsview Park's redevelopment, including the new Stanley Greene neighbourhood to the south – next in line for an overhaul, naturally, is Robert Woodhead Crescent and John Drury Drive (both named after First World War soldiers).
What's less simple to understand is the mixture of melancholy and contentment felt while walking these weedy streets. Many of the 1953 homes aren't particularly beautiful (of course, in the same way one can appreciate the design of a utilitarian household appliance, a stocky, dollhouse-like saltbox can be a beautiful object indeed), some are painfully small, and, even the more stylish of the designs wear barebones exterior trim. Despite this, there is sadness in the stillness. Perhaps that's because the neighbourhood hasn't changed much in six decades: Squint away the odd satellite dish and it's Leave It To Beaver Land, minus the children and station wagons.
It doesn't help that Canada Post continued to stuff overflowing mailboxes until recently, and swollen, mouldy phonebooks lay spread-eagled across front stoops.
But, opening the front doors to a few homes with David Anselmi, CLC's director of real estate, and the mood changes to contentment.
Inside, these homes are not forlorn; they're not the kinds of places "decay fetishists" swoon over. Peeling paint is minimal and graffiti, thankfully, is non-existent. They're empty, yes, but they're mostly bright, in surprisingly good repair (the vinyl windows are fairly new, and Mr. Anselmi says the CLC is talking to Habitat for Humanity's ReStores). They still feel like warm and inviting spaces to live.
"I think in the day they suited the purpose," Mr. Anselmi says. "The military families, they would have been quite comfortable here."
In the chocolate-brown house, for instance, the freestanding fireplace in the 13-by-16-foot living room looks good as new, and the shelves that connect it to the kitchen partition wall are ready for tchotchkes and trinkets (or bowling balls, they're that sturdy). The parquet floor in the family room is pristine. The washrooms (both a two- and three-piece) look to have been renovated in the 1980s or early 1990s.
The holes in the wall, Mr. Anselmi explains, are not the result of vandalism, but rather of recent testing by CLC staff to check for lead paint and asbestos. It would be difficult to vandalize these homes anyway: workers are coming and going (especially leading up to the demolitions) and patrols are frequent. South of this home, on Robert Woodhead Crescent, we find the little saltboxes and semis to be in slightly worse condition, with buckling floors and at least one collapsed ceiling. But that's only because these were the earliest to be vacated, and have therefore had to contend with more freeze-thaw cycles, he explains.
Back on John Drury, we head north to a pair of striking all-brick, split-level beauties with shed roofs built in 1980. Around back, tiny balconies offer sweeping views of the thick forest that must have been a wonderland for children. Inside, we find another freestanding fireplace, a spacious kitchen and, for the time, a large bathroom. Further up the street, at Nos. 7, 5, 3 and 1, we find "general's row." Lined up as if for inspection, these are larger, angular homes built for those of higher rank (also constructed in 1980). In No. 7, save for a vertical blind gone awry, the tidy, carpeted home awaits a four-star cocktail party.
According to Canadian Forces Housing Agency senior communications adviser Marie-Claude Gagne, only this row of homes were off the "first come first serve" list (before the CFHA was created in late 1995, however, she says homes were in fact assigned according to rank and family size). Ms. Gagne also confirms that, no matter what the rank, all occupants were charged market rent for their PMQs (Private Married Quarters).
Despite the fact that these homes were rentals, there are a number of examples of renovated basements, divided rooms, and interior decoration beyond the spartan nature of the exteriors. Evidence, again, that people cared. With enough care applied to the planning of the new neighbourhood, this little patch of land will fill once again with the sounds of lawn mowers, snow shovelling, road hockey and backyard barbecues.


text:
\section{Introduction}
When datasets become large and complex, it is often more important and interesting to know what stands out in the data than to learn about its general behavior. The anomaly detection, an important branch of data mining, is to discover rare occurrences in datasets. This field has wide applications in finance, security, health care, law enforcement, and many others.

During past decades, there has been a large body of research work on anomaly detection, such as detecting network failure or network intrusion \cite{ding}, inspecting card and telecommunications fraud \cite{cpv}, classifying Web and email spam \cite{web}, monitoring data-center \cite{thermocast}, detecting malware/spyware \cite{imc}, and image/video surveillance \cite{kh}. Anomaly detection has been studied in a variety of data domains including high-dimensional data \cite{sigmod}, uncertain data \cite{ay}, streaming data \cite{as}, network data \cite{azy}, and time series data \cite{po}.

Different types of data always require techniques from different domains. Inspecting anomalies in time series needs to examine the behavior of the data across time. Thus, several models were proposed in the statistics literature, which includes autoregressive integrated moving average (ARIMA), autoregressive moving average (ARMA), CUmulative SUM Statistics (CUSUM), vector autoregression (VARMA), exponentially weighted moving average, etc \cite{s1}\cite{s2}\cite{s3}. In this work, we focus on multi-dimensional time series in streams, where temporal dependencies are highly non-linear and data is analyzed in an online way. Yamanishi et al. \cite{yt} present SmartSifter algorithm for time series in streams, which employs an online discounting learning algorithm to incrementally learn the probabilistic mixture model, with a decay factor to account for anomalies. Other than the online discounting methods, large number of methods use dynamically maintained cluster models for computing outliers from data streams such as \cite{sz}.

Because of the rising applications of social network, wearable devices and sensor networks, the analysis of structured (graph) time series becomes more and more important. In this work, we also incorporate graph structures of data into anomaly detection. Ide et al. \cite{ik} proposed an eigenvector-based method for anomaly detection over graph streams. A similar method is also proposed by Akoglu et al. \cite{af} to spot anomalous points in time at which many agents in an agent network change their behavior in a way such that it deviates from the norm. However, these methods only learn graph behavior in the time interval $h$, and cannot work well on time series with long and complex repeating patterns.

In this paper, we combine variation inference (VI) and recurrent neural network (RNN) to model the graph time series, and detect anomalies based on log likelihood. RNNs can be employed for a wide range of tasks as they inherit their flexibility from plain neural networks. This includes universal approximation capabilities, since RNNs are capable of approximating any measurable sequence to sequence mapping and have been shown to be Turing complete \cite{hammer}. However, the internal transition structure of the standard RNN is entirely deterministic. There is recent evidence that when complex sequences are modeled, the performances of RNNs can be dramatically improved when uncertainty is included in their hidden states \cite{bo}\cite{ckd}\cite{bb}. In \cite{ckd}, authors propose to extend the variational autoencoder (VAE) into a recurrent framework for modeling complex sequences which is called variational RNN (VRNN). The proposed time series model is based on VRNN.

In order to incorporate the structural information, the graphical convolution neural network (GCNN) is applied to extract features from graphical data. We use K-localized filter to incorporate and utilize the local information on the graph. In practice, time series are influenced by external features, which should be considered in outliers detection. Our model is conditioned on the external features as extra input. The experiments on human wearable senor data and traffic flow data show the detection capability of the proposed model.

\section{Preliminary}
\subsection{Recurrent Neural Network}
RNNs are discrete-time state–space models trainable by specialized weight adaptation algorithms. The input to RNN is a variable-length sequence $\bm{x}=(\bm{x}_1,\ldots,\bm{x}_T)$ which can be recursively processed. And when processing each symbol, RNN maintains its internal hidden state $\bm{h}$. The operation of RNN at each timestep $t$ can be formulated as
\begin{equation}
\bm{h}_t=f_{\theta}(\bm{x}_t, \bm{h}_{t-1}) \nonumber
\end{equation}
where $f$ is the deterministic state transition function and $\theta$ is the parameter of $f$. RNNs model sequences based on the parameterization a factorization of the joint sequence probability distribution as a product of conditional probabilities such that:
\begin{eqnarray}
p(\bm{x}_1,\bm{x}_2,\ldots,\bm{x}_T)&=&\prod_{t=1}^Tp(\bm{x}_t|\bm{x}_{<t}) \nonumber \\
p(\bm{x}_t|\bm{x}_{<t})&=&g_{\tau}(\bm{h}_{t-1}) \label{hsp}
\end{eqnarray}
where $g$ is the function mapping the hidden state to the output distribution conditioned on previous observations, which is parameterized by $\tau$.

The representational power of an RNN is limited by the output function $g$ in \eqref{hsp}. With the deterministic transition function $f$, the joint probabilities $p(\bm{x}_1,\ldots,\bm{x}_T)$ which can be represented by the RNN are determined by the function $g$.

In this work, the function $f$ can be realized by long short-term memory \cite{lstm}. Since the state transition is performed in a deterministic way, when modeling high-dimensional and structured time series, the variability embedded in the hidden states cannot be learned and expressed. That's because the conditional output probability density is the only source of variability. So, it is necessary to introduce stochasticity into the hidden state transition. And in analyzing some highly structured time series, the hidden state $\bm{h}_t$ of RNNs is not expressive enough to capture the state transition dynamics. Some previous work have introduced extra latent variables to model the output functions \cite{bb}\cite{ckd}\cite{storn}. Different from other work, following \cite{ckd}, this work makes the prior distribution of the latent random variable $\bm{z}_t$ at timestep $t$ dependent on all the preceding inputs via the RNN hidden state $\bm{h}_{t-1}$ which can improve the representational power of the model.

\subsection{Variational Autoencoder}
Different from previous work, we detect anomalies based on the conditional probability of time series conditioned on previous data. However, it is difficult to compute the probability of complex time series. Recently variational autoencoder (VAE) \cite{vae} has been shown to be a powerful tool to approximate the intractable complex posterior in the data space. The VAE introduces a set of latent random variables $\bm{z}$, designed to capture the variations underlie the observed variables $\bm{x}$. Typically VAE models the conditional probability $p(\bm{x}|\bm{z})$ as a highly flexible function approximator such as a neural network, which makes the inference of the posterior $p(\bm{z}|\bm{x})$ intractable. Thus VAE uses a variational approximation $q(\bm{z}|\bm{x})$ of the posterior, which introduces the evidence lower bound (ELBO):
\begin{equation}
\log p(\bm{x})\ge-\text{KL}(q(\bm{z}|\bm{x})||p(\bm{z}))+\mathbb{E}_{q(\bm{z}|\bm{x})}[\log p(\bm{x}|\bm{z})] \label{elbo}
\end{equation}
where $\text{KL}(P|Q)$ is the Kullback-Leibler divergence between two distributions $P$ and $Q$.

In \cite{vae}, the approximate posterior $q(\bm{z}|\bm{x})$ is a Gaussian $\mathcal{N}(\bm{\mu}, \text{diag}(\bm{\sigma}^2))$ and the mean $\bm{\mu}$ and variance $\bm{\sigma}^2$ are modeled as the output of neural networks of $\bm{x}$. The prior $p(\bm{z})$ is assumed to simple standard Gaussian distribution. The training process is to maximize the ELBO, which can yield optimal selection of parameters for generative model $p(\bm{x}|\bm{z})$ and inference model $q(\bm{z}|\bm{x})$. Based on re-parameterizing trick, we formulate $\bm{z}=\bm{\mu}+\bm{\sigma}\odot\bm{\epsilon}$ and rewrite:
\begin{equation}
\mathbb{E}_{q(\bm{z}|\bm{x})}[\log p(\bm{x}|\bm{z})]=\mathbb{E}_{p(\bm{\epsilon})}[\log p(\bm{x}|\bm{z}=\bm{\mu}+\bm{\sigma}\odot\bm{\epsilon})] \nonumber
\end{equation}
where $\bm{\epsilon}$ is drawn from standard Gaussian distribution. Then, both generative and inference model can be trained by standard backpropagation technique based on gradient descent.

\section{Anomaly Detection Model}
In this work, our model is based on variational recurrent neural network (VRNN) \cite{ckd}, which is a recurrent extension of VAE. And we extend it to anomaly detection on graph time series by graph Laplacian transform. Moreover, in order to better model the practical situation, we incorporate external features as extra input.

\subsection{Graph Laplacian Transform}
The graph time series always demonstrate strong spatial dependency. Each node is strongly influenced by its neighbors. For example, on highways, the sensors are installed every 1-2 miles, and the traffic flow of adjacent sensors are highly correlated. Moreover, the wearable sensors on human body are always dependent on the neighboring sensors, and this dependency is different in different activities. However, VRNN cannot explicitly model such spatial dependencies. In this work, VRNN is augmented by modeling spatial dependency in an efficient way.

In order to model spatial dependency, in vertex domain, the hidden state transition of certain node should incorporate the hidden states of neighboring nodes weighted by correlation coefficients. However, it is quite time-consuming to consider the pairwise dependencies of every vertex in a large graph. So, it is difficult to express a meaningful translation operator in the vertex domain \cite{bzsl}. Therefore, we transform the graph time series from vertex domain into spectral domain by graph Laplacian transform.

Assume that the graph time series are defined on undirected and connected graph $\mathcal{G}=(\mathcal{V}, \mathcal{E}, W)$, where $\mathcal{V}$ is a finite set of $|\mathcal{V}|=n$ vertices, $\mathcal{E}$ is a set of edges and $W\in\mathbb{R}^{n\times n}$ is a weighted adjacency representing the weight between two vertices. Define the snapshot of graph time series at time $t$, $\bm{x}_t\in\mathbb{R}^n$, as a signal defined on the nodes of the graph, where the i-th element of $\bm{x}_t$ is its value at the i-th node. Define the diagonal matrix $D\in\mathbb{R}^n$ as $D_{ii}=\sum_jW_{ij}$. The graph Laplacian operator $\bm{y}=\bm{L}\bm{x}$ \cite{chung} is the one-step diffusion of the signal defined on the graph, where the Laplacian matrix $\bm{L}$ is defined as $\bm{L}=\bm{D}-\bm{W}\in\mathbb{R}^n$ and normalized version is $\bm{L}=\bm{I}_n-\bm{D}^{-1/2}\bm{W}\bm{D}^{-1/2}$.

Diagonalizing the Laplacian matrix $\bm{L}=\bm{U}\bm{\Lambda}\bm{U}^T$, where $\bm{U}\in\mathbb{R}^n$ and $\bm{\Lambda}=\text{diag}(\lambda_1,\ldots,\lambda_n)$ are eigenvectors and eigenvalues of the Laplacian matrix $\bm{L}$, the graph Fourier transform is defined as $\hat{\bm{x}}=U^T\bm{x}$ and its inverse as $\bm{x}=\bm{U}\hat{\bm{x}}$. The spectral filtering of graph signals \cite{dbv} is defined as:
\begin{equation}
\bm{y}=g_{\theta}(\bm{L})\bm{x}=g_{\theta}(\bm{U}\bm{\Lambda}\bm{U}^T)\bm{x}=\bm{U}g_{\theta}(\bm{\Lambda})\bm{U}^T\bm{x} \nonumber
\end{equation}
where $g_{\theta}(\bm{\Lambda})=\text{diag}(\theta_1\lambda_1,\ldots,\theta_n\lambda_n)$ and $\theta\in\mathbb{R}^n$ is a vector of Fourier coefficients. However, this filter is enough, since the Laplacian operator is local and working on 1-hop neighborhoods. The $k$th power of Laplacian operator is supported by exactly k-hop neighbors \cite{emerge}, representing the signals on the graph at different scales. Since the $k$th power of Laplacian matrix is expensive to compute, We apply the Chebyshev polynomial expansion \cite{dbv} in the following:
\begin{equation}
\bm{y}=g_{\omega}(\bm{L})\bm{x}=\sum_{k=0}^{K-1}\omega_{k}\bm{L}^k\bm{x}=\bm{U}\sum_{k=0}^{K-1}\omega_k\bm{\Lambda}^k\bm{U}^T\bm{x}\approx\sum_{k=0}^{K-1}\tilde{\omega}_kT_k(\tilde{\bm{\Lambda}})\bm{x} \label{cheby}
\end{equation}
where $g_{\omega}(\bm{L})$ is the learned filter based on Laplacian matrix parameterized by $\omega\in\mathbb{R}^K$, and $T_k(\tilde{\bm{\Lambda}})\in\mathbb{R}^{n\times n}$ is the Chebyshev polynomial of order $k$ with $\tilde{\bm{\Lambda}}=2\bm{\Lambda}/\lambda_{\text{max}}-\bm{I}$. The computational complexity is reduced from $O(|\mathcal{V}|^2)$ to $O(K|\mathcal{E}|)$.

\subsection{Generative Model}
In this model, VAE is applied in a recurrent setting. At each timestep, different from previous work \cite{viad}, the prior of latent variables is dependent on the hidden state $\bm{h}_{t-1}$ of RNN, which makes the output function incorporate the temporal structure of the time series. Then we have:
\begin{equation}
\bm{z}_t\sim\mathcal{N}(\bm{\mu}_{0,t},\text{diag}(\bm{\sigma}^2_{0,t})), \text{ where }[\bm{\mu}_{0,t}, \bm{\sigma}_{0,t}]=\varphi^{\text{prior}}_{\tau}(\bm{h}_{t-1}) \label{prior}
\end{equation}
where $\bm{\mu}_{0,t}$ and $\bm{\sigma}_{0,t}$ denote the mean and variance of the prior distribution. Then, the generating distribution will be conditioned on both latent variable $\bm{z}_t$ and hidden state $\bm{h}_{t-1}$ such that:
\begin{equation}
\bm{x}_t|\bm{z}_t\sim\mathcal{N}(\bm{\mu}_{x,t},\text{diag}(\bm{\sigma}^2_{x,t})), \text{ where }[\bm{\mu}_{x,t}, \bm{\sigma}_{x,t}]=\varphi^{\text{dec}}_{\tau}(\varphi^{\bm{z}}_{\tau}(\bm{z}), \bm{h}_{t-1}) \label{dec}
\end{equation}
where $\bm{\mu}_{x,t}$ and $\bm{\sigma}_{x,t}$ denote the mean and variance of the generating distribution, $\varphi^{\text{prior}}_{\tau}$ and $\varphi^{\text{dec}}_{\tau}$ can be realized by neural networks. Since the latent variables $\bm{z}$ are assumed to be independent of each other at the same time, the feature extractor $\varphi^{\bm{z}}_{\tau}(\cdot)$ can introduce dependencies to better model the sequential variation. Inside the RNN, the hidden state is updated as follows:
\begin{equation}
\bm{h}_t=f_{\theta}(\varphi^{\bm{x}}_{\tau}(\bm{x}_t), \varphi^{\bm{z}}_{\tau}(\bm{z}_t), \bm{h}_{t-1}) \label{rnn}
\end{equation}
where transition function $f$ parameterized by $\theta$ is realized by LSTM cell \cite{lstm}, which can capture both long and short-term temporal dependencies. In order to capture the spatial dependencies, the function $\varphi^{\bm{x}}_{\tau}(\cdot)$ is modeled by graph spectral filter \eqref{cheby} with Chebyshev polynomial expansion, i.e. $\varphi^{\bm{x}}_{\tau}(\bm{x})=\sum_{k=0}^{K-1}\tilde{\omega}_kT_k(\tilde{\bm{\Lambda}})\bm{x}$. From \eqref{rnn}, we know that hidden state $\bm{h}_t$ is dependent on $\bm{x}_{\le t}$ and $\bm{z}_{\le t}$. The distributions \eqref{prior} and \eqref{dec} define $p(\bm{z}_t|\bm{x}_{<t}, \bm{z}_{<t})$ and $p(\bm{x}_t|\bm{z}_{\le t}, \bm{x}_{<t})$ respectively. We can see that the parameterization of this generative model is motivated by the factorization of joint probability as below:
\begin{equation}
p(\bm{x}_{\le T}, \bm{z}_{\le T})=\prod_{t=1}^Tp(\bm{x}_t|\bm{z}_{\le t}, \bm{x}_{<t})p(\bm{z}_t|\bm{x}_{<t}, \bm{z}_{<t}) \label{f1}
\end{equation}

\subsection{Conditional Inference Model}
Denote the external feature at time $t$, such as holiday and meteorological data, as a extra input vector $\bm{e}_t$. It is first processed by a two-layer neural networks denoted by $\varphi^{\text{ext}}_{\tau}(\bm{e}_t)$. Then it is directly added to the mean of latent variables $\bm{\mu}_{\bm{z},t}$, which has no influences on the variance $\bm{\sigma}_{\bm{z},t}$. Based on Gaussian assumption, the approximate posterior is a function of input $\bm{x}_t, \bm{e}_t$ and hidden state $\bm{h}_{t-1}$ as below:
\begin{equation}
\bm{z}_t|\bm{x}_t\sim\mathcal{N}(\bm{\mu}_{z,t}+\varphi^{\text{ext}}_{\tau}(\bm{e}_t),\text{diag}(\bm{\sigma}^2_{z,t})), \text{ where }[\bm{\mu}_{z,t}, \bm{\sigma}_{z,t}]=\varphi^{\text{enc}}_{\tau}(\varphi^{\bm{x}}_{\tau}(\bm{x}), \bm{h}_{t-1}) \label{enc}
\end{equation}
where $\bm{\mu}_{z,t}$ and $\bm{\sigma}_{z,t}$ denote the mean and variance of the approximate posterior. Conditioning on $\bm{h}_{t-1}$, the posterior follows the factorization:
\begin{equation}
q(\bm{z}_{\le T}|\bm{x}_{\le T})=\prod^T_{t=1}q(\bm{z}_t|\bm{x}_{\le t}, \bm{z}_{< t}) \label{f2}
\end{equation}

\subsection{Detection Objective Function}
Applying ELBO at each timestep, based on factorizations \eqref{f1} and \eqref{f2}, we have the accumulative ELBO as below:
\begin{equation}
\mathbb{E}_{q(\bm{z}_{\le T}|\bm{x}_{\le T})}\bigg[\sum_{t=1}^T(-\text{KL}(q(\bm{z}_t|\bm{x}_{\le T}, \bm{z}_{<T})\|p(\bm{z}_t|\bm{x}_{<T}, \bm{z}_{<T}))+\log p(\bm{z}_t|\bm{x}_{<T}, \bm{z}_{\le T}))\bigg] \label{acelbo}
\end{equation}
which is learning objective function during the training. By maximizing \eqref{acelbo}, we first train this model on time series data without anomalies. The optimization problem is solved by ADAM \cite{adam} algorithm. The optimal generative and inference model can be learned simultaneously. During the testing phase, at time $t$, we detect the anomaly based on the ELBO bound as below:
\begin{equation}
b(\bm{x}_t):= \mathbb{E}_{q(\bm{z}_t|\bm{x}_t)}\big[-\text{KL}(q(\bm{z}_t|\bm{x}_t)\|p(\bm{z}_t|\bm{x}_t))+\log p(\bm{z}_t|\bm{x}_t)\big] \label{elbo}
\end{equation}
which is an approximate to the log likelihood.

\subsection{Likelihood Ratio Test}
Different from previous work \cite{viad}, we can locate anomalies on the graph via likelihood ratio test (LRT). In statistics, people use the likelihood ratio test to compare the fit of two models, one of which (the null model) is a special case of (or "nested within") the other (the alternative model). This often occurs when testing whether a simplifying assumption for a model is valid, as when two or more model parameters are assumed to be related. Each of the two compared models, the null model and the alternative model, is separately fitted to the data with the log-likelihood recorded \cite{lrt}. The test statistics (often denoted by $\Lambda$) is twice negative the difference in these log-likelihoods:
\begin{equation}
\Lambda=-2\log\frac{\text{likelihood for null model}}{\text{likelihood for alternative model}} \nonumber
\end{equation}
Whether the alternative model fits the data significantly better than the null model can be determined by deriving the probability or p-value of the obtained difference $\Lambda$.

When applying LRT to a single node $r_ii$ on the graph, based on the generative model, we assume its value follows Gaussian distribution $P$ with mean $\mu^{(i)}_{x,t}$ and variance $\sigma^{(i)}_{x,t}$ calculated in \eqref{dec}. Suppose the observed value at node $r_i$ is $x_i$, the likelihood ratio is:
\begin{equation}
\Lambda(r_i)=-2\log\frac{P(x_i|\Theta)}{\sup\{P(x_i|\Theta')\}} \nonumber
\end{equation}
where $\Theta'$ is the new parameter changing over $\Theta$ that fits the observed data best; $\sup$ denotes the supreme function that finds the maximizer of $P(x_i|\Theta')$ over $\Theta'$. For simplicity, we assume the mean and variance in the $\Theta'$ has same proportion as $\Theta$. The anomalous degree $\text{od}$ of this test is calculated as below:
\begin{equation}
\text{od}=\chi^2_\text{cdf}(\Lambda, \text{df}) \nonumber
\end{equation}
where $\chi^2_\text{cdf}$ denotes the cumulative density function of Chi-Square distribution; $\text{df}$ is the degree of freedom, which means the the number of free parameters of the null model and the alternative model, respectively. The nodes with $\text{od}$ larger than a given threshold are likely to be anomalous.

For example, suppose a certain node $r_i$ at time $t$ follows Gaussian distribution $P\sim\mathcal{N}(100,200)$. Assume the its observed value at time $t$ is 30. Then we calculate the degree of anomaly at $r_i$ as below. The likelihood of the null model is $L_{\text{null}}=P(30|\mathcal{N}(100,200))$. In order to achieve the supreme of likelihood, the alternative model should have mean 30 and variance 60. The likelihood of alternative model is $L_{\text{alter}}=P(30|\mathcal{N}(30,60))$. According to above equations, the anomalous degree is calculated as below:
\begin{eqnarray}
\Lambda(r_i)&=&-2\log\frac{L_{\text{null}}}{L_{\text{alter}}}=25.70 \nonumber \\
\text{od}&=&\chi^2_{\text{cdf}}(25.70,\text{df}=1)=0.999 \nonumber
\end{eqnarray}
Normally, we set the threshold of anomalous degree to be 0.95.

\section{Experimental Study: Beijing Taxi Flows}
In this experiment, the city of Beijing is partitioned into a $32\times32$ grid map based on the longitude and latitude where a grid denotes a region. The dataset contains Beijing's taxicab's trajectories and meteorological data \cite{data}. At $t$-th time interval, the inflow and outflow in all $32\times32$ regions can be denoted as a tensor $\bm{x}_t\in\mathbb{R}^{2\times32\times32}$ where $(\bm{x}_t)_{0,i,j}, (\bm{x}_t)_{1,i,j}$ represent the amount of taxi infow and outflow in region $(i,j)$. The inflow matrix is shown as below.
\begin{figure}[H]
\begin{minipage}[t]{0.5\linewidth}
\centering
\includegraphics[width=2.0in]{bj1.jpg}
\caption{Beijing City Map.}
\label{fig:side:a}
\end{minipage}%
\begin{minipage}[t]{0.5\linewidth}
\centering
\includegraphics[width=2.4in]{bj2.png}
\caption{Inflow in every region of Beijing.}
\label{fig:side:b}
\end{minipage}
\end{figure}
The traffic flows are always influenced by external factors, such as weather and holidays. There are 16 types of weather conditions, such as sunny and rainy. The holidays include all public holidays in China, with total number of 41. The figures below show the influence of Chinese Spring Festival and thunderstorm on the CBD of Beijing, i.e., grid (18,22) on the map.
\begin{figure}[H]
\begin{minipage}[t]{0.5\linewidth}
\centering
\includegraphics[width=2.3in]{bj3.png}
\caption{Spring Festival: Feb 8-14 2016, Normal: Feb 15-21 2016.}
\label{fig:side:a}
\end{minipage}%
\begin{minipage}[t]{0.5\linewidth}
\centering
\includegraphics[width=2.3in]{bj4.png}
\caption{Thunderstorm: Aug 10-12 2013, Normal: 17-19 2013.}
\label{fig:side:b}
\end{minipage}
\end{figure}
The other external factors are weekday, temperature and windspeed. The weekdays are ranging from Monday to Sunday. The temperature is from $-24.1^\circ$C to $41.0^\circ$C. And windspeed is within 0 and 48.6mph. For examples, traffic flows in weekends are usually lower than working days. And strong wind can make people stay at home and reduce the traffic. In implementation, we use one-hot binary vectors to represent weekday, holiday and weather, and use float numbers to represent temperature and windspeed.

The dataset contains traffic flows ranging in two time intervals, i.e., 1st Mar. 2015 to 30th Jun. 2015, and from 1st Nov. 2015 to 10th Apr. 2016. The traffic flow in every region is scaled into $[0,1]$. Each data point shows the traffic flow in 30 minutes. The first 80\% data points are used for training, and the rest is for testing. Since the external factors are already given, we assume that the training set doesn't have any anomalies.

Anomalies are manually added into testing set. All anomalies can be categorized into global anomaly and local anomaly. The global anomaly is always outstanding from global perspective, which has significant difference from normal values and covers a relatively wide range. The local anomalies are bound between the seasonal minimum and maximum and may not appear to be outliers from a global perspective. We evaluate the detection performances on the following types of anomalies ($k=0,1$, anomaly center $(p,q)\in[32,32]^2$):
\begin{itemize}
\item (a) global mean shift (GMS): $(\bm{x}_t)'_{k,i,j}=(\bm{x}_t)_{k,i,j}+\mu$ with $\mu\in[0.8,1.3], (i,j)\in[p\pm3,q\pm3]^2$ and $t\in[30,60]$
\item (b) local mean shift (LMS): $(\bm{x}_t)'_{k,i,j}=(\bm{x}_t)_{k,i,j}+\mu$ with $\mu\in[0.4,0.6], (i,j)\in[p\pm1,q\pm1]^2$ and $t\in[5,10]$
\item (c) global amplitude change (GAC): multiplying multiple dimensions of the data with $1+(\bm{g}_t)_{k,i,j}$ where function $(\bm{g}_t)_{k,i,j}$ is a Gaussian random variable with 0 mean and $10(\bm{\sigma}_{x,t})_{k,i,j}$, and $(i,j)\in[p\pm3,q\pm3]^2, t\in[30,60]$
\item (d) local amplitude change (LAC): multiplying single dimensions of the data with $1+(\bm{g}_t)_{k,i,j}$ where function $(\bm{g}_t)_{k,i,j}$ is a Gaussian random variable with 0 mean and $6(\bm{\sigma}_{x,t})_{k,i,j}$, and $(i,j)=(p,q), t\in[10,20]$.
\end{itemize}
where $\bm{\sigma}_{x,t}$ is the generated variance in \eqref{dec}. The detection examples are shown as below. The y-axis label shows the index of the corresponding dimension. The red-dotted points are detected outliers.
\begin{figure}[H]
\centering
\includegraphics[width=3.6in]{bj_gms.png}
\caption{Global Mean Shift: $\mu=0.9, k=0, (p,q)=(18,18)$, duration is 30 data points.}
\end{figure}%
\begin{figure}[H]
\centering
\includegraphics[width=3.6in]{bj_lms.png}
\caption{Local Mean Shift: $\mu=0.5, k=0, (p,q)=(15,16)$, duration is 10 data points.}
\end{figure}
\begin{figure}[H]
\centering
\includegraphics[width=3.6in]{bj_gac.png}
\caption{Global Amplitude Change: $\sigma=0.8, k=0, (p,q)=(10,22)$, duration is 50 data points.}
\end{figure}
\begin{figure}[H]
\centering
\includegraphics[width=3.6in]{bj_lac.png}
\caption{Local Amplitude Change: $\sigma=0.4, k=0, (p,q)=(22,15)$, duration is 10 data points.}
\end{figure}
The following table shows the detection performance on four types of anomalies, where each type of anomaly is tested 200 times in different locations.
\begin{table}[H]
\caption{Results of the proposed detection algorithm on different anomalies. We use average precision (AP) and area under ROC curve (AUC) as performance measures.}
\centering
\begin{tabular}{|c|c|c|c|c|}
\hline
     & GMS & LMS & GAC & LAC \\\hline
AP & 1.00 & 0.91 & 0.95 & 0.88 \\\hline
AUC & 1.00 & 0.93 & 0.89 & 0.92 \\\hline
\end{tabular}
\end{table}

\section{Conclusion}
This paper shows the the application of variational inference and recurrent neural networks on time series anomaly detection. With comprehensive experiments, we prove that the proposed model can have competitive performance on anomaly detection in graph time series. The spectral filter can better learn the spatial information, and the variational recurrent neural networks can model the temporal correlation. The multi-variable Gaussian model is sensitive enough to outliers. Different from previous work, based on likelihood ratio test, our model can not only detect anomalous time points, but also localize the anomalous nodes (positions) on the graph. In the future, we plan to apply this model to detect video outliers.

redpajama_set_name: RedPajamaArXiv


text:
Q: How to detect user is using Experience Cloud/Community Portal during while posting through chatter feed? I've very entry-level knowledge about Communities & Portal, correct me if my understanding is wrong.
Scenario: Internal users can create Cases in the Org, on the other hand, external users can also create Cases too using Experience Cloud (Communities Portal). So, after creating Cases, they might create chatter posts in the Case.
My aim: Detect whether the chatter post is coming from the internal user or the external user(From Communities) and take action based on it.
My assumed solution:
-> A trigger will be on the FeedItem Object
-> Whenever a user posts in the Chatter Feed, the trigger will be fired.
-> Check the FeedItem object's InsertedById by the trigger.
-> From InsertedById, I can find User>Profile>Salesforce License.
-> After finding the Salesforce License, I can cross-check the License Name whether it matches with any of the Experience Cloud License Names (String value checking)
-> Thus I can detect whether the chatter post was created by the internal or the external Community Portal User.
Please let me know if my assumption is correct or needs tuning. Thank you.

redpajama_set_name: RedPajamaStackExchange


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text:
Israel and 9/11: New Report Connects the Dots
by Justin Raimondo Posted on August 31, 2005 August 7, 2009
This news report in the Philadelphia Times Herald might shock the average reader, but its subject is surely familiar to longtime readers of Antiwar.com:
"A memorandum sent to the 9/11 Commission, and Senate and House intelligence committees in September 2004, suggests that young Israelis who canvassed dozens of U.S. Drug Enforcement Administration (DEA) offices in 2000 and 2001 trying to sell paintings to federal workers, may have been spying not only on the DEA, but also on Arab extremists in the United States – including the Sept. 11 hijackers who were living in Florida and New Jersey."
The author of this memorandum [.pdf] is Gerald Shea, a retired corporate lawyer. Shea – an alumnus of Phillips Academy, Yale (1964), and Columbia Law School – was associated for many years with one of New York's most prominent law firms, in New York and Paris, and his memo reads like a lawyer's brief: it is written with the same meticulous attention to details of time and place, and with a lawyerly regard for maintaining a high standard of evidence.
Shea comes to substantially the same conclusion that I did in a series of columns I started writing in late December 2001, the substance of which is contained in a short book, The Terror Enigma: 9/11 and the Israeli Connection: that the Israelis were engaged in spying on U.S. soil in the months leading up to the 9/11 terrorist attacks, that these agents were concentrated in the two areas where the 9/11 hijackers lived and planned their atrocities – Hollywood, Fla., and two counties in New Jersey, Bergen and Hudson – and that they did not share all they knew about preparations for the attack with U.S. authorities. Shea writes:
"Why the Israeli government decided not to share with us all the critical information they had, and the extent of that information, is a subject for the public inquiry. They may have thought some sort of warning prudent in the event their surveillance activities later became a matter of public knowledge. But any energetic Israeli effort to assist the United States in preventing the attacks would not have served their strategic interest, in view of the disastrous effect those attacks were likely to have on the relationships between the United States and the Arab world. As a leader of the Israeli New Jersey Group said when he was arrested on the afternoon of September 11, 'We are Israeli. We are not your problem. Your problems are our problems.'"
Students of this subject will not be surprised by much of what is contained in the Shea memorandum, but there are significant new details unearthed by Shea's research and his thoroughness, particularly in tracing the parallel movements of the 9/11 hijackers (and their known associates) and the Israelis. Shea shows the Israelis had the means, the motive, and the physical proximity to track the hijackers' movements and intercept the details of their plans. Of particular interest is how some of the hijackers came to be put on the FBI's watch list – too late to do any good, but in time to provide the Israelis with a cover story if their shadowing activities came to light – which suggests a cover-up of major proportions.
The "Able Danger" data-mining operation that supposedly uncovered the New Jersey cell of the 9/11 plotters was – for some reason yet to be determined – blocked and prevented from apprehending key figures in the plot, according to the testimony of at least three people who have direct knowledge of this matter. Shea's memo opens up a possibility that may relate to (and explain) the "Able Danger" blockage: was surveillance of Arab terrorist groups in the U.S. subcontracted out to the Israelis, with the knowledge and complicity of the CIA, so that "Able Danger" was considered poaching on the Israelis' preserve? Shea cites a piece in The Forward that describes Israeli covert activities in the U.S. as a violation of "a secret gentleman's agreement between the two countries," and avers:
"The real question today, however, appears to be whether the 'gentlemen's agreement' did indeed prevail here and, because we lacked adequate warning from our surrogates who were keeping the Arabs under surveillance, helped bring us to disaster."
Discussion of this subject is, of course, considered a hate crime: I was attacked by David Frum and others for even raising the question of Israeli foreknowledge – although, tellingly, Frum never mentioned the Fox News four-part series by Carl Cameron that brought this story to widespread public attention. What could he say, after all – that Fox News is a bastion of liberal bias, and prejudiced against Israel? I don't think so.
Of particular interest are the various appendices. The DEA report on the "Israeli art students" has been public for some time, but readers will find Exhibit B very informative: this lists the various hijackers according to their geographical locations in one column and the Israeli agents detained and deported or otherwise identified in the other. The maps at the end of the memo show this correlation in graphic terms: the effect is shocking. Whether shocking enough to shake the 9/11 Commission and our government officials out of their torpor remains to be seen. It brings home, visually, the significance of the following bit of dialogue – cited by Shea – between Brit Hume and Cameron at the conclusion of their first broadcast on the Israeli spy operation:
Hume: "What about this question of advanced knowledge of what was going to happen on September 11? How clear are investigators that some Israeli agents may have known something?"
Cameron: "It's very explosive information, obviously, and there's a great deal of evidence that they say they have collected, none of it necessarily conclusive. It's more when they put it all together. A bigger question, they say, is how could they not have known?"
Indeed. Now go read the whole thing…
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redpajama_set_name: RedPajamaCommonCrawl


Q1 Please introduce yourself – what's your name and where do you come from? Share your dx if you like, or your relationship with IBD.
Q2 Where do you find community and support for your journey with #IBD?
Q3 If you could tell everyone one thing about #IBD, what would it be?
Q4 What's the most useful piece of advice or information you've received since #IBD diagnosis?
Q5 Have you ever taken part in a research study?
Q6 We've got a lot to do in the years ahead. What do you think our number 1 priority should be?


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In order to post your job onto the seafood industry jobs board please email us and we will supply you with our rate card for advertising on this page.
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text:
Shark Tech: Top 5 Shark-Related Technologies
You might be wondering why we're writing about sharks again. We're fascinated by sharks, and with our corporate headquarters nearly next door to a great white shark breeding ground, they're a big part of the local lore. In fact, just over a month ago, a surfer was attacked by a shark in nearby Manhattan Beach (he survived).
In celebration of Shark Week, we're taking a look at the top 5 shark-related technologies.
1. Animatronics
Despite its well-documented malfunctions, Bruce, the animatronic shark built for the 1975 movie classic Jaws, led the way for the explosion of animatronic special effects in films in the 1980s and 1990s.
Bruce was actually a collection of three sharks, each costing $150,000 to build, nicknamed for director Steven Spielberg's lawyer. Two were versions known as platform sharks, one that moved from camera-left to -right and the other from camera-right to -left. The hidden sides of each of these left exposed pneumatic hoses and gears. The third shark was known as a sea-sled shark, a full-body prop attached by a 100ft long cable to a steel platform sunken to the ocean floor requiring 14 operators to control its movements. They were created by a 40-person special effects team led by Bob Mattey, who created the giant squid in 1954's 20,000 Leagues Under the Sea.
The sharks were made of hard polyurethane rubber over a tubular-steel skeleton, but were not water-tested before arriving on location in Martha's Vineyard two months after filming had begun. The first day Bruce was tested, he sank and had to be retrieved by divers. On the second day, the hydraulic system exploded. The salt water of the Atlantic Ocean caused problems with the onboard electrical systems, motors, and pneumatics.
In hindsight, Jaws wouldn't have been what it was without Bruce's numerous setbacks. The technical hiccups forced the crew to shoot much of the movie without showing the shark as planned. This allowed Spielberg to create the film's iconic feeling of suspense before Bruce's big reveal 55 minutes into the movie.
Want to create your own modern version of Bruce? You can find motion control, RF, sensor, Bluetooth Low Energy (BLE), Wi-Fi, cellular, and GPS products at Symmetry Electronics.
2. littleBits Swimming Shark
Among the infinite number of projects that can be created with littleBits Electronics, you can make your own Swimming Shark! A scaled-down model of an installation at the New York Museum of Modern Art (MoMA), this mechanical monster maneuvers back and forth to swim through air just like a real shark swims through water.
Ready to jump in with littleBits? Symmetry carries the full line of littleBits components so you can create anything you can imagine, including the DC motor, power module, RGB LED module, slide dimmer, and wire required for the Swimming Shark project.
3. Shark Tracking
Scientists use two types of tags to track and study sharks: the Smart Position-Only Tag (SPOT) tracks individual sharks in real time using satellite positioning, while Pop-Up Archival Tags (PAT) record water pressure, temperature, and light by disengaging themselves from a shark at a preprogrammed date and transmitting data once they float to the surface.
Recently, a new tracking tool has been developed, in the form of a torpedo-shaped autonomous underwater vehicle (AUV). AUVs present a less disruptive way for researchers to study sharks, including being able to build a 3D map around an individual shark to better understand its environment and surroundings. A REMUS AUV captured some great footage in 2013.
Tracking down components for your next application? Symmetry offers GPS modules, position sensors, proximity sensors, accelerometers, current sensors, temperature sensors, and connectivity options in the form of Wi-Fi, Bluetooth Low Energy (BLE), and cellular.
4. Sharkskin Aerodynamics
In 2011, Lufthansa began the two-year Multifunctional Coating research project that sought to determine whether a new surface coating mimicking the riblet effect of a shark's skin would reduce air flow resistance and thus increase fuel efficiency. Two Airbus A340-300 planes were outfitted with eight 10cm x 10cm lacquer-coated patches attached to the fuselage and wings of the planes. Initial results suggested that the planes could increase fuel efficiency by 1%. This translates to around 90,000 tons of jet fuel per year, saving roughly $95 million. Results from the project are being analyzed, and Lufthansa is looking into methods to economically coat entire planes with the riblets.
Interested in getting your aerodynamic project off the ground? Symmetry sells accelerometers, current sensors, gas sensors, position sensors, proximity sensors, temperature sensors, and connectivity options in the form of GPS, Bluetooth Low Energy (BLE), and cellular.
5. Shark Deterrent
Hesitant to jump in the ocean after reading more about sharks? Head over to Kickstarter to back NoBite Technology's Ocean Recreation Comfort Apparatus (ORCA) wearable shark deterrent, which comes with a $1 million NoBite Guarantee. The watch-sized, rechargeable ORCA emits sounds that claim to keep sharks away without harming humans or marine life. The ORCA creates a 100ft (30m) "Sonic Camouflage" buffer, and will last up to 12 hours on one charge.
Want to design your own sonic or wearable device? Symmetry stocks audio ADCs and DACs, codecs, amplifiers, and development kits for wearables.
If you want to keep the ocean safe for humans and sharks, support your local Surfrider Foundation.


text:
Title: OLETC: What's New
Corporate Author: National Law Enforcement and Corrections Technology Center (NLECTC)
National Law Enforcement and Corrections Technology Center (NLECTC)
Grant Number: 2005-MU-CX-K007
Sale Source: National Law Enforcement and Corrections Technology Center (NLECTC)
700 N. Frederick Ave.
Bldg. 181, Room 1L30
Type: Issue Overview
Annotation: This article briefly describes five technologies in which the Office of Law Enforcement Technology Commercialization is assisting in their development for use by public safety agencies.
Abstract: The Office of Law Enforcement Technology Commercialization (OLETC) assisted in the "bump switch" project by developing a commercialization plan and identifying a manufacturer willing to make a small item in small runs for a relatively small market. This was done for two veteran bomb technicians and members of the Birmingham, Alabama Police Department who designed and patented a "bump switch" to negate the need to keep a tool turned on, thereby conserving valuable battery power. The battery-powered tool could be used by a robot working on a package suspected of being a bomb. A company's applied-technology division in Virginia received commercialization assistance through OLETC's Commercialization Workshop and the use of experts and scientists in its development of the License Sanction Enforcement System (LSES). The LSES is system that can monitor drivers whose licenses have been restricted, suspended, or revoked. The University of Central Florida designed a statewide computer network known as the Florida Integrated Network of Data Exchange and Retrieval (FINDER). OLETC is providing assistance on marketing strategies. OLETC has provided assistance in commercialization planning and a technology review by the OLETC advisory council to a Minnesota company that developed a suite of software and hardware products that translates bi-directionally whatever is said, heard, or written. Lastly, the Trooper Special Intervention System (SIS) designed by an Austrian company and used by the Austrian Special Forces is a useful device as a less lethal weapon. The 1-liter blast of atomized water can be used for crowd-control purposes. However, its precise effect on people needs extensive research for which OLETC has provided assistance.
Main Term(s): Science and Technology
Index Term(s): Corrections standards; Police standards; Technical evolution; Technology transfer
Note: From TechBeat, Summer 2006; downloaded on October 23, 2006.


[rndtxt] I hate how people focus on what they THINK is a healthy weight. Let me tell you what does, proper diet! If you can handle a high protein low carb diet, rope jumping would help for weight loss! I lost 60 pounds following a high protein diet! Alice Says: 09-06-11 at 2:27 pm Hey guys. I'm not going to burn nearly as many calories as someone my same height and weight who is 25% body fat. Hourglass (1 minute)20 gp1/2 lb. And i really can't believe i'm 145. I've about 30 more pounds to lose to reach goal of 140 on a 5'4″ frame, and this is mos def the ticket there! Alexis Says: 01-22-12 at 4:50 pm OMG! Wow I am sooo inspired now. I have been eating less, keep the sweets and breads away for the past three days. I quit the gym, do pushups and other floor exercises, walk and skip rope and ride my stationary bike. Eat things that makes you feel fuller quicker such as couscous. Liquid Ice: Also known as "alchemist's ice," this sealed jar of crystalline blue fluid immediately starts to evaporate once opened. You can mix and match the workouts: for example, do 200 jump rope, 50 jumping jax, another 200 jump rope, 50 jj, etc. Harder materials wear down or break the drill more quickly. Pilum: The tip of this heavy javelin is designed to break off and embed itself into a shield once it hits. I know most will say that whatever I personally accomplished can't speak for everyone else, since we're different, but I'll give you an example. Sophie Says: 06-11-10 at 6:39 am Hi! I'm 23 years old, 4'9″, 117lbs. I felt good enough to try so I did 600 repetitions, then took a minute walk, and finsihed with the last 400 repetitions. although im pretty muscularly built id like to get rid of all my excess fat and pack on about 45lbs of muscle. kip thompson Says: 12-13-09 at 3:22 am mmk everyone, I am a professional natural bodybuilder, an ACE certified personal trainer, and i have to say that your goals of being able to jump rope for 45 consecutive minutes is a little off track.


text:
Инчгарви () — небольшой необитаемый остров, расположенный в заливе Ферт-оф-Форт у восточных берегов Шотландии, близ Эдинбурга. Его название происходит от Innis Garbhach, что в переводе с шотландского языка означает «грубый остров». Местное предание утверждает, что остров получил своё название от молодой сельди, название которой произносится как «garvies», которая укрывалась большими стаями вокруг его берегов. Хотя в настоящее время остров необитаем, Инчгарви был заселен на протяжении различных периодов истории. Первые записи об этом датируются концом XV века. Наивысшая точка острова расположена в 19-ти метрах над уровнем моря. Его профиль и цвет делает его очень похожим на линкор на расстоянии.

Сейчас на острове находятся центральные опоры моста Форт-Бридж.

История
В дни, когда лодки были единственным способом пересечь залив Ферт-оф-Форт, остров был на главном пути между  в области Файф и  в Лотиане. Это делало его стратегически важным объектом.

На Инчгарви располагался замок, построенный королём Джеймсом IV, предположительно для оборонительных целей. Джеймс повелел построить замок 20 марта 1491. В 1497 году остров был использован как карантинная зона для зараженных «Grandgore» (сифилис) в Эдинбурге. В сентябре 1497 года был принят закон, согласно которому обязательно выходили на пенсию люди, страдающие от этой болезни.

8 марта 1514 Маргарита, вдова Уильяма Дандас, взяла на себя обязательство завершить строительство крепости. Два «серпантина» и орудия из Кольстона (Colstone) были размещены на острове после визита артиллерийских экспертов в июле 1515 года. В 1707 году остров, как известно, был сдан в аренду Арчибальду Примроузу (Archibald Primrose, 1st Earl of Rosebery). В 1779 году, однако, укрепления на острове были возобновлены ещё раз, в ответ на угрозу, исходящую от Джона Пол Джонса, американского адмирала.

Примечания

Литература

Ссылки
 The Daily Telegraph — Where seabirds go Forth
 Online (scanned) version of Old and New Edinburgh

География Эдинбурга


Specializing in FHA, construction, renovation, jumbo, doctor loans, and VA, with an excellent track record of client satisfaction and quick closings. Proudly serving the greater Bothell, Washington region. Get started with Caleb today!
Caleb Johns is highly detail-oriented and delves into all the seemingly minor details that if glossed over, could come back to haunt a home buyer. He prides himself on his clear communication and expert knowledge of the esoteric home loan process.
Due to his meticulous nature, he can boast that through hundreds of loan fundings, he has never missed a detail that caused a loan to fail. For this reason his clients and business partners trust him implicitly.
Caleb has spent most of his career in fields tied to residential homes. He was raised in a family of builders and earned a hands-on education in construction; later he spent ten years helping clients build art collections for their homes. Besides obtaining his Real Estate License in the State of Hawaii, he has also achieved certification as a LEED Green Associate. Finding new, healthier ways to grow our built environment and economy is important to him.
In his spare time Caleb enjoys hiking with his wife and their German Shorthaired Pointer, working on home improvement projects, strumming his ukulele, planning his next international trip, and feeding the literature major trapped inside by seeking out intriguing new books and authors.
At Northpointe Bank, we strive to be the best bank in America by bringing value and innovation to the people we serve. We do this by empowering our employees to exceed our customer expectations, delivering convenient innovative technology, and supporting our employees and the communities we serve.


text:
International defence commitments
RFA Fort Rosalie finishes her Libya mission
The Royal Fleet Auxiliary (RFA) vessel Fort Rosalie has completed her tasking under Operation ELLAMY, the UK's contribution to international efforts to protect Libyan civilians from attack by the former Gaddafi regime.
RFA Fort Rosalie, with an Apache helicopter on deck, during her deployment on Op ELLAMY [Picture: LA(Phot) Guy Pool, Crown Copyright/MOD 2011]
For the past four months, RFA Fort Rosalie has been supporting helicopter carrier HMS Ocean and other Royal Navy warships by providing stores and ammunition, and has also undertaken a vital shuttle service - known as a 'Del Boy' run - between the Task Group and NATO bases in the Mediterranean.
She finished her tasking off Libya last week and is now on her way back to the UK.
Before she returns home, RFA Fort Rosalie will make a brief stop in Civitavecchia, Rome, which will be an opportunity to thank Italy for the support they have given to UK forces during Libya operations and for their own substantial contribution to the international campaign.
RFA Fort Rosalie's Commanding Officer, Captain Phillip T Hanton, said:
RFA Fort Rosalie has switched from amphibious exercises in the Mediterranean to supporting maritime strike operations off Libya and will shortly deploy to the North Atlantic for anti-narcotics duties.
This high tempo of front line operations is a vivid demonstration of our integral role in supporting the Royal Navy and our ability to make a direct contribution to UK defence and security.
Libya is the latest in a long line of front line roles for the 23,384-tonne Auxiliary Fleet Support Vessel, which is designed to supply Royal Navy warships with food, stores and ammunition whilst underway.
Twenty-nine years ago, Fort Rosalie provided direct support to the amphibious operations in the Falklands. She later spent much of the 1990s alongside the Croatian port of Split supporting peacekeeping operations in the Balkans.
Successive refits have kept the ship up-to-date, and the demand for her versatile capabilities has never diminished.
RFA Fort Rosalie deployed from the UK at the beginning of May 2011 laden with 6,000 cubic metres of stores as part of the Royal Navy's Response Force Task Group (RFTG) - the UK's military quick reaction force - for a series of amphibious exercises in the Mediterranean.
RFA Fort Rosalie (centre) during her deployment on Op ELLAMY [Picture: LA(Phot) Guy Pool, Crown Copyright/MOD 2011]
However, the RFTG was redeployed to the North African coast when it was decided to increase pressure on the Gaddafi regime through the use of Apache attack helicopters belonging to the Army Air Corps but based on HMS Ocean.
A key task for RFA Fort Rosalie was to deliver a fifth Apache to HMS Ocean. In a first for the RFA, the attack helicopter was flown onto Fort Rosalie in Souda Bay, Crete.
An equally important role has been supplying ammunition to HM Ships Liverpool, Sutherland and Iron Duke to ensure their ability to deliver naval gunfire support when operating in range of the Libyan coast.
General stores to support the Task Group are brought from the UK by road and air via a forward logistic site located in southern Italy. These were then transferred to Fort Rosalie for delivery to the ships in the Task Group.
The transfer of over 200 personnel in and out of theatre, together with regular deliveries of mail, has made a big difference to morale at sea.
Despite a high tempo of operations, RFA Fort Rosalie was able to take part in commemorations of the 69th anniversary of Op PEDESTAL, the Second World War convoy to relieve Malta.
Captain Hanton laid a wreath on behalf of the Royal Fleet Auxiliary Service at two official ceremonies to mark this historic event.
Since the start of the year a total of 16 Royal Navy warships, submarines and Royal Fleet Auxiliary vessels have contributed to humanitarian, combat and embargo operations off Libya. Two ships - the destroyer HMS Liverpool and the minehunter HMS Bangor - remain deployed.
Commodore Bill Walworth, Commodore Royal Fleet Auxiliary, said:
Four Royal Fleet Auxiliaries have supported UK and coalition activities off the coast of Libya. In doing so they underline our ability to sustain the Royal Navy on station for as long as operations require.


Good review are really matter when it comes to your app they are helped to increased number of downloads.
Some of people trust local businesses more as a result of positive online reviews.
Consumers have changed their minds about a purchase based solely on the negative reviews they read.
Some User said they trust online reviews as much as personal recommendations.
You need to create a great app that provides value to its users. Make sure you update app regularly to keep it relevant and functional.
Value to your customer need and solve their problems very quickly so you get always happy user. Once you have helped your customer, you can politely ask them to write review for your app.
C) Use Gamification - Typically as an online marketing technique to encourage engagement with a product or service to increase your users.
D) Cover Social media- appeal user to leave review for app! Remind users to rate and review your app by cross-promoting through email, newsletters and social media. Remember, never purchase fake reviews or you will run the risk of having your app suspended from the app store! Anyhow, organic reviews are a win-win for the user and the app developer: it helps to improve the app for its users, as well as increasing its ranking and downloads.


text:
(Tochigi, 8 de maio de 1968) é um ex-futebolista profissional japonês e atualmente treinador.

Kurosaki, foi jogador por diversos clubes, no inicio da J-League, chegando a disputar pela Seleção Japonesa de Futebol, a Copa da Ásia de 1988. Kurosaki, defendeu Honda FC, Kashima Antlers, Kyoto Purple Sanga, Vissel Kobe, Albirex Niigata e Omiya Ardija.

Em 19 de dezembro de 2009 ele passou a atuar como treinador do Albirex Niigata.

Ligações externas
Perfil no National F.T.

Treinadores de futebol do Japão
Futebolistas do Japão
Futebolistas do Albirex Niigata
Pessoas do período Showa
Pessoas do período Heisei


text:
Tilt! Old-school pinball is no longer old-school
Published 5:45 am Saturday, December 30, 2017
ELK GROVE VILLAGE, Ill. — The old-school arcade game of pinball is resurging in popularity.
Interest has skyrocketed over the last decade or so, with the number of players and competitions growing worldwide, according to the International Flipper Pinball Association. There were 500 players in 50 competitions worldwide in 2006, according to the IFPA. In 2017, there were nearly 4,500 competitions and more than 55,000 players.
"Pinball is not going away," says pinball player Zach Sharpe, also the spokesman for the world's leading pinball manufacturer Stern Pinball, Inc. in Elk Grove Village, a Chicago suburb. "It can't be replicated and I think that's why it never truly goes away."
The first game ever patented was in 1871, but access to games stalled from the 1940s through the 1970s with some cities banning pinball because it was deemed to be gambling, according to Roger Sharpe, Zach's father, who wrote the book "Pinball!"
The elder Sharpe is known in the pinball world for convincing the New York City Council that the game is more skill than luck, leading to the city lifting its ban in 1976 and precipitating the removal of similar restrictions elsewhere.
Since then, popularity has ebbed and flowed. But Zach Sharpe says his company's revenue has shot up in recent years. This year revenue grew 30 percent over 2016 and 2016 was up 40 percent over 2015.
He attributes some of this growth to smartphone applications that show enthusiasts where to find pinball machines, video pinball and arcade bars.
The game attracts a variety of ages, including 14-year-old Escher Lefkoff, who at 13 won the Professional and Amateur Pinball Association's World Championships last spring.
"It was my day," he said recently at the Chicago Pinball Expo. "I played great that day and I won. It was the most magical moment of my life."
His dad Adam Lefkoff is also a ranked player and inspired his son to play when he was just two years old. Nearly 12 percent of ranked players are women.
The Lefkoffs travel to pinball tournaments from their home in Longmont, Colorado, and Adam Lefkoff says the game teaches his son life lessons.
"The ball is going to drain. That is inevitable," said Adam Lefkoff. "It's important what you do while the ball is in play."
Tax overhaul breeds uncertainty for clean energy industry
By Elizabeth Dunbar Minnesota Public Radio ST. PAUL — Lawyers and accountants in the renewable energy industry are poring over... read more


CARFAX 1-Owner, Hendrick Certified, Excellent Condition, GREAT MILES 24,101! $600 below Kelley Blue Book!, FUEL EFFICIENT 25 MPG Hwy/18 MPG City! Turbo, Bluetooth, Smart Device Integration, Alloy Wheels, EQUIPMENT GROUP 301A MIDEXCELLENT VALUEThis F-150 is priced $600 below Kelley Blue Book.KEY FEATURES INCLUDEBluetooth, Aluminum Wheels, Smart Device Integration Keyless Entry, Steering Wheel Controls, Electronic Stability Control, Brake Assist, 4-Wheel ABS.OPTION PACKAGESEQUIPMENT GROUP 301A MID 4.2 Productivity Screen in Instrument Cluster, Class IV Trailer Hitch Receiver, towing capability up to 5,000 lbs, smart trailer tow connector and 4-pin/7-pin wiring harness, Rear Window Defroster, Rear Under-Seat Storage, Fixed Backlight w/Privacy Glass, 8-Way Power Driver's Seat, Radio: Single-CD w/SiriusXM Satellite SiriusXM US satellite service is available in the 48 contiguous United States and D.C, 7 speakers, Power-Adjustable Pedals, Leather-Wrapped Steering Wheel, Rear View Camera w/Dynamic Hitch Assist, Power Glass Heated Sideview Mirrors, manual folding, turn signal, auto-dimming feature (driver's side) and black skull caps, Auto-Dimming Rearview Mirror, ENGINE: 2.7L V6 ECOBOOST auto start/stop technology system, 3.31 Axle Ratio, GVWR: 6,250 lbs Payload Package, ELECTRONIC LOCKING W/3.31 AXLE RATIO. Ford XLT with Shadow Black exterior and Medium Earth Gray interior features a V6 Cylinder Engine with 282 HP at 6250 RPM*. CARFAX 1-Owner 10-year/100,000 mile Limited Powertrain Warranty, 12-month/12,000 mile High-Tech Coverage, 24-hour Roadside Assistance, Trip Interruption Services, Vehicle Rental Coverage, 168-Point Vehicle Inspection, CARFAX Vehicle History ReportPricing analysis performed on 4/1/2019. Horsepower calculations based on trim engine configuration. Fuel economy calculations based on original manufacturer data for trim engine configuration. Please confirm the accuracy of the included equipment by calling us prior to purchase.This Dealership checks the vehicle recall status prior to making any pre-owned vehicle available for sale. Manufacturers and/or NHTSA may announce additional recalls at any time. To inquire about the recall status of this vehicle, visit: https://vinrcl.safercar.gov/vin/Price may include Dealer Installed Options.
Hendrick Certified, Superb Condition. FUEL EFFICIENT 23 MPG Hwy/17 MPG City! XLT trim. Bluetooth, CD Player, Smart Device Integration, iPod/MP3 Input, Aluminum Wheels, 4x4, Flex Fuel, Serviced here.KEY FEATURES INCLUDE4x4, Flex Fuel, iPod/MP3 Input, Bluetooth, CD Player, Aluminum Wheels, Smart Device Integration. MP3 Player, Privacy Glass, Keyless Entry, Steering Wheel Controls, Child Safety Locks. Ford XLT with Oxford White exterior and Medium Earth Gray interior features a V6 Cylinder Engine with 282 HP at 6250 RPM*. Serviced here.PURCHASE WITH CONFIDENCE10-year/100,000 mile Limited Powertrain Warranty, 12-month/12,000 mile High-Tech Coverage, 24-hour Roadside Assistance, Trip Interruption Services, Vehicle Rental Coverage, 168-Point Vehicle Inspection, CARFAX Vehicle History ReportVEHICLE REVIEWSAt the end of the day, the 2016 Ford F-150 is simply an all-around strong performer, with a good balance of comfort, utility and performance. -Edmunds.com.EXCELLENT SAFETY FOR YOUR FAMILYElectronic Stability Control, Brake Assist, 4-Wheel ABS, Tire Pressure Monitoring System, 4-Wheel Disc Brakes Great Gas Mileage: 23 MPG Hwy.WHO WE AREToyota of Wilmington, now Hendrick Toyota Scion of Wilmington, is a proud recipient of the 2012 President's Award and is a Hendrick Champion dealer for our commitment to customer service and excellence in what we do.Pricing analysis performed on 4/8/2019. Horsepower calculations based on trim engine configuration. Fuel economy calculations based on original manufacturer data for trim engine configuration. Please confirm the accuracy of the included equipment by calling us prior to purchase.This Dealership checks the vehicle recall status prior to making any pre-owned vehicle available for sale. Manufacturers and/or NHTSA may announce additional recalls at any time. To inquire about the recall status of this vehicle, visit: https://vinrcl.safercar.gov/vin/Price may include Dealer Installed Options.
Excellent Condition, Hendrick Affordable. XL trim. $500 below Kelley Blue Book! CD Player, iPod/MP3 Input, Fourth Passenger Door, Onboard Communications System, Trailer Hitch, 5.0L V8 FFV ENGINE, 4x4, Alloy Wheels, Serviced hereA GREAT TIME TO BUYThis F-150 is priced $500 below Kelley Blue Book.KEY FEATURES INCLUDE4x4, iPod/MP3 Input, CD Player, Onboard Communications System, Trailer Hitch, Aluminum Wheels, Fourth Passenger Door MP3 Player, Privacy Glass, Keyless Entry, Steering Wheel Controls.OPTION PACKAGES5.0L V8 FFV ENGINE (STD). Ford XL with Tuxedo Black Metallic exterior and Black interior features a 8 Cylinder Engine with 360 HP at 5500 RPM*. Serviced here.SHOP WITH CONFIDENCE3-month/3,000 Mile Limited Powertrain Warranty, 24/7 Roadside Assistance, Rental Coverage: up to 3 days at $30 per day not to exceed $90 per occurrence, Trip Interruption Service: hotel, transportation, meal reimbursement, Multi-point Vehicle Inspection; detail mechanical inspection, CARFAX Vehicle History ReportEXPERTS ARE SAYINGEdmunds.com's review says America's top-selling pickup is offered in a substantial array of trims and powertrains to accommodate all manner of towing, hauling or off-road needs..EXCELLENT SAFETY FOR YOUR FAMILYElectronic Stability Control, 4-Wheel ABS, 4-Wheel Disc Brakes, Tire Pressure Monitoring SystemWHY BUY FROM USToyota of Wilmington, now Hendrick Toyota Scion of Wilmington, is a proud recipient of the 2012 President's Award and is a Hendrick Champion dealer for our commitment to customer service and excellence in what we do.Pricing analysis performed on 4/18/2019. Horsepower calculations based on trim engine configuration. Please confirm the accuracy of the included equipment by calling us prior to purchase.This Dealership checks the vehicle recall status prior to making any pre-owned vehicle available for sale. Manufacturers and/or NHTSA may announce additional recalls at any time. To inquire about the recall status of this vehicle, visit: https://vinrcl.safercar.gov/vin/Price may include Dealer Installed Options.
Hendrick Certified, Excellent Condition, GREAT MILES 13,896! PRICE DROP FROM $35,490, PRICED TO MOVE $1,300 below Kelley Blue Book!, EPA 23 MPG Hwy/17 MPG City! Flex Fuel, iPod/MP3 Input, CD Player, MEDIUM EARTH GRAY, VINYL 40/20/40 FRO...AFFORDABLEWas $35,490. This F-150 is priced $1,300 below Kelley Blue Book.KEY FEATURES INCLUDE4x4, Flex Fuel, iPod/MP3 Input, CD Player, Aluminum Wheels MP3 Player, Keyless Entry, Privacy Glass, Child Safety Locks, Steering Wheel Controls.OPTION PACKAGESMEDIUM EARTH GRAY, VINYL 40/20/40 FRONT SEAT. Ford XL with Oxford White exterior and Medium Earth Gray interior features a V6 Cylinder Engine with 282 HP at 6250 RPM*. Serviced here.BUY WITH CONFIDENCE10-year/100,000 mile Limited Powertrain Warranty, 12-month/12,000 mile High-Tech Coverage, 24-hour Roadside Assistance, Trip Interruption Services, Vehicle Rental Coverage, 168-Point Vehicle Inspection, CARFAX Vehicle History ReportEXPERTS CONCLUDEThe F-150 boasts high tow and payload ratings and some of the best fuel economy in the light-duty segment. Aluminum body panels and an aluminum bed (rather than traditional steel) help make the 2017 F-150 the lightest truck in its class, too. -Edmunds.com.EXCELLENT SAFETY FOR YOUR FAMILYElectronic Stability Control, Brake Assist, 4-Wheel ABS, Tire Pressure Monitoring System, 4-Wheel Disc Brakes Great Gas Mileage: 23 MPG Hwy.WHO WE AREToyota of Wilmington, now Hendrick Toyota Scion of Wilmington, is a proud recipient of the 2012 President's Award and is a Hendrick Champion dealer for our commitment to customer service and excellence in what we do.Pricing analysis performed on 4/13/2019. Horsepower calculations based on trim engine configuration. Fuel economy calculations based on original manufacturer data for trim engine configuration. Please confirm the accuracy of the included equipment by calling us prior to purchase.This Dealership checks the vehicle recall status prior to making any pre-owned vehicle available for sale. Manufacturers and/or NHTSA may announce additional recalls at any time. To inquire about the recall status of this vehicle, visit: https://vinrcl.safercar.gov/vin/Price may include Dealer Installed Options.


Sorry I'm late with this, I am setting up a new Microsoft Exchange 2007 environment for some clients, and my mind has been completely wrapped around that. Enough jibba jabba though. I am sure you want to know who won their Google Voice Invites.
In order to pick the winners, I plugged everyone's name into an Excel spreadsheet and then using a random number generator, I picked the names corresponding to their number in the spreadsheet. The more entries, the better chance of winning.
Thank you all for participating! Please DM me by 12/4/09 at midnight with your email address so I can send you the Google Voice invites!


text:
package alertsAPI

// This File contains all the helpers definitions by the different parsers
// that can decode the messages received from the Catchpoint Push API.

// This variable contains the translation of the Alert.Setting.AlertTypeId
// variable into its human-friendly definition
var AlertTypeIdLabel = map[uint8]string{
	2:  "ByteLength",
	3:  "ContentMatch",
	4:  "HostFailure",
	5:  "HttpHeaderMatch",
	7:  "ResponseTime",
	8:  "Traffic (RU only)",
	9:  "PageFailure",
	10: "Insight",
	11: "ScriptFailure (IE,Chrome only)",
	12: "Ping",
	13: "Requests",
	14: "Zone",
	15: "Availability",
	16: "Address",
}

// This variable contains the translation of the Alert.Setting.AlertSubTypeId
// variable into its human-friendly definition
var AlertSubTypeIdLabel = map[uint8]string{
	1:   "ByteLengthPage",
	2:   "ByteLengthTotalPage",
	3:   "ByteLengthPageResponseContent",
	10:  "ContentMatchRegularExpression",
	11:  "ContentMatchLastCheckedFile",
	12:  "ContentMatchUploadedFile",
	13:  "ContentMatchRegularExpressionWithNumericCheck",
	30:  "HttpHeaderMatchConnection",
	31:  "HttpHeaderMatchContentType",
	32:  "HttpHeaderMatchCustom",
	33:  "HttpHeaderMatchEncoding",
	34:  "HttpHeaderMatchRedirect",
	35:  "HttpHeaderMatchVersion",
	50:  "ResponseTimeDns",
	51:  "ResponseTimeConnect",
	52:  "ResponseTimeSend (for FTP, refers to 'Upload-Time')",
	53:  "ResponseTimeWait",
	54:  "ResponseTimeLoad - from First byte to Last byte. (for FTP, refers to 'Donwload-Time')",
	55:  "ResponseTimeTimeToFirstByte",
	56:  "ResponseTimePageLoad",
	57:  "ResponseTimePageElementsLoad: Web based synthetic agents -> Total-Time - \"Page's First-Byte Time Full (includes all redirects)\" + \"Page's last redirect Wait-Time\"",
	58:  "ResponseTimeTotalPageLoad: SN only -> \"Page's Load-Time Full (DNS + Conn + Send + Wait + Load for all redirects)\"",
	59:  "ResponseTimeTotalPageElementsLoad: RU -> Total-Time + Exit-To-Entry Time ; SN -> Total-Time",
	61:  "ResponseTimeDomLoad",
	63:  "ResponseTimeTotalPageLoadWithObjectSuspect (Web, Transaction, HTML only -> Total-Time, plus obtaining worst performing Object)",
	64:  "ResponseTimeServerResponse",
	65:  "ResponseTimeFtpDelete",
	66:  "ResponseTimeDocumentComplete",
	90:  "InsightTracepoint",
	91:  "InsightIndicator",
	100: "PingRoundTripTimeAvg",
	101: "PingPacketLoss",
	110: "RequestsObjects",
	111: "RequestsHosts",
	112: "RequestsConnections",
	113: "RequestsRedirects",
	114: "RequestsContentTypeOther",
	115: "RequestsContentTypeImage",
	116: "RequestsContentTypeScript",
	117: "RequestsContentTypeHtml",
	118: "RequestsContentTypeCss",
	119: "RequestsContentTypeXml",
	120: "RequestsContentTypeFlash",
	121: "RequestsContentTypeMedia",
	130: "ContentZoneStartToEndTime",
	131: "ContentZoneBottleneckTime",
	132: "ContentZoneBottleneckTimePct",
	133: "ContentZoneRequests",
	134: "ContentZoneRequestFailures",
	140: "AvailabilityPage",
	141: "AvailabilityContent",
	150: "AddressTestUrl",
	151: "AddressChild",
	152: "AddressPage",
}

// This variable contains the translation of the Alert.Setting.AlertGroupItemFilterTypeId
// variable into its human-friendly definition
var AlertGroupItemFilterTypeIdLabel = map[uint8]string{
	0: "None",
	1: "Index",
	2: "Name",
	3: "Address",
}

// This variable contains the translation of the
// Alert.Setting.NodeThreshold.TypeId
// variable into its human-friendly definition
var NodeThresholdTypeIdLabel = map[uint8]string{
	0: "NodeAgainstTotalAverage (Default)",
	1: "AverageAcrossNodes",
	2: "NodeAgainstNodeAverage",
}

// This variable contains the translation of the Alert.Setting.Trigger.TypeId
// into its human-friendly definition
var TriggerTypeIdLabel = map[uint8]string{
	1: "Default",
	2: "PercentageDeviationFromAverage",
}

// This variable contains the translation of the
// Alert.Setting.Trigger.OperatorId
// variable into its human-friendly definition
var TriggerOperatorIdLabel = map[uint8]string{
	0: "NotEquals",
	1: "Equals",
	2: "GreaterThan",
	3: "GreaterThanOrEquals",
	4: "LessThan",
	5: "LessThanOrEquals",
	6: "NotBetween",
	7: "Between",
}

// This variable contains the translation of the
// Alert.Setting.Trigger.Insight.Indicator.DataSourceId
// and Alert.Setting.Trigger.Insight.Tracepoint.DataSourceId
// variables into its human-friendly definition
var IndicatorDataSourceIdLabel = map[uint8]string{
	0: "HttpHeader",
	1: "HttpContent",
}

// This variable contains the translation of the Alert.Setting.Trigger.CompareId
// variable into its human-friendly definition
var IndicatorCompareIdLabel = map[uint8]string{
	0:  "Counter",
	1:  "Average",
	2:  "Median",
	3:  "StandardDeviation",
	4:  "Min",
	5:  "Max",
	6:  "GeometricMean",
	7:  "Percentile95",
	8:  "Percentile85",
	14: "InterQuartileRange",
	15: "Percentile75",
	16: "Percentile99",
	17: "GeometricStandardDeviation",
	18: "PercentileCustom",
	19: "Percentile25",
	21: "Total",
}

// This variable contains the translation of the Alert.TestDetail.MonitorTypeId
// variable into its human-friendly definition
var MonitorTypeIdLabel = map[uint8]string{
	0:  "IE",
	2:  "OBJECT",
	3:  "EMULATED",
	8:  "PING",
	9:  "TRACERT",
	10: "DNSTRAVERSAL",
	11: "PING-TCP",
	12: "DNSEXP",
	13: "DNSDIRECT",
	14: "TRACERT-UDP",
	15: "PORTTCP",
	16: "FTP",
	17: "DATAPUSHAPI",
	18: "CHROME",
	19: "PLAYBACK",
	20: "PLAYBACK-MOBILE",
	21: "SMTP",
	22: "PORT-UDP",
	23: "PING-UDP",
	24: "STREAMING",
	25: "API",
	26: "MOBILE",
	27: "SFTP",
	28: "SSH",
	29: "TRACERT-TCP",
}

// This variable contains the translation of the Alert.TestDetail.TypeId
// variable into its human-friendly definition
var TestDetailTypeIdLabel = map[uint8]string{
	0:  "Web",
	1:  "Transaction",
	2:  "HtmlCode",
	3:  "Ftp",
	4:  "Tcp",
	5:  "Dns",
	6:  "Ping",
	7:  "Smtp",
	8:  "Udp",
	9:  "API",
	10: "Streaming",
	11: "SSH",
	12: "TraceRoute",
}

// This variable contains the translation of the
// Alert.Diagnostic.DnsTraversalLastLevel.Groups.Group.Responses.Response.Class
// variable into its human-friendly definition
// For now there is only 1 value but keeping this as it can evolve
var ResponseClassLabel = map[uint8]string{
	1: "IN (Internet)",
}

// This variable contains the translation of the Alert.TestDetail.TypeId
// variable into its human-friendly definition
var AlertNotificationLevelIdLabel = map[uint8]string{
	0: "Warning",
	1: "Critical",
	2: "Information",
	3: "Improved",
}

redpajama_set_name: RedPajamaGithub


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text:
Dick Drott
Richard Fred Drott
Cincinnati, OH USA
Deceased: 1985
Glendale Heights, IL USA
Primary Position: P
All Position(s) Played: P, PH
Career: 1957-1963
Minor League Statistics
Dick Drott compiled a career record of 27 wins and 46 losses in his 176-game career with the Chicago Cubs and Houston Colt .45's. He began playing during the 1957 season and last took the field during the 1963 campaign.
1957 NL Chicago Cubs 15 11 .577 3.58 38 32 3 7 3 0 229.0 200 107 91 22 129 2 170 7 2 7 1007
1958 NL Chicago Cubs 7 11 .389 5.43 39 31 3 4 0 0 167.3 156 118 101 23 99 6 127 6 0 9 763
1959 NL Chicago Cubs 1 2 .333 5.93 8 6 0 1 1 0 27.3 25 19 18 5 26 1 15 0 0 2 129
1960 NL Chicago Cubs 0 6 .000 7.16 23 9 7 0 0 0 55.3 63 49 44 7 42 0 32 3 0 4 269
1961 NL Chicago Cubs 1 4 .200 4.22 35 8 12 0 0 0 98.0 75 54 46 13 51 0 48 1 1 7 414
1962 NL Houston Colt .45's 1 0 1.000 7.62 6 1 1 0 0 0 13.0 12 12 11 1 9 0 10 0 0 2 60
1963 NL Houston Colt .45's 2 12 .143 4.98 27 14 6 2 1 0 97.7 95 61 54 13 49 2 58 6 1 7 436
Totals 27 46 .370 4.78 176 101 32 14 5 0 687.7 626 420 365 84 405 11 460 23 4 38 3078
1957 NL Chicago Cubs 38 85 80 3 8 0 0 0 0 0 0 2 25 .100 .122 .100 .222 8 2 0 3 0 0
1958 NL Chicago Cubs 39 59 55 6 15 0 0 0 6 0 0 3 18 .273 .310 .273 .583 15 0 0 1 0 0
1959 NL Chicago Cubs 8 8 8 0 1 0 0 0 0 0 0 0 2 .125 .125 .125 .250 1 0 0 0 0 0
1960 NL Chicago Cubs 23 10 10 0 1 0 0 0 0 0 0 0 6 .100 .100 .100 .200 1 0 0 0 0 0
1962 NL Houston Colt .45's 6 4 4 0 0 0 0 0 0 0 0 0 0 .000 .000 .000 .000 0 1 0 0 0 0
1963 NL Houston Colt .45's 27 24 23 2 3 0 0 0 0 0 0 0 8 .130 .130 .130 .261 3 1 0 1 0 0
Totals 176 213 202 12 34 0 1 0 7 0 0 6 66 .168 .192 .178 .371 36 5 0 5 0 0
Inn.
Fld%
RF/9
RF/G
1957 NL Chicago Cubs P 38 32 229.0 7 33 1 2 .976 1.572 1.053
1958 NL Chicago Cubs P 39 31 167.3 9 27 1 0 .973 1.936 .923
1959 NL Chicago Cubs P 8 6 27.3 0 4 1 0 .800 1.317 .500
1960 NL Chicago Cubs P 23 9 55.3 4 7 0 0 1.000 1.789 .478
1961 NL Chicago Cubs P 35 8 98.0 1 11 1 0 .923 1.102 .343
1962 NL Houston Colt .45's P 6 1 13.0 1 1 1 0 .667 1.385 .333
1963 NL Houston Colt .45's P 27 14 97.7 2 13 1 0 .938 1.382 .556
Totals 176 101 687.7 24 96 6 2 .952 1.571 .682
Statistical data from Baseball Databank, made available under the Creative Commons Attribution-ShareAlike 3.0 Unported License
Baseball Search


it was by the jing wu wushu society of kampar. basicly wat we did was the pass out the food to people who bought coupons to attend tis event.We gathered at 5pm at the shop den started moving things to the venue. it wasnt a buffet style thing, u are free to choose wat u wan to eat from the dishes we had but its not all u can eat.
here are some pics ,didnt had time to snap much cause we were scooping food for them none stop.
it was good night, we wrapped up about 10.30pm, den when we went back to home base a.k.a. the shop, we stayed to helped out because they were short handed.


Lou D'Alo is not following anybody.
Sorry about that Gail - should be working now.
Thanks for the kind words Amanda. Hope our paths cross again soon, and often.
LOL - yes, great line. The dry ironic comedic writing in this clip is through-the-roof good.
heading out to do donuts in the parking lot soon ... thanks for the great idea!
...oh yah, Mark - and don't forget your snowshoes, eh?