text:
24-year-old woman dies shortly after having abortion
TheGrio Staff |
Tonya Reaves, 24, died suddenly after having an abortion a Planned Parenthood clinic in Chicago Friday night. While Planned Parenthood has declined to speak about this tragedy, the medical examiner says Reaves died of a hemorrhage as well as pregnancy complications. Reaves' family and pro-life organizations are outraged by the accident. Daily Mail reports:
The family of Tonya Reaves, 24, is demanding answers after she died suddenly after an abortion at a Chicago Planned Parenthood
Ms. Reaves died Friday night of a hemorrhage, the medical examiner said, with complications from the procedure as well as the pregnancy itself as a contributing factor.
Her death was ruled as an accident, but her twin sister and her family want to know how this tragedy happened.
Click here for the full story.


Well that was an eventful trip! The first time the SSA came off since December and we decided to go for it, figuring we could be in Bedford in a couple of days. So at 10am we left Hartford Marina and set off up river. Now if we had been more experienced we might have noticed that the water was still flowing pretty fast and we probably would have checked with the environment Agency that the river was clear of winter debris, but we were keen new boaters so blithely set off.
It was a beautiful day though a tad chilly and we made fairly good time. In fact we reached St Neots by early afternoon and were feeling confident.
As dusk fell we came to Roxton lock. Getting through was ok, but the flow of water was so great coming out we couldn't make it to the pontoon where we figured we could spend the night. In fact Hamish had to really push the engine to get us off the weir protector!
So we carried on as night fell towards Great Barford. By the time we were close it was pitch black and we were navigating by torch light having found out that the headlight on Apple is actually pretty rubbish. It was pure chance that swinging the torch around the river bank we found the lock pontoon and tied off.
Now, we were both being brave while travelling the last couple of miles but in fact as soon as we tied off our knees were shaking and we had a big hug and admitted to each other how worried we had been.
so tea and bed was called for to get warm and recover. The next morning we went through the lock and moored up by The Anchor pub where we had a well earned couple of pints.
As it happened what we didn't know was that whilst we were travelling the SSA had been re-instated and we probably shouldn't have been out on the river!
This entry was posted in Travelling and tagged Great Barford, Huntingdon, Travelling, Widebeam.


Ese ING que hacés vos...: "Enchanted Moments", mi resolución al CSI #30!
The page continues the story I started telling on Case 13, as I want it to go on the same album. That day, 14th January 2007 was Alana's first birthday in Patagonia. She was turning 8 and being in Trevelin for only a couple of weeks and during school break, she still didn't have any friends so we decided to give her and all of us actually a very special day together.
We did soooo many things that day, but this page tells how we finished it, treating all of us with a delicious icecream at an icecrem producer that is no longer in town (oh we miss them!). Those icecreams are DELICIOUS!... so creamy and tasty flavours... We came to the conclusion that we were no longer missing our favourite icecream shop from Santa Fe (our former town up north) which was something we never thought someone would top.
That's more or less what the journaling says.with a final line that reads "What an amazing start of our life in Pagatonia!"
I have only used the background paper from my paper stash... all the rest of the papers are from the beautiful set of printable coordinates that CSI teams designs each week to go with the case.
The frames are all crackled... I added that little piece of journaling from the Coordinates which I loved.
Sweet child... that can't describe her better... she's such a sweet little girl... she was not even 3 at the time... I can't believe she's already 8! which was Ali's age at the time of these photos... Time flies!
And that's more or less it!


text:
\section{Introduction}

With the widespread use of neural networks in life-critical applications, such as self-driving cars, commercial and government agencies are concerned about the security of deployed deep learning (DL) neural networks (NNs). One example is poisoning NN models during training with datasets containing triggers (trojans) for misclassification.  A trojan is defined as a specific subset of training inputs that cause modifications of the NN weights in such a way that the NN-based classifications for inputs without and with trojans will differ. For example, a trojan can be a yellow sticky inside of a STOP sign picture \cite{Xu2019} in which case  the classifications of STOP sign and STOP sign with yellow sticky will differ. When a poisoned NN model with trojans is used for inferencing, a user will not know about the introduced misclassification by adversaries unless the specific input for inferencing is presented with the trojan.

The motivation for this work is  to gain basic insights about trojans, their interactions with NN architectures, NN measurements that can indicate the presence of trojans, and what algorithmic approaches can be successful in detecting trojans for a variety of NN architectures under computational constraints.

We address three problems in the aforementioned context.
The first problem is in creating an interactive environment, as shown in Figure~\ref{fig:00}, for quick evaluations of (1) NN models with varying complexities and hyper-parameters, (2) datasets with varying manifold representation complexities and class balance ratios, and (3) measurements based on varying approaches and statistical analyses. The second problem lies in designing NN efficiency measurements with understood sensitivity to variations in NN architectures, NN initialization and training, as well as dataset regeneration. The third problem is in devising an approach to detecting trojans embedded in NN models.

\begin{figure}[h]
 \resizebox{.5\textwidth}{!}{
\includegraphics[
  width=12cm,

  keepaspectratio,
]{./figs/nncalculator2.png}
}
  \centering

  \caption{Interactive user interface of neural network calculator.}
  \label{fig:00}
\end{figure}

The problems come with associated challenges. The first challenge lies in the interactivity requirement. As of today, DL NN architectures are very complex; from 60K parameters in LeNet \cite{Khan2019}, to common networks having millions and billions of parameters (160 billion reported in \cite{Trask2015}). Modern networks require hours or days to train on advanced graphics processing unit (GPU) cards \cite{Justus2019}.
The challenge of the second problem lies in the lack of explainable artificial intelligence (AI) \cite{Doran2018} and AI mathematical models \cite{Bruna2017}, \cite{Unser2019}, and \cite{Mallat2016}.
The last challenge lies in the large search space of possible trojans, training data, DL NN architectures, and NN training algorithms that must be understood. Related work is described in Section~\ref{section:related}.

Our approach to these challenges relies on designing a NN Calculator environment and is based on analyses of neuron states in fully connected layers of NNs. The NN Calculator is built on top of Tensorflow Playground \cite{Smilkov2017} by enabling all calculator operators on datasets and NNs, such as storing, retrieving, setting, adding, subtracting, and clearing memory containing training/testing data points and NN coefficients. Furthermore, the NN Calculator contains functionality for introducing a wide range of trojans, collecting NN state measurements, visualizing them, computing trojan sensitive probes, evaluating their robustness to NN training, and saving them for further analyses. The trade-off for interactivity of analyses is the input limitation to 2D dot patterns, the NN limitation to less than 7 hidden layers and 9 nodes per layer due to screen size, and the limitation to custom designed features derived from 2D dot patterns.

The novelty of the work lies in designing:
\begin{itemize}
	\item a web-based NN calculator for the AI community interested in gaining research insights about NN performance under various configurations,
	\item a Kullback-Liebler (KL) divergence based measurement of NN inefficiency,
	\item an approach to detecting embedded trojans in AI models.
\end{itemize}

\section{Related Work}
\label{section:related}

The problem of detecting trojans in NN models has been posed as the Trojan in Artificial Intelligence (TrojAI) challenge by the Intelligence Advanced Research Projects Agency (IARPA) \cite{IARPA2020}. The challenges include round 0, 1, and 2 datasets consisting of trained NN models that classify input images into 5 to 25 classes of traffic signs. The goal of the challenge is to detect models trained without trojan (\texttt{TwoT}) and trained with trojan (\texttt{TwT}) based on the analyses of NN models in limited amount of time on the NIST computational infrastructure.  The problem has many variations based on what information and computational resources are available for trojan detection (type of attack, type of model architecture, model coefficients, training data subsets, description of trojans, number of classes to be misclassified by embedding trojans, classes that are misclassified by trojans, models that have been trained with trojans, computational complexity limits imposed on the delivered solution, etc.). Other challenges related to TrojAI can be found, for example, in the Guaranteeing AI Robustness against Deception (GARD) challenge \cite{Siegelmann2019}. As of today, none of the challenges can be described in terms of their difficulty level which motivates our work.

The TrojAI challenge models were created with a variety of contiguous regions within a traffic sign defining a trojan. In the previous work, the problem of trojans in AI has been reported from the view point of detecting trojans \cite{Xu2019} \cite{Roth2019}, constructing trojan attacks \cite{Liu2018}, defending against trojans \cite{Liu2018a}, and bypassing trojan detectors \cite{Juin2019}. The problem of trojan presence is often related to the efficiency (or utilization) of DL NNs as introduced in the early publications about optimal brain \cite{LeCun1989} and optimal brain surgeon \cite{BabakHassibi1992}. A few decades later, the topics of pruning links and trimming neurons are being explored in \cite{Hu2016}, \cite{Li2017}, and \cite{Han2015} to increase an efficiency of Deep Learning (DL) NNs and to decrease NN model storage and computational requirements of model training. Our work is motivated by the past concepts of NN efficiency. However, our goal is to explore the hypothesis that NN models trained with trojans will demonstrate higher efficiency/utilization of NN than NN models trained without trojan. In comparison to previous work, our approach is focused on reliable measurements in the context of trojan detection and is investigating questions about where trojans are encoded. We assume that the models \texttt{TwoT} and \texttt{TwT} are neither under-fitted nor over-fitted \cite{Belkin2019}.

The problem of gaining insights about DL NNs has been approached by (1) mathematical modeling \cite{Bruna2017} (network layers), \cite{Unser2019} (activation functions), \cite{Mallat2016} (wavelets), (2) feature and network visualizations  \cite{Zeiler2013} (across layers), \cite{Erhan2009}(higher layers),  \cite{Zhou2015} (discriminative features),\cite{Smilkov2017} (fully connected layers at small scale), and (3)  limited numerical precision of modeling to achieve `interactive' response \cite{Wu2016}(quantized NN for mobile devices), \cite{Rastegari2016} (binary weights for ImageNet), \cite{Gupta2015} (tradeoffs), \cite{Hubara2016} (binary NNs). Many insights are pursued with respect to representation learning \cite{Bengio2013}, expressiveness \cite{Simonyan2015a}, \cite{Lu2017}, and sensitivity and generalization (under- and over-fitting NN models) \cite{Novak2018}, \cite{Shwartz-Ziv2019}. From all past work, we leveraged the mathematical framework in \cite{Bruna2017}, visualization called Tensorflow Playground in \cite{Smilkov2017}, and efficiency and expressiveness concepts in \cite{Lu2017}.

\section{Methods}

We describe next the developed NN Calculator with trojan simulations followed by the design of NN inefficiency measurements and our approach to trojan detection.

\subsection{NN Calculator}

Our approach to designing NN Calculator aims at making it as similar as possible to a scientific calculator.
Unlike a scientific calculator, NN Calculator operates on datasets and NN coefficients as opposed to simple numbers. Thus, we reused the symbols for $MC$, $MR$, $M+$, $M-$, and $MS$  for clearing, retrieving, adding, subtracting, and setting memory with datasets (training and testing sets) and NN coefficients (biases and weights). The user interface is shown in Figure~\ref{fig:00} (top left and middle left) where the standard five symbols are preceded with NN or D to indicate whether the operation is applied to NN or data. In addition, we included NN model averaging and dataset regeneration in order to study variability over multiple training sessions and random data perturbations. Evaluating combinations of datasets and NNs in real time enables one to explore full factorial experiments for provided factors.

Most of the calculator settings are used for the main operations on datasets and NNs: training, inferencing, inefficiency computations, and robustness measurements (mean squared error (MSE)) for training, testing and inferencing of sub-sets. Additional operations include collecting neuron state histograms, and derived measurement statistics.
The remaining settings are used to view characteristics of datasets (noise, trojan), parameters of NN modeling algorithm (Learning Rate, Activation Function, Regularization, Regularization Rate), and parameters of NN training algorithms (Train to Test Ratio, Batch Size). In order to keep track of all settings, we added the option of saving all NN parameters and NN coefficients, as well as saving all inefficiency and robustness analytical results. The save options are shown in Figure~\ref{fig:00} (bottom left).

\subsection{Trojan Characteristics Modeled in NN Calculator}

In order to explore how to discriminate a model trained with trojan and a model trained without trojan, we added nine types of trojans to the NN Calculator. Our objective is to understand how the characteristics of trojans affect the trojan detection, i.e. the discrimination of models trained without trojan (\texttt{TwoT}) and trained with trojan (\texttt{TwT}).
We generalized trojan embedding characteristics to be described by
(1) number of trojans per class,
(2) number of trojans per contiguous region,
(3) shape,
(4) size , and
(5) location of trojans inside of a class region.
Figure~\ref{fig:02} illustrate the nine trojan embeddings.

\begin{figure}
 \resizebox{.5\textwidth}{!}{
\includegraphics[
  width=12cm,

  keepaspectratio,
]{./figs/trojan_tablevis.png}
}
  \centering
  \caption{Illustration of nine trojan embeddings in four datasets. Orange dot - class 1, blue dot - class 2, red boundary encloses dots that represent a trojan embedding.}
  \label{fig:02}
\end{figure}

\subsection{Neural Network Inefficiency Measurement}

For a given NN, its (in)efficiency is understood as the ratio of utilized representation states over the total number of representation states. Representation states are introduced next. In addition, we describe  a NN inefficiency measurement from a histogram of NN states at each layer by using (1) KL divergence, (2) a reference state distribution, and (3) computational constraints.

\underline{States of Neural Network:}
In order to derive NN inefficiency, we must measure and analyze states of NN layers as training data are encoded into class labels in a typical classification problem. A state of one NN layer is defined as a set of outputs from all nodes in a layer as a training data point passes through the layer.
The output of a node is encoded as 1 if the value is positive and 0 otherwise.
Thus, for a point $d_{k}$ from a 2D dataset with points $[d_{k}=(x_{k}, y_{k}), c_{j}]$,
 $k=1, ..., npts$ and  $C=2$ classes $c_{1}={orange/N(negative)}, c_{2}={blue/P(positive)}$, it can generate one of  $2^{nl}$ possible states at a NN layer with $nl$ nodes. Figure \ref{fig:03} (top left) shows how a training point $d_{k}$ is converted into a feature vector that enters a neuron of the layer 0. The neuron output is generated and converted to 0 or 1 via thresholding. The neuron outputs create states 0100, 110 and 10 at the three layers for an input point. Figure \ref{fig:03} (top right) presents a table with the state information for all training points at all layers. The combined histogram of states for all  layers and both class labels (one color per layer) is shown in Figure \ref{fig:03} (bottom right).  Finally, Figure \ref{fig:03} (bottom left)  summarizes KL divergence values computed per layer and per label from the histogram of states.

\begin{figure}
 \resizebox{.5\textwidth}{!}{
\includegraphics[
  width=12cm,

  keepaspectratio,
]{./figs/state-to-divergence.png}
}
  \centering
  \caption{The computation of KL divergence from NN state information at each layer per class label.  }
  \label{fig:03}
\end{figure}

\underline{Representation Power:}
We view the histogram of states as a probability distribution that indicates the utilization of a layer. In order to quantify the NN utilization, we leveraged the parallels between neural network and communication fields in terms of (a) NN representation power/capacity (channel capacity in communications), (b) NN efficiency (channel efficiency), and (c) the universal approximation theorem \cite{Hornik1991} (source coding theorem \cite{Shannon1948}).
According to the universal approximation theorem, we view the NN representation power (also denoted as expressiveness or model capacity or model complexity) as its ability to assign a training class label to each training point and create accurate class regions for that class. For instance, a NN must have at least two nodes ($nl=2$) in the final layer in order to assign four class labels (i.e.,  $ C = 4 \leq 2^{nl} = 4 \rightarrow \{00, 01, 10, 11\}$).

Once we gather the state information (see Figure \ref{fig:03} (top)), we can categorize the states into three categories:

\begin{samepage}
\begin{enumerate}
	\item State is used for predicting multiple class labels.
	\item State is used for predicting one class label.
	\item State is not used.
\end{enumerate}
\end{samepage}

The first category is detected when a NN does not have enough nodes (insufficient representation power). It could also occur when a NN layer does not contribute to discriminating class labels (poorly trained NN). The second category suggests that a subset of data points associated with the same class label is represented by one state (efficient or inefficient representation).
The last category implies that a NN has a redundant (inefficient) node in a layer for representing a class label.
Thus, states at NN layers provide information about NN representation power as
(1) \emph{insufficient,} (2) \emph{sufficient and efficient,} or (3) \emph{sufficient and inefficient.}
 An ideal NN is sufficient and efficient.

\underline{Inefficiency of Neural Network:}
Since the source coding theorem is based on calculating mutual information defined via KL divergence \cite{Kullback2017},
we adopt KL divergence as a measurement of how inefficient it would be on average to code one histogram of NN layer states using a reference histogram as the true distribution for coding, where the reference histogram is defined below as the outcome of a uniform distribution over states assigned to each label.
Figure \ref{fig:03} (bottom) shows example results of KL divergence values derived per layer and per label that can be used to compare against values obtained from other datasets; for instance, datasets with trojans.

The rationale behind choosing entropy-based KL divergence with probability ratios comes from three considerations. First, entropy-based  measurement is appropriate because which state is assigned to predicting each class label is a random variable and a set of states assigned to predicting each class label is random. Second, probability-based measurement is needed because training data represent samples from the underlying phenomena. Furthermore,  while training data might be imbalanced (a number of samples per class varies), all training class labels are equally important and the probabilities of classes should be included in the measurement. Third, the divergence measurement reflects the fact that we measure NN efficiency relative to a maximum efficiency of NN that is achieved when sets of states utilize the entire network capacity (representation power).

 \emph{Mathematical definition:}
 Formally, let us denote $Q_{j}=\{ q_{ij} \}_{i=1}^{n}$ to be a discrete probability distribution function (PDF) of $n$ measured NN states and $P_{j} = \{ p_{ij} \}_{i=1}^{n}$ to be the PDF of reference (ideal) NN states. The probabilities are associated with each state (index $i$) and each class label (index $j$). The KL divergence per class label $j$ is defined at each NN layer in Equation~\ref{eq:01}.

\begin{equation*}
D_{KL}(Q_{j} \parallel P_{j})=\sum_{i=1}^{n}(q_{ij}*\log_{2} {\frac{q_{ij}}{p_{ij}}})
\tag{1}
\label{eq:01}
\end{equation*}

where $q_{ij}=\frac{count(i,j)}{p_{j}*npts}$ is the measured count of states normalized by the probability $p_{j}$ of a class label $j$ and the number of training points $npts$.
The PDF of reference states per class label uniformly utilizes the number of states assigned to predicting each class label (i.e., 2 classes imply $\frac{1}{2}$ of all states per label). The reference probability distribution is uniform across all assigned states. Thus, all reference probabilities can be computed as $p_{ij}=m*\frac{1}{n}$ where $m$ is the number of classes and $n=2^{nl}$ is the maximum number of states ($nl$ is the number of nodes per layer).
Table~\ref{table:02} presents the theoretical definition of KL divergence with respect to input probabilities $q_{ij}$ and $p_{ij}$.

\begin{table}
    \caption{Definition of KL divergence}
   \label{table:02}
    \centering
    \begin{tabular}{ | c | c | c |  }
      \hline
       \thead{$p_{ij}$ \textbackslash  $\quad q_{ij}$ }	& \thead{$q_{ij} = 0$} & 	\thead{$q_{ij} \neq 0$}  \\
       \hline
	  $p_{ij} = 0$     & 0 & 	\makecell{not defined} \\
	  \hline
	  $p_{ij} \neq 0$  & 0 & 	\makecell{defined} \\
       \hline
      \end{tabular}
\end{table}

Equation \ref{eq:01} for the Kullback–Leibler divergence is defined only if for all $x$, $p_{ij}=0$ implies $q_{ij}=0$. Whenever  $q_{ij}=0$ the contribution of the corresponding term is interpreted as zero because
$\lim_{x \to 0} (x * \log_{2}x) = 0$.
The case of ``not defined'' takes place when there are more non-zero states than the number of non-zero reference states  (i. e., the cardinality of two sets satisfies the equation: $|Set(q_{ij} \neq 0)| > |Set(p_{ij} \neq 0)|$). This case indicates that a NN has insufficient representation power to encode input dataset into a class label.

 \emph{Expected properties:}
 It is expected that KL divergence will satisfy a list of basic properties as datasets, features, and NN capacity vary. For example, given an input dataset and a set of features, inefficiency (KL divergence) per layer should increase for an increasing number of nodes per NN layer. In another example, given a NN capacity, inefficiency should decrease for datasets with added noise or trojans. The relative changes are expected to be larger than the KL divergence fluctuations due to data reshuffling, data regeneration from the same PDF or due to re-training the same NN (referred to as sensitivity of KL divergence).

\underline{Computational Consideration about Inefficiency:}
The KL divergence computation considers computational and memory complexities since it must scale with increasing numbers of class labels, nodes, and layers.

 \emph{Memory concerns:}
 One should create a histogram with the number of bins equal up to $2^{nl}$ per class label and per layer which can easily exceed the memory size.
 For example, if a number of classes is $\approx 10$, a number of nodes is $\approx 100$, and a number of layers is $\approx 100$, then memory size is
 $\approx 2^{100} * 10 * 100 \approx 10^{33}$  bytes.
  In our implementation approach, we create bins only for states that are created by the training data which leads to the worst case memory requirement scenario to be $npts * 10 * 100$ bytes.

\emph{Computational concerns:}
 One should align measured histograms per class label to identify the states uniquely encoding each class in order to avoid the ``not defined'' case of KL divergence or the case of the same state encoding multiple class labels. To eliminate the alignment computation in our implementation approach, we modify the KL divergence computation to approximate the KL divergence according to Equation \ref{eq:02}. The computation of modified KL divergence $\widehat{D_{KL}}$ requires only collecting non-zero occurring states and calculating their histogram.

\begin{equation*}
\widehat{D_{KL}}(Q_{j} \parallel  P_{j})=\sum_{i \in Set(q_{ij} \neq 0)} (q_{ij} * \log_{2}{ q_{ij}} ) - \log_{2} \frac{m}{n}
\tag{2}
\label{eq:02}
\end{equation*}

While KL divergence satisfies $D_{KL} \leq 0$, the modified KL divergence $\widehat{D_{KL}}$ can be negative for those cases when  $|Set(q_{ij} \neq 0)| > |Set(p_{ij} \neq 0)|$. However, the negative value is lower bounded by Equation \ref{eq:03}. For negative values, the NN layer is insufficient for encoding input data to class labels.

\begin{align*}
\max_{Q_{j}} ( D_{KL}(Q_{j} \parallel  P_{j}) - \widehat{D_{KL}}(Q_{j} || P_{j}) ) = \notag\\
 - \sum_{i \in Set(q_{ij} \neq 0)} (q_{ij} * \log_{2}{ p_{ij}} ) - \log_{2} \frac{m}{n}
\tag{3}
\label{eq:03}
\end{align*}

The rationale behind modified KL divergence is that (1) the alignment is not important for sufficient efficient and inefficient models (it is primarily important for insufficient models), (2) the approximation assumes $p_{ij} \neq 0$ at all non-zero states $q_{ij} \neq 0$ which yields negative modified KL divergence values as indicators of insufficiency, and (3) the alignment is important for detecting poorly trained models which could be using the same states for predicting multiple class labels while leaving all other available states in a NN layer unused. For the last case, we assume that all models were properly trained and class labels are not assigned at random. Furthermore, the modified KL divergence addresses the problem of different within-class variations in training data which can lead to one class needing more allocated states than some other class. The modified KL divergence can be extended in the future by estimating within-class variations and assigning the number of states per class accordingly. In the following section we show how we use the modified KL convergence to detect the presence of trojans in a network.

\subsection{Approach to Trojan Detection}

Our assumptions are that (1) we have only testing datasets without trojans and (2) NN models with trojan and without trojan have the same accuracy.
We can simulate many varying NN models, with 4 example datasets containing 2 classes, and nine types of trojans.
The simulations assume close to $100 \, \%$ model accuracy on training data (with or without trojan). The  comparisons of modified KL divergence values are computed from \texttt{TwoT} and \texttt{TwT} models using datasets without trojans. The model \texttt{TwT} evaluated with datasets without trojans might have an accuracy less than $100 \, \%$ in simulations but the accuracy difference would be negligible in a real scenario (and the challenge models).

The comparisons are performed at each NN layer and for each class label. The simulation execution is interactive (i.e., execution time is on the order of seconds) and follows the steps:
(1) \emph{Select data}
(2) \emph{Train}
(3) \emph{Store model}
(4) \emph{Select other data}
(5) \emph{Restore model}
(6) \emph{Perform NN measurement.}

 Our assumption is that the magnitudes of KL divergence values for a NN model trained with a trojan embedded in a particular class (\texttt{TwT}) are smaller than the magnitudes for a NN model trained without trojan for the same class (\texttt{TwoT}). Our approach toward trojan detection is summarized in Figure~\ref{fig:trojan}. The axes correspond to the class-specific deltas between modified KL divergence of models \texttt{TwoT} and \texttt{TwT}. The dashed lines are set at a value $\sigma$ that corresponds to the sensitivity of $\widehat{ D_{KL} }$ to NN re-training as well as to data regeneration and re-shuffling. The notation ``to'' and ``from'' in Figure~\ref{fig:trojan} refers to our inference about trojans causing data points ``from'' one class to be mis-classified ``to'' another class based on the deltas defined in Equation~\ref{eq:trojan} where $P$ and $N$ are the two classes shown as blue and orange in the NN Calculator.

\begin{equation*}
	\begin{split}
\Delta(P)=\widehat{ D_{KL} }(TwoT/P) - \widehat{ D_{KL} }(TwT/P) \\
\Delta(N)=\widehat{ D_{KL} }(TwoT/N) - \widehat{ D_{KL} }(TwT/N)
	\end{split}
	\tag{4}
	\label{eq:trojan}
\end{equation*}

 \begin{figure}
  \resizebox{.5\textwidth}{!}{
\includegraphics[
  width=15cm,
  height=6cm,
  keepaspectratio,
]{./figs/trojan-detection-decision.png}
}
  \centering
  \caption{Trojan detection using the delta between modified KL divergence of models \texttt{TwoT} and \texttt{TwT} as defined in Equation~\ref{eq:trojan}. The values for dashed lines can be determined based on the sensitivity of deltas to data regeneration and reshuffling, as well as to multiple NN initializations and re-training.
    }
  \label{fig:trojan}
\end{figure}

\section{Experimental Results}
\label{exper_results}

Next, we describe the implementation details of NN Calculator and document properties of NN inefficiency measurements.

\subsection{NN Calculator}
NN Calculator is implemented in TypeScript. The code is available from a GitHub repository with the development instructions and deployment via GitHub pages \url{https://github.com/usnistgov/nn-calculator}.
The current list of features extracted from 2D datasets includes $X1, X2, X1^2, X2^2, X1*X2, \sin(X1), \sin(X2), \sin(X1*X2), \sin(X1^2+X2^2)$, and $X1+X2$. The code uses D3.js and Plotly.js JavaScript libraries for visualization. All analytical results are displayed in NN Calculator below the NN visualization. The results consist of a state histogram (bins for both classes) and tabular summaries. The state histogram is interactive while the numerical results are presented as tables with a unique delimiter for easy parsing.

To gain additional insights about state (although they might be computationally expensive for large NNs), simulations using NN Calculator report also the number of non-zero histogram bins per class, the states  and their counts per layer and per label for most and least frequently occurring states, the number of overlapping states across class labels and their corresponding states,  and the bits in states that are constant for all used states for predicting a class label. The additional information is reported for the purpose of exploring optimal NN architectures and investigating NN model compression schemes.

\subsection{Neural Network Inefficiency}

\underline{KL Divergence Properties:}
We verified and quantified desirable properties of the modified KL divergence defined in Equation~\ref{eq:02}, such as decreasing inefficiency for increasing amount of added noise and increasing inefficiency for increasing number of nodes.

\underline{Sensitivity of Inefficiency Measurement:}
We quantified the sensitivity of NN inefficiency measurement with respect to (a) data reshuffling and regeneration,  (b) NN re-training with different initialization, and (c) no-training as the worst case of poor training.
To look at the sensitivity of the NN inefficiency with respect to data regeneration, we performed the following: a NN model is trained for a dataset and stored in memory. Next, four datasets are regenerated and a standard deviation of inefficiency values are computed at each layer and for each class. Finally, the average value is computed over all standard deviations and the experiment is repeated for four 2D datasets with the results presented in Figure~\ref{fig:sensit}. From the data regeneration points in in Figure~\ref{fig:sensit},  we concluded that the average of standard deviations in inefficiency values larger than $0.1$ will indicate dissimilarity of models by other factors.

We performed similar sensitivity experiments for no-training and retraining with random initialization. Figure \ref{fig:sensit} includes the results for four datasets. The sensitivity to retraining is bounded to approximately the average of inefficiency standard deviations equal to $0.46$ while the same value for no-training is about 5 to 8 times larger and appears to be proportional to the complexity of the class distribution.

\begin{figure}
 \resizebox{.5\textwidth}{!}{
\includegraphics[
  width=15cm,
  height=6cm,
  keepaspectratio,
]{./figs/sensitivity-kldivergence2.png}
}
  \centering
  \caption{Sensitivity of inefficiency to stochastic regeneration of datasets from the same distribution, retraining and no-training with different random initialization. The box plot shows values computed from a set of standard deviations of modified KL divergence per layer and per class for the four datasets.
 }
  \label{fig:sensit}
\end{figure}

\underline{Comparison of Inefficiencies for Trojan Embeddings:}
Comparisons of models \texttt{TwoT} and \texttt{TwT} were conducted in NN Calculator using a NN with 6 hidden layers, 8 nodes per layer and 4 features including $X1, X2, X1^2, X2^2$ and $X1*X2$. The algorithmic and training parameters are set to learning rate: $0.03$, activation: $Tanh$, regularization: none, ratio of training to test data:
$50 \, \%$, and batch size: $10$.

Figure~\ref{fig:08} shows the delta between modified KL divergence values of models \texttt{TwoT} and models \texttt{TwT} for the two classes P (blue) and N (orange) and for the two trojans (T1 and T2) of different sizes (Figure~\ref{fig:08} left).  For both trojans, the delta KL divergence values are positive for the P (blue) class and negative for the N (orange) class: $\Delta(P)>0.454$ and $\Delta(N) < -0.702$. These values imply that a trojan is embedded in class P (blue) in both trojan cases and is encoding class N (orange) according to Figure~\ref{fig:trojan} (``From P to N'' $\rightarrow$ misclassified points labeled as P to N). Furthermore, as the size of a trojan increased from T1 to T2 by a size factor of 2.25, the ratio of deltas increased by $2.24$ for class N and by $2.37$ for class P.

\begin{figure}
 \resizebox{.5\textwidth}{!}{
\includegraphics[
  width=15cm,
  height=6cm,
  keepaspectratio,
]{./figs/comparison-trojan-size.png}
}
  \centering
  \caption{Comparison of inefficiencies between models \texttt{TwoT} and \texttt{TwT}, and embedded orange trojans T1 and T2 with different sizes (see Figure~\ref{fig:02}, top row). The plot shows the values of $\Delta(P)$ and $\Delta(N)$ for T1 and T2 at each NN layer.
 }
  \label{fig:08}
\end{figure}

Figure~\ref{fig:10} illustrates the delta between modified KL divergence values of models \texttt{TwoT} and models \texttt{TwT} for the trojans T8 and T9 whose embeddings differ in terms of the number of classes and the number of class regions. First, we observe for trojan T8 that
$\Delta(T8/P) > 0.48$ and
$\Delta(T8/N) < -0.769$. These values imply that the trojan T8 is embedded in class P (blue) according to Following Figure~\ref{fig:trojan} (``From P to N'').

We recorded much lower delta values for the trojan T9 than in the previous comparisons. This indicates the much higher complexity of modeling the spiral dataset than circle, exclusive OR, or Gaussian datasets and therefore lower inefficiency values measured at NN layers. Based on the sensitivity values shown in Figure~\ref{fig:sensit} ($0.1$ for data regeneration and $0.5$ for re-training), we could  infer that the trojan T9 is likely in both classes based on the placement of
the point $[\Delta(T9/P) > -0.034, \; \Delta(T9/N) > 0.035]$ in Figure~\ref{fig:trojan} (i.e., the sub-spaces ``From N'', ``From P'', ``Not detectable'', and ``From N to P'' $+$ ``From P to N'').

Due to the discrete nature of the spiral pattern, the P class (blue) occupies a longer curve than the N class (orange). This contour length ratio
 ($P:N \approx 12.31:7.33$) can explain why
 ($\Delta(T9/P) > \Delta(T9/N)$  for almost all layers. However, we are not able to make any inferences about the number of regions from Figure~\ref{fig:10} (right) other than that the complexity of modeling class P or N in the case of T8 is more inefficient than modeling class P and N in the case of T9 by comparing the deltas of modified KL divergence values.

\begin{figure}
 \resizebox{.5\textwidth}{!}{
\includegraphics[
  width=15cm,
  height=6cm,
  keepaspectratio,
]{./figs/comparison-trojan-spiral.png}
}
  \centering
  \caption{Comparison of inefficiencies between models \texttt{TwoT} and \texttt{TwT}, and embedded trojans T8 and T9 with different number of classes (1 or 2) and class regions (1 or 4).
 }
  \label{fig:10}
\end{figure}

\section{Discussion about Trojan Detection}
\label{section:discussion}

One can obtain several additional useful insights from interactive analyses in NN Calculator before designing a trojan detection algorithm. In many of the results, it is apparent that the encoded class information is not in one layer but spread across multiple layers. Thus, trojan detection must include comparisons of vectors of $\widehat{ D^{l}_{KL}}$ across all layers $l$. Furthermore, the encoding of the same training data in NN can have multiple solutions, especially in inefficient NN and therefore the comparison of vectors of $\widehat{ D^{l}_{KL}}$ must include again a statistical nature of such solutions. Finally, the last layers carry less information about trojans because they serve the purpose of a final decision maker which should appear fair for datasets without trojans. This could be accommodated by weighting the layer-specific vector elements. From a global algorithmic design perspective, designing an actual trojan detector must still consider the trade-offs of doing all pair-wise model comparisons versus clustering all vectors of $\widehat{ D^{l}_{KL}}$ to identify the cluster of model TwoT.

\section*{Summary and Future Work}

We designed NN calculator and an inefficiency measurement for detecting trojans embedded in NN models. Our work is focused on measuring neural network inefficiency using KL divergence as a means to  advance mathematical and statistical modeling of neural networks. Current modeling efforts suffer currently from a steep learning curve, hardware requirements, and time delays between experimental runs. Some of these drawbacks can be overcome by the NN Calculator since it is interactively accessible using a browser at \url{https://pages.nist.gov/nn-calculator/} and performing experiments does not require specialized hardware (i.e., GPU cards) nor long waiting times.

\section*{Acknowledgement}
The funding for Bajcsy and Majurski was provided by IARPA, and for Schaub was provided by NCATS NIH.

\section*{Disclaimer}
Commercial products are identified in this document in order to specify the experimental procedure adequately.
Such identification is not intended to imply recommendation or endorsement by the National Institute
of Standards and Technology, nor is it intended to imply that the products identified are necessarily the best available for the purpose.

\bibliographystyle{aaai}

redpajama_set_name: RedPajamaArXiv


Der Baumeister Johann Arnold Nering erhält vom brandenburgischen Kurfürsten Friedrich Wilhelm den Auftrag, neben dem Potsdamer Stadtschloss eine Orangerie (Pomeranzenhaus) zu bauen - als Winterquartier für mediterrane Pflanzen aus dem Lustgarten.
König Friedrich Wilhelm I. lässt einen Teil des Lustgartens zum Exerzierplatz planieren, aus der Orangerie wird ein Pferdestall.
Sanssouci-Architekt Georg Wenzeslaus von Knobelsdorff leitet im Auftrag von Friedrich II. Umbau und Erweiterung des Marstalls, der plastische Schmuck über den Eingängen wird von Friedrich Christian Glume geschaffen.
176 Jahre war das Haus als Pferdestall genutzt worden. Nach der Abdankung von Kaiser Wilhelm II. werden die Pferde 1918 versteigert, im Gebäude wird ein Garnisonsmuseum eingerichtet.
Der Potsdamer Kunstverein stellt im Marstall aus.
Das Haus wird durch einen britischen Luftangriff beschädigt.
Nach provisorischer Instandsetzung zeigt das Städtische Museum die erste historische Ausstellung der Nachkriegszeit.
Die Ruinen der Garnisonskirche werden gesprengt.
Potsdam's municipal council is informed about the idea of the establishment of a film museum.
The municipal council decides to establish a film museum in the dilapidated building of the royal stables - the Marstall - in Potsdam's historical city centre. Extensive restoration and reconstruction work begins.
Upon opening, the "Film Museum of the GDR" offers an exhibition on technical equipment and shows films in its cinema.
A permanent exhibition on Ufa and DEFA history is opened on the ground floor of the Marstall. Travelling exhibitions on DEFA films and an ambitious film programme supplement the offers.
The travelling exhibition on director Wolfgang Staudte, first shown in Düsseldorf, is the first inter-German joint venture in the context of the cultural conventions between GDR and FRG.
A GDR regulation requiring the film museum to exclusively collect film and cinema technology becomes invalid. The collections are given their own location, stocks are growing fast. Estates of former DEFA members are acquired. A new staff is running the museum and its departments.
The film museum, so far a branch of the State Palaces und Gardens Potsdam-Sanssouci, becomes a museum of the federal state. The Marstall becomes a cultural centre featuring film festivals, media projects, seminars, conferences, discussions and, of course, attractive exhibitions. Film talks with contemporary witnesses of the DEFA years supplement the collections.
First international exhibition in cooperation with the Institut Français.
The interior of the Marstall is rebuilt according to international museum standards, and the restored Welte Cinema Organ is integrated into the cinema.
The second permanent exhibition "Film City Babelsberg" is opened and the film museum publishes a book on DEFA film history, which is to become a standard work of reference. The redecorated café becomes a cultural meeting point.
The cinema is reopened after reconstruction measures. It has been equipped with a state-of-the-art sound system.
The cinema is equipped with two new Ernemann projectors.
The museum celebrates its 20th anniversary with a party and a book on the technology collection.
Funding that has been granted on the occasion of the anniversary for the first part of the permanent exhibition is released. Work on the exhibition "Babelsberg -Faces of a Film Metropolis" is running at top speed.
On 1 April, the ribbon is cut at the third permanent exhibition "Babelsberg -Faces of a Film Metropolis". In this context, the redecoration of the foyer is carried out by Potsdam artist Steffen Mühle.
On 1 June, the family exhibition "Fairyland Babelsberg" is opened.
In April, the museum celebrates its 25th anniversary. On this occasion, the Federal State of Brandenburg donates financial support to equip the cinema with new seats, lamps, carpets and a beamer by the end of the year.
The cinema has been equipped with new chairs and looks better than ever. All digital equipment is up-to-date. Plans are made to rebuild the Brandenburg State Parliament at the site of the former city castle - to which the Marstall once belonged. The reconstruction of the surrounding streets also requires the replanning of the museum forecourt.
The reconstruction of the streets surrounding the museum begins. In autumn, the new, golden FILMMUSEUM POSTDAM logo is mounted above the museum entrance.
For the first time ever, the museum presents an exhibition on a Studio Babelsberg production - the children's film "Hexe Lilli" - before its theatrical release.
October sees the completion of the reconstruction of the streets that accompanies the rebuilding of the Brandenburg State Parliament at the site of the city castle.
The museum forecourt has been redesigned. On the occasion of Sandmann's 50th birthday, the museum presents its 5th Sandmann exhibition in its 28-year history. Once again, visitors pour into the museum.
Prior to the opening of the new permanent exhibition "The Dream Factory - 100 Years of Film in Babelsberg" on 3 November, preparations (which started in 2008) are in full swing.
In May, a state of the art 2K digital cinema projector was installed in the cinema's screening rooming. From now on, in addition to analog prints, all digital film formats can be brought to the screen.
On 1 July, the museum - an institution of the Ministry of Science, Research and Culture up to this point - is integrated into Film University Babelsberg KONRAD WOLF.
Renewing of the fire alarm/safety system.
Reopening of the restaurant "Genusswerkstatt"
The jewel of the technology collection is the Bioscope projector, a permanent loan from the Bundesarchiv-Filmarchiv, which Max Skladanowsky used when he first showed his films on 1 November 1895 at the Wintergarten in Berlin - this was the first public film screening in Europe. The Bioscope is on display in the museum foyer.
The Bioscope, finale of the new variety show at Berlin Wintergarten that started on 1 November 1895, was announced grandly at that time: "The most interesting invention of modern times". Constructed by Max Skladanowsky, the Bioscope was a projector for the "representation of living photographs". The Bioscope shows are considered to be the first public film screenings in Germany.
The technical procedure was quite complex: Skladanowsky recorded up to 48 individual frames on a 1.5-m roll film without perforation. The distance from frame to frame varied due to the missing perforation. The frequency was 8 frames per second, which is inadequate for the projection of "moving images". For this reason, Skladanowsky cut up the developed negative roll and put the images 1, 3, 5, etc. in one row and the images 2, 4, 6, etc. in a second row. During this process, he also adjusted the distances between the images. Then, he copied the rows onto film. This positive roll was subsequently cut into strips that Skladanowsky fixed to bands using metal eyelets. These bands were perforated and the holes were also reinforced with metal eyelets. Afterwards, Skladanowsky stapled the two bands - the one with the even and the one with the uneven frame numbers - together, creating two rings.
These ring films could now be projected with the Bioscope. The intermittent shuttle mechanism was a worm wheel mechanism, for which Skladanowsky received a patent on 1 November 1895. While one image was projected from the first image strip, a shutter covered the second lens, and the second image strip was advanced by one frame. This made a change of frames possible that was fast enough to be perceived as movement when projected.
In terms of technology and image quality, Skladanowsky's equipment and screenings were already behind the times. Skladanowsky needed two filmstrips, which, in comparison to the usual system with just one filmstrip (as successfully used by the Lumiéres), meant significantly more technical effort and disadvantages.
The Bioscope was the central exhibit of Land Brandenburg at the EXPO 2000.
German and international guests from the film business and from politics visit Filmmuseum Potsdam for lectures, readings, discussions and talks.
November 2006: Vortrag zur Filmnacht "Vampire - Die Sehnsucht nach Unsterblichkeit"
Januar 2004: Werkschau / Juni 2007: Freilichtkino zur Ausstellung "Lola, Lenin und Mein Führer: X Filme"
Mai 2006: "LOLA Festival 2006"
Januar 1994: "Krauses Kneipe" (Aktuelles Potsdamer Filmgespräch) / September 1997: "Raus aus der Haut (Aktuelles P. F.) / April 1999: "Nachtgestalten" (Aktuelles P. F.) / September 2000: "Die Polizistin" (Aktuelles P. F.) / September 2002: "Halbe Treppe" (Aktuelles P. F.) / März 2003: "Herr Wichmann von der CDU" (Aktuelles P. F.) / April 2004: Eröffnung der Ständigen Ausstellung / November 2004: "Filmstadt Babelsberg" (Filmnächte) / Juli 2007: "Debütfilme"
April 2007: Eröffnungsvortrag zur Film- und Veranstaltungsreihe "Work in Progress"
Februar 1991: Besuch der Ausstellung "Zwischen Gestern und Morgen"
Mai 1999: Gespräch zur Filmreihe "Frauenbilder im polnischen Spielfilm"
Juli 2008: Festveranstaltung zur Verlegung der "Stolpersteine"
Dezember 2008: Buchpräsentation "Gesichter der DEFA"
Mai 2007: Eröffnung der Ausstellung "Lola, Lenin und Mein Führer: X Filme"
April 2006: Filmgespräch zu "For the Living and the Dead"
Februar 1999: Besichtigung der Ausstellung "Leni Riefenstahl"
Dezember 2002: "Kühe vom Nebel geschwängert" (Aktuelles Potsdamer Filmgespräch) / April 2008: "Schwule Filmnacht Potsdam"
März 2002: Filmabend zur Foyerausstellung "Carl Raddatz"
März 2005: Lesung zur Ausstellungseröffnung "Verehrt, Verfolgt, Vergessen"
Januar 2002: Gespräch zur Filmreihe "Die Trauer des Afghanen" / November 2005: Diskussion "Soviel Statt war nie: Krise und Zukunft"
Filmfestival "HOLLYHOOD - Hip Hop Social Justice"


With RSA Films' co-founder Ridley Scott, Jen recently executive produced ALIEN: COVENANT "In Utero" for Scott's latest sci-fi blockbuster, Alien: Covenant. This comes on the heels of the groundbreaking "The Martian VR Experience," which Jen also executive produced with Scott. Since joining RSA in 2014, she's executive produced Nike's first-ever scripted YouTube series, the viral blockbuster "Margot vs Lily," via Wieden + Kennedy, which won a Cannes Gold Lion and a Webby Award. Jen brings over 20 years of global production experience to RSA. She began her career in her native New York as an agency producer before moving to California. There she embarked upon a freelance career that included many years with Wieden + Kennedy. That work took her around the world collaborating closely with celebrated athletes and icons, with many of those commercials getting top honors at Cannes, D&AD, AICP and the One Show. Jen also worked with CP+B, 72andSunny, 180 and GSD&M, and on the production side as a line producer and EP. In 2013, Jen was tapped to partner with the ECD of Samsung's global business to open a boutique division of Leo Burnett in LA where she acted as SVP, creative content production.


text:
Quake hits southern Siberia
Monday, October 13th 2003, 12:00 am
MOSCOW (AP) _ An earthquake on Monday shook a region in southern Siberia that has been hit by a series of powerful tremors since the end of last month. No damage or casualties were immediately reported.
The quake, which had an estimated magnitude of 5, had its epicenter about 20 miles south of the town of Aktash in the mountainous Altai Republic, said Viktor Beltsov, a spokesman for Russia's Emergency Situations Ministry.
Since Sept. 27, several quakes in the same area which measured up to 8 in magnitude destroyed numerous buildings, leaving hundreds of people homeless.
Those quakes also cracked roads and bridges and knocked out power and water supplies in the southern portion of the region, which borders Mongolia and Kazakhstan. No deaths were reported, but several people were lightly injured.

redpajama_set_name: RedPajamaCommonCrawl


text:
Some of the Most Racist Things People of Color Have Heard on Dates
ByJulie Zeilinger
Grappling with ghosting or the politics of texting are annoying parts of the dating game, but adding racist statements and microaggressions takes the difficulties to another level.
When it comes to courtship, even the slightest forms of racist discrimination emerge on every level. But it's not always easy to take a stand against such treatment. One young dater, Maria, told Mic, she "always shrugged it off" because she "never knew how to express how poorly those statements made me feel about myself" and "didn't have the language to really understand why those things were not okay."
Several millennials of color shared some of the racist things told to them on dates:
"You're not my 'type.'"
Mic/Getty Images
While many people claim to (and may very well have) a particular "type" they're attracted to, there are undeniably some who use this framework to couch what is actually racial discrimination.
"If you say that your type is a blond, blue-eyed guy or a 'cute Jewish boy,' it's a waste of my time to even make an attempt at dating you," Ron told Mic. "It's obvious that you can't see past race as far as the concept of attractiveness is concerned." Any time he's rejected, he said, he has to wonder if "they didn't like me or if I just don't fit the acceptable 'type.'"
Annie* has also experienced this firsthand. "I once started seeing a Persian guy in high school who was like a perfect match for me," she told Mic. "One day he told me that he thought I was really beautiful and he loved my company, but he would never seriously date a black woman. He said he was only interested in dating white women with blonde hair and blue eyes. He said that was his 'type.'"
"You're so beautiful for a Mexican."
Richard Shotwell/AP
On the other hand, many people of color have encountered potential partners who are all too eager to date them based on their race alone.
Men have told Chloe, who is half Chinese and half Irish, that she's "very exotic" and "the closest they have been to Asian." Aly, who is also half Chinese, has similarly faced such fetishization. One person she matched with on a dating app, for example, used the oh-so-eloquent opening line "I like Chinese."
Maria noted that racial stereotypes about Latina women's bodies has informed others' sexual interest in her.
"One ex-boyfriend actually said that the only thing that gave away that I was Mexican was my ass," Maria told Mic. Another partner once referred to her as being "so beautiful for a Mexican."
"I only date men of color because they're good in bed."
In addition to their appearance, people of color have encountered individuals whose interest in dating them is based on race-based preconceived notions about their personality or abilities.
Ron said he "dated a woman a few years back that only dated men of color," based on the belief that "they were better in bed" — a belief that, while superficially flattering, ultimately felt "a bit insulting and demeaning, implying that everyone who is a certain race shares a certain characteristic. It's pretty much the same situation as being told that all black guys are aggressive, but with a positive spin that doesn't make it any less racially charged."
"Don't your parents want you to have an arranged marriage?"
Mic/Flickr
Of course, there are those who can't just see people of color as individuals and regard them as inherently embodying racial stereotypes.
Sahil has heard everything from a girlfriend stating he couldn't cook in her kitchen because "it will start to stink" to asking, "Don't your parents want you to have an arranged marriage?" Another asked him to shave his beard so he would "blend in better."
"I even had a girlfriend who didn't want to get on a plane with me because she was afraid she'd be more likely to get randomly checked traveling with a bearded brown guy," he told Mic.
Aly said the racism she's experienced usually comes from men "trying to find more about me," which is intended less to blatantly offend her than it is to point out "the fact that I'm something untraditionally American," she told Mic.
"Because I'm mixed, I [hear racist statements] a lot further into discussion," she said, adding that people frequently ask her what her background is, made objectifying statements about Asians being beautiful and ask to guess where she's from. Her answer to the latter (Ohio) often surprises people far too often.
Alternatively, when people of color don't fit stereotypes affiliated with their race, they are often subjected to insulting comments.
Because she doesn't speak English with an accent, Paula told Mic, "many people have assumed I'm trying to exoticize myself or I'm embellishing my non-Americanness for attention," especially, but not exclusively, in a romantic context.
"I answer a call from my parents and speak perfect Spanish and they freak out because it turns out I was telling the truth," she said.
It's up to all of us: Even with interracial marriages on the rise, that racism occurs in the context of finding love demonstrates just how deeply ingrained these attitudes still are in American culture — and why pushing back, in individual relationships as well as starting dialogues like these, is crucial.
*Names have been changed to allow subjects to speak on private matters.


I am struggling mightily with this one. I love ruby and rails, but more and more I am resenting the constraints of the controller architecture, the router, and activerecord, and I miss writing and tweaking raw SQL. This is not to say that I don't love rails for quick and dirty stuff, and it has done wonders for writing MVC web applications, but I miss being able to break that pattern where it applies and being able to understand the result. I view rails just like I view Cocoa Bindings. I love the magic that they do, but there is a performance cost, and it is suboptimal for me to have little to no visibility into how they do what they do.
My friends who are really into rails tell me of course, you can use it without those modules, namely ActiveRecord, but if my problem is with ActiveController, and its routing magic, along with the conventions then what is the point. I was looking at MERB, but it is difficult to say what the result will be in rails 3. But almost no matter how I consider the outcome of that Merb will inevitably become more Rails like. I mean, rails is the 800 lbs gorilla in the web dev space, who would argue with Rails and win?
Well, there are plenty of examples of ugly PHP sites, and there are some examples of almost elegant code in PHP. Better yet, there is no magic, I can avoid heavy frameworks if I choose and still have my choice of easy deployment options with mod_php. The rails community keeps saying that hardware is cheap, and it is if you have money to begin with. If I need to deploy on a $30 / month host, it is very hard to deploy a rails app with a homegrown framework on rack. I am sure they exist, but PHP hosting has gotten so cheap as to almost be an afterthought.
Some of the PHP code that I have written recently is quite clean and readable. Its using classes and MVC to a degree, but I have objects that implement a hydration base class that allow them to persist their contents by overriding some simple parameterized SQL via PDO in the subclass. That is really all of the database abstraction that I need. If I want to persist objects, I'll just save them to disk. Ruby's marshal works just fine for that, so does PHP's serialize / unserialize. If I want to use a relational database, I'll write SQL and let it play to its strengths instead of pretending that it doesn't exist.
There are totally justifiable situations in which Rails is the absolute best choice. I work in Rails every day at work and love it for that particular problem domain. I just have started realizing that I have been seeing the world as Rails shaped nails. There is no one language, framework, design pattern, or library that is the end-all-be-all method for solving problems. It takes good practices and pragmatic choices to choose the best tool or tools for the job. I was getting away from that, now I am coming back.


text:
Q: PHP Editing data from database per category data My tabel database looks like this:
id | name         | Job
-----------------------------
1  | Andrew       | Engineer
2  | Fahmy        | Designer
3  | Fitriani     | Animator
4  | Karin        | Animator

i want to display data editing in my web, as you can see, there are two people with same job there, so if i do this:
$query="select * from tb_employees";
$show=mysql_query($query);
while ($data=mysql_fetch_array($show)){

$id= $data['id'];
echo"

      $data[id_usulan]
      $data[name]
      $data[job]
      Edit | Delete

";

It will display all data in that table.
But what i want to do is to edit data per jobs, like this:
id | Job       | Option
---------------------------------------------
1  | Engineer  | Edit - Delete
2  | Designer  | Edit - Delete
3  | Animator  | Edit - Delete

(this is my editing interface, since my reputation is so small, i can't post image of my interface, sorry)
So if i Click edit in Animator, i want it to show per person with Animator job editing like this:
id | Name      | Option
---------------------------------------------
1  | Fitriani  | Edit - Delete
2  | Karin     | Edit - Delete

(this is my editing interface, since my reputation is so small, i can't post image of my interface, sorry)
So if i click edit in Karin, i want it to display Karin data editing interface, i use Mysql for database.
Sorry for my bad language.

A: You can get data per job by the following query
$query="select * from tb_employees group by job ";

And when you will edit then run another query
$query="select * from tb_employees where job=$data[job] ";

A: Run a query like this
$query="select job from tb_employees";

Than run it like this
if(isset($_GET['id']))
{
$id=$_get['id'];
    $sql="select name from tb_employees where job='$id'";
    $show=  mysql_query($sql);

    while($data=  mysql_fetch_array($show));
    echo $data['name'].'
';
}

redpajama_set_name: RedPajamaStackExchange


All content is 3rd party and not directly or support by kodi themselves. Tv Addons just did an update on Exodus for kodi 17. This will work for kodi 17 and soon will no longer support an older version. Tons of HD quality content for Exodus. Jump on kodi 17 and get the update.
Lmao U left captchas on.. and didn't enable set by provider wow…so now your gonna get captchas first with Tv show mainly and 20 is too long..But hey Ur the expert Lol.
20 is not long if you have a bad internet and also if you have a low end device. 20 might even be to short.
Oh and no U don't have to upgrade to 17 ..
Great videos Rohas keep them coming ?
I've used a couple of builds and TVaddons, which crashed. What is the best build? I do movies, tv and a lot of sports, ppv. Whats the best?


text:
Q: Move window to desktop: Keyboard shortcut I'm running Linux Mint 17.1 (KDE). I know that I'm able to move windows from one desktop space to another by right-clicking in the title bar. I also know I'm able to do so from the command line with wmctrl, which first requires me to know the window ID. In my case, I can do this:
wmctrl -l

This lists open windows with their respective IDs:

0x04c00012  0 laptop2 Downloads – Dolphin
0x00800015 -1 laptop2 Yakuake
0x03800012  1 laptop2 sh-format.sh – Kate

(along with several others).
If I then type:
wmctrl -i -r 0x03800012 -t 1

then I can move the Kate window to desktop 2.
My question is this:
I would like to either (1) set up a keyboard shortcut to move the active window to a different desktop (I'm willing to set up separate shortcuts per desktop), or (2) identify the window ID for the active window, so that I can script a shortcut.
Is there a way to do this?

A: I found a solution. Rather than delete the question, I thought I'd post what I have. I'm sure someone or other has the same question somewhere, and if not, I'm sure there's still a cleaner way of doing this. Here goes.
(This, by the way, is to make a keyboard shortcut to move the active window to workspace 4.)
First, I wrote a script (~/.move4.sh):
#!/bin/bash
a=`xprop -root _NET_ACTIVE_WINDOW | rev | cut -d ' ' -f1 | rev`;wmctrl -i -r $a -t 3

(the "-t 3" is because the workspace numbering starts at zero, so "-t 3" moves the window to workspace 4)
I made the script executable:
chmod u+x ~/.move4.sh

Finally, I went into system settings and made a custom global keyboard shortcut (I used Meta+4), and assigned it to run this script. Worked like a charm.
I feel silly for not having come up with it sooner, but I think I had to post a question to come up with an answer. Sorry.


text:
Q: Unable to get the feature collection data while exporting as kml file I am trying to get the kml file by giving simple polygon data but not able get exact kml Please suggest me if iam wrong.
code snippet:
GeometryFactory geometryFactory = JTSFactoryFinder.getGeometryFactory(null);

WKTReader reader = new WKTReader( geometryFactory );

Encoder lEncoder = new Encoder(new KMLConfiguration());

OutputStream lFileOutputStream = null;

lEncoder.setIndenting(true);

try
{
    lFileOutputStream = new FileOutputStream("my.kml");

    Polygon polygon = (Polygon) reader.read("POLYGON((20 10, 30 0, 40 10, 30 20, 20 10))");
    lEncoder.encode(polygon, KML.kml, lFileOutputStream);
    lFileOutputStream.close();
}
catch (FileNotFoundException e1)
{
    e1.printStackTrace();
}
catch (ParseException e)
{
    e.printStackTrace();
}
catch (IOException e)
{
    e.printStackTrace();
}

output getting in KML File
< ?xml version="1.0" encoding="UTF-8"?>
< kml:kml xmlns:atom="http://www.w3.org/2005/Atom"
    xmlns:kml="http://www.opengis.net/kml/2.2" xmlns:xal="urn:oasis:names:tc:ciq:xsdschema:xAL:2.0"/>

A: The KML Encoder (like most of GeoTools) works with features not geometries so you need to wrap your polygon in a feature, this gives you the opportunity to add some attributes to make your KML more useful too.
public static void main(String[] args) throws IOException {
    File t = new File("test1.kml");
    SimpleFeatureTypeBuilder builder = new SimpleFeatureTypeBuilder();

    builder.setName("test");
    builder.setNamespaceURI("http://www.geotools.org/");
    builder.setSRS("EPSG:4326");
    builder.add("the_geom", Point.class);
    // builder.add("id",Integer.class);
    SimpleFeatureType schema = builder.buildFeatureType();
    ArrayList<SimpleFeature> list = new ArrayList<>();
    for (int i = 0; i < 10; i++) {
      list.add(GenerateRandomData.createSimplePointFeature(schema));
    }
    SimpleFeatureCollection collection = DataUtilities.collection(list);
    exportFeatures(t, collection);

  }

  static void exportFeatures(File outfile, SimpleFeatureCollection features) throws IOException {
    OutputStream outputstream = new BufferedOutputStream(new FileOutputStream(outfile));
    Encoder encoder = new Encoder(new KMLConfiguration());
    encoder.setIndenting(true);
    encoder.encode(features, KML.kml, outputstream);
  }


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We're all either believers or skeptics when it comes to the paranormal, but that doesn't mean we know which of the two we really are. This is the bottom line of Light from Light, an "anti-horror" drama from director Paul Harrill (Something, Anything), who also wrote the screenplay. His moody new film is essentially a ghost-hunter—or perhaps "ghost-whisperer"— narrative without the YouTube celebrities or game show gimmicks the practice has become known for in recent years. In fact, Light from Light is barely about ghosts at all.
The film begins by introducing Shelia (Marin Ireland), a single mother who has a background as a paranormal investigator in Tennessee but is "between groups." Her day job, however, couldn't be farther from her true passion. She's an associate at an airport car rental desk, vacuuming out vehicles and spending her slow nights wasting time on her phone. Her teenage son Owen (Josh Wiggins) has become largely independent and dependable, forcing Shelia to reconsider what might come next for her after he leaves (metaphors, anyone?).
After a recommendation from a priest, Shelia begins to help Richard (Jim Gaffigan), who senses his house might be haunted after the recent passing of his wife. Shelia dives back into the world of ghost-hunting with her son and his friend Lucy (Atheena Frizzell), but the film begins to question what ghosts really are, as well as the unique ways they can be experienced. It also hangs to a subplot involving Owen's reluctance to engage in a relationship with Lucy, because he can't justify beginning something that will eventually end and cause him pain.
Though Shelia is the main protagonist, some moviegoers may wonder about Gaffigan, a usually comedic actor. This is not a film that uses him for laughs, but instead draws a deep, unsettling grief that proves his dramatic range. Marin Ireland has been delivering wonderful performances for years, but as lead she is as enchanting as the supposed ghosts she desperately wants to find. Her body language and facial expressions are a mystery unto their own, and though we never seem to get a full picture of her backstory beyond vague asides, there's an assurance that we do know this person. This person exists, sometimes humbly in the background, putting others first and never asking for much in return.
Light from Light is not loud or energetic. It's not frightening. It's simple. It's quiet and patient. It's no surprise that this was co-produced by James M. Johnston from A Ghost Story, a film that also takes stabs at rethinking the usual "haunted house" motif (the film was also produced by Elisabeth Moss, Tim Headington, and Theresa Page).
It's almost become cliché at this point to mention that a film about the supernatural deals with grief, which is why it's a bit underwhelming to see Light from Light dabble in what is an already shallow pool of themes. But there is merit to how the film never forgets who its lead is (Ireland), and she is clearly dealing with traumas that do ring of something harder to put into words than grief or depression.
A more generous read would be that Harrill is trying to make a different case for how horror can reveal new truths about mental illness. Grief and depression aren't always tense and thrilling like you may find in Hereditary. They tend to have a slower suspense, one that is atmospheric instead of terrifying. For many viewers, that may just make Light from Light a deeply powerful, invaluable experience.


This unisex crew neck style t-shirt is one of the most comfortable and stylish options for women. Made with the softest, combed ringspun poly/cotton blend. Tees run larger on women. If you fall between sizes go with the smaller size for a slimmer fit. This tee is made with vinyl silver material.
So you may preserve graphics on shirt turn it inside out and iron on low heat.


text:
MRI signs helpful in the differentiation of patients with anterior ischaemic optic neuropathy and optic neuritis
Valentina Daphne Petroulia1,
http://orcid.org/0000-0001-9837-6652Dominik Brügger2,
Robert Hoepner3,
Rino Vicini2,
Anna Winklehner4,
http://orcid.org/0000-0002-5150-9320Mathias Abegg5,
http://orcid.org/0000-0001-7502-4814Franca Wagner4
1Diagnostic and Interventional Neuroradiology, Inselspital Universitatsspital Bern, Bern, Switzerland
2Ophthalmology, Inselspital University Hospital Bern, Bern, Switzerland
3Neurology, Inselspital University Hospital Bern, Bern, Switzerland
4Diagnostic and Interventional Neuroradiology, Inselspital University Hospital Bern, Bern, Switzerland
5Ophthalmology, University of Bern, Bern, Switzerland
Correspondence to Dr Franca Wagner, Diagnostic and Interventional Neuroradiology, Inselspital University Hospital Bern, Bern 3010, Switzerland; franca.wagner{at}insel.ch
Background/Aims The aim of this study was to identify specific MRI characteristics of anterior ischaemic optic neuropathy (AION) and optic neuritis (ON) that would aid in the differentiation between these two diagnoses.
Methods We retrospectively analysed a consecutive case series including all patients with an MRI study of brain and orbit and the clinical diagnosis of either ON or AION. We examined the scans for restricted diffusion of the optic nerve, optic sheath diameter, enhancement and location of enhancement of the optic nerve and distribution of the white matter lesions.
Results Fifty patients met the inclusion criteria. We found an accuracy of 0.98 for the discrimination between AION and ON based solely on parameters extracted from MRI data. Dominance analysis to determine the most influential parameters showed that the enhancement pattern of the optic nerve and distribution of the white matter lesions had the biggest impact on the classification and led to a discrimination accuracy of 0.9 when used alone.
Conclusion In patients with an inconclusive clinical diagnosis, optic nerve enhancement pattern and distribution of white matter lesions can aid in the diagnosis and differentiation between AION and ON. Diffusion-weighted imaging did not add significant information to the diagnosis or help to differentiate between the two conditions.
diagnostic tests/investigation
All data relevant to the study are included in the article or uploaded as supplemental information.
VDP and DB contributed equally.
Contributors VDP: collected the data, performed imaging and data analysis, co-wrote the manuscript draft. DB: collected the data, performed statistical analysis, co-wrote the manuscript draft. RH: collected clinical data. RV: contributed imaging data and analysis. AW: contributed imaging data. MA: conceived and designed the analysis and corrected the final manuscript. FW: conceived and designed the analysis, supervised the study, reviewed and corrected the final manuscript. Responsible for the ethical statement.


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Parties last 2-3 hours and the price range is $25–$45 depending on canvas size and media.
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I've been a bit sidetracked by all my projects as of late and neglecting to post. One such project that has consumed a bunch of my time is the design and fabrication of new pillar vents for the Skyline (ongoing since December 2013).
The majority of the trim on the car was in pretty good shape when I purchased her but, being 44 years old, it had a few pieces that needed attention. With such a prominent location on the wide C-pillar, these vents/badges with bubbly pitted chrome and poor paint job needed to go.
Because replacements are so expensive, I decided to spend that money productively and make my own versions while learning new fabrication skills along the way. Below is the experimentation/build process and the final product which I happily installed today.
Like many of my recent projects, this one started out with experimentation in rapid prototyping technologies. This first design was done in haste and only served to test the printing accuracy and overall dimensions before committing to more expensive metal pieces. Nevertheless, it was exciting to have a solid piece in hand!
After confirming that the dimensions of the plastic print would fit, I went to work cleaning up the design for the limitations of stainless steel. It was at this point that I decided to make the outer ring and center cap in two pieces (so I could swap centers at whim). After all, variety is the spice of life.
This is the raw stainless steel outer ring before any finishing work.
I had lots of machining, filing, polishing and paint work ahead of me.
The various parts all together. Clockwise from upper left: White plastic prototype, stainless ring, red plastic center caps, original "S"vent.
The first thing I tackled was polishing the outer ring and tapping the mounting holes.
Next up was the VicariousGear center cap in paint. I decided to spray directly on the untreated print surface to see how it would turn out.
I liked the textured look so decided to proceed.
After painting came a technique I just started to learn about called gilding. I decided to use silver leaf and the gilding process to make the logo really shine. Another reason I decided to do this is because I have been trying to integrate some Japanese traditions into my fabrication choices.
After painting on an adhesive (called "size") onto the areas where you want the foil to adhere, a super thin silver leaf is placed on top.
The silver is so thin that any spots that aren't glued will brush away. Multiple layers were required to really bring out the metallic sheen.
After completing the first center cap, the outer ring received paint. Then it was thrown onto the rotary table of my CNC mill for some fancy finishing cuts.
Since the other cap was done with a textured finish, I decided to make the one with the Prince logo smooth. Automotive body filler was applied and sanded down over a few base coats of paint.
Then once I was happy with the prepared surface, a couple thick coats of paint were applied. Finished surface on the left and cap with rough body filler on the right. I decided to paint this one to match the exterior color of the car.
Today was finally the day for installation. Goodbye pitted chrome!
I was relieved that I found an extremely clean mounting surface after removing the original pillar vent/badge.
Here is a look at the mounting nuts and rubber seal from the inside of the car. Relatively easy access.
First up was my favorite, the VicariousGear cap.
I purposely left the red plastic translucent so I can play with illuminating the logo from behind.
The finished Prince center cap with silver gilded logo and red enamel.
I had some carbon fiber lying around so I cut out some disks as another option. I may use these as the day-to-day center cap since they aren't as delicate.


Simin Farkhondeh is an award winning filmmaker, artist, educator and activist. She directs films with a progressive message. Her work has appeared on PBS, Channel Four of England and at screening venues such as the Whitney Museum and the Margaret Mead Film Festival. She was co -director of the award winning anti war Deep Dish TV Series, Gulf Crisis TV Project and director of the acclaimed monthly television series Labor at the Crossroads (LABOR X), about work issues, which aired on cable in New York and other cities across the US. Among her films are WHO GIVES KISSES FREELY FROM HER LIPS, a film about temporary marriage in Iran inspired by Cuban Imperfect Cinema, and Ground One an interactive web project about the impact of 911 on the Chinatown community. Among the shorts she produced and directed are ADJUNCT AGONY, a drama about the plight of adjunct faculty in US universities, SALT PEANUTS a look at the effect of September 11 on airline workers and OTHER, a fiction piece on the plight of Middle Eastern detainees. Simin is a recipient of the Rockefeller Fellowship Award and numerous other awards. She teaches Film and Video Arts at School of Visual Arts, Communications Theory at Fordham University, and is currently working on a documentary entitled Iran in the Orientalist Mind. Third World Newsreel and Arab Films distribute her work.
She received her M.F.A. in Media and Communication Arts from the City University of New York and is a Whitney Independent Study Program for the Arts fellow. She received her B.F.A. in Video/Experimental Arts, from the University of Hartford Art School.
Her personal documentaries are about Iran and her relationship to the country she left as a young person, the upheavals of revolution, the Iran-Iraq war and years of difficult history with the US. Her work has shown widely in museums, art houses, universities and on television. They include Road to Kurdistan, 2011, Women Like Us, 2002; A Place Called Home, 1998; Far From Iran, 1990; Journal from Tehran, 1987. Her work is distributed by Women Make Movies and Arab Films.
Editor Bahman Kiarostami started his work as an assistant director in 1996 and made his first documentary, Morteza Momayez: Father of Iranian Contemporary Graphic Design, in the same year. His subsequent films have focused primarily on art and music, often touching on religious themes.


text:
Anavet Cup 2018-2019 Preview and Playoff Schedule
By Derek Holtom
See schedule below
With 12 wins apiece, the MJHL champion Portage Terriers and the SJHL champion Battleford North Stars will begin the ANAVET Cup this Friday with a berth to the RBC Cup – the national championship for junior A hockey – on the line.
Both teams have been tested, with each winning a dramatic Game 7 during their playoff run to get to this point. Let's take a look at how they got here and what to expect in this best-of-seven clash.
Battleford North Stars (35-13-5-5 in the regular season, 12-2-2 in the playoffs)
The North Stars posted the second-best record in the regular season – finishing a single point behind the Nipawin Hawks – and thus avoided the Survivor Series round still employed in the SJHL.
In the quarter-finals, the North Stars needed seven games to get past the Flin Flon Bombers. Things started well enough the Battleford as they went up 3-0, only to lose the next three games – two of which went to overtime – before prevailing 4-1 in a deciding Game 7 on home ice.
From there, the North Stars learned their lesson and did not let up, sweeping the Yorkton Terriers 4-0 in the semifinals. And in the finals, Battleford won three straight against Melfort after splitting the first two games to capture the SJHL championship. The clinching game wasn't even close – they beat on the Mustangs by a 7-2 score.
Offensively the North Stars have been led by Quintin Loon-Stewardson. The Sioux Lookout, ON, native has 10 goals and 19 points in the playoffs – as well as 36 penalty minutes. He brings skill and grit to this team. Hometown product Braydon Buziak is next in team scoring with eight goals and 19 points, followed by Elijah Loon-Stewardson, who has 12 points in these playoffs.
Battleford's true strength lies in keeping the puck out of the next, as witnessed in netminder Joel Gryzbowski's statistics. The Hafford, SK, native sports a 1.68 GAA and a .950 save percentage.
Portage Terriers (46-11-2-1 in the regular season, 12-3-0 in the playoffs)
After breezing through the first two rounds of the playoffs by sweeping Waywayseecappo and Virden, the Portage Terriers faced the Swan Valley Stampeders in an epic seven-game classic. After falling down 3-2 in the series, head coach Blake Spiller switched netminder, and the play of Nathan Moore helped spark the Terriers to two straight wins and the MJHL title.
In storybook fashion, the Terriers gave up the lead late in Game 7 on home ice, only to see Reece Henry score the golden goal in overtime to lift the Terriers to their fourth league title in the last five years.
Chase Brakel has been a force for the Terriers. The Winnipeg native has 22 points in 15 playoff games – second most in the MJHL playoffs. Scott Mickoski – another Winnipeg product – added 16 points in Portage's championship run, while rookie Jay Buchholz from Fargo, N.D., added 13 points to aid the cause.
The Terriers certainly have goaltending depth. Ethan Peterson has played most of the playoffs for Portage, going 9-1-2 with a 2.32 GAA and a .913 save percentage. And now they have Nathan Moore to turn to as well. He posted a 3-0-0 record in the playoffs with a 3.00 GAA and a .876 save percentage – numbers which are slightly elevated due to him playing in a high-scoring win over Wayway in the first round. Against the Stampeders his GAA was less than 2.00.
The big challenge for both teams will be to reset and get up for a playoff against a team they've never played against. Both teams had emotional runs to their respective league titles, so they'll have to dig deep to be ready for this ANAVET Cup.
Intangibles – The Terriers have home ice advantage due to the arena in North Battleford being previously booked. Games 1 and 2 go in Portage April 26 and 27. Games 3, 4 and 5 if necessary go in North Battleford April 30-May 2. And if Game 6 or 7 are required they will take place in Portage May 5 and 6 … The Terriers will have suspended veteran Braden Billaney available in Game 2. He has been suspended for the entire playoffs due to a late season infraction … If Terriers can win this series they'll make back-to-back appearances at the national championship tournament, as they are hosting the national championship next year.
BATTLEFORDS NORTH STARS vs PORTAGE TERRIERS
April 26 Battlefords North Stars at Portage Terriers 7:30 pm
April 30 Portage Terriers at Battlefords North Stars 7:30 pm
May 1 Portage Terriers at Battlefords North Stars 7:30 pm
May 2 Portage Terriers at Battlefords North Stars (if necess) 7:30 pm
May 5 Battlefords North Stars at Portage Terriers (if necess) 7:30 pm
Previous PostPortage Terriers win MJHL ChampionshipNext PostIBAM Scholarship awarded to Steinbach Pistons' McConnell


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text:
You are here: Home / feature story / Gallery / Legends Series / National Music Coverage / Steely Dan Returns To The Beacon Theatre For A Nine Show Residency
Steely Dan Returns To The Beacon Theatre For A Nine Show Residency Reviewed by Dianna Augustine on October 19, 2018 .
Gallery: Night Two – The Sights and Sounds of Steely…
Steely Dan Returns To The Beacon Theatre For A Nine Show Residency
By Dianna Augustine 0 841
Tags: aja, beacon residency, Beacon Theater, concert photography, Donald Fagen, Editor: Sheila Walsh, Freddie Washington, horn section, horns, Jam Band, jamfunk, Jim Beard, Jon Herington, Keith Carlock, music, music journalism, Photographer: David Zeck, Residency, Steely Dan, steely dan in pa, tour, Writer: David Zeck
Gallery: Night Two – The Sights and Sounds of Steely Dan Aja
Photos by: David Zeck
Night two came and went on a residency at the Beacon Theatre with the yacht-rock band Steely Dan. The die hard fans, known as the "Dan Fans", packed themselves like sardines into the venue, and we excitedly got to photograph the performance. If you've ever heard the name Steely Dan I'm sure you associate it with some sort of funky hippie powered performance. Your grandparents, parents, and friends have all talked about at some point in time. Movies have depicted the experience of seeing them in the flesh with rotating touring band members. You can even find the band as a centerpoint in John Mulany and Nick Kroll's two-man show Oh Hello!
They've been around since 1972, with an extended hiatus from 1981 -1993. In between, several of the band members branched out into some solo acts, and at the end of the day their performances under the name Steely Dan, was a much better fit, solidified by their fan base known.
The one common thread throughout the band's lifespan is founding member Donald Fagen. The 70-year-old New Jerseyan referenced co-founding guitarist, songwriter and producer Walter Becker Walter Becker's death last September from esophageal cancer, late in the set, and fans alike still feel his loss. But he is well remembered in all the licks and strums on stage. The band continues to carry the torch with an 11-piece backup — including a four-member horn section — in his absence. The touring band is comprised largely of musicians that have been with them since their 2000 reunion album Two Against Nature; the album that won a Grammy and leans more toward the jazz-funk spectrum of things.
Steely Dan pulled off a nine concert residency at the Beacon is a feat for any conglomerate. Keeping the engagement, showmanship, and just plain ol' stamina to produce an energetic two hour-ish performance of hits and jams spanning their career and keeping anyones attention.
Stage went pitch black with a blueish purple tint on the curtains behind a rousing thunder of theater claps with the finely dressed concert-goers there for a night of enjoyment, cool music, and funky good times.
The band mostly stayed glued to their areas sans a few songs where Fagen came off of his piano. Strumming, hard hitting, and going over the biggest hits over the span of their career, this set (as many) were geared toward the die-hard fans.
The funk was so evident and took us into not just a SD era but flashed genius of Eurythmics via their 80s funk era – think "Sisters Are Doing It For Themselves" and shake a hard stick, heavy keyboard, and bumping bass with a 3 deep background group of talented female artists with vocal ranges that make angels shy.
We were treated to drum solos, r&b funk, keyboard jams, guitar wah's echoing the halls, and musicianship cultivated over four long hard and fun decades. Steely Dan's concert was void of much sentimentality or elongated banter, packed tight with a string of classic and heart pulling tunes — including— masterfully meshed sounds playdoughed into tight, preserved, and cohesive jazz-rock.
Fagan and his merry men and women are building upon the legacy of a killer name, Steely Dan, and we all look forward to see what 2019 has in store for them. Stay tuned to all things Steely Dan over on their website and we hope to catch them on tour again!
See the full gallery of the night!
Catch the remainder of this tour:
Sat., Oct. 20 New York City, NY Beacon Theatre The Nightfly
Sun., Oct. 21 New York City, NY Beacon Theatre By Popular Demand
Wed., Oct. 24 New York City, NY Beacon Theatre Countdown to Ecstasy
Fri., Oct. 26 New York City, NY Beacon Theatre Gaucho
Sat., Oct. 27 New York City, NY Beacon Theatre Aja
Mon., Oct. 29 New York City, NY Beacon Theatre The Nightfly
Tues., Oct. 30 New York City, NY Beacon Theatre Greatest Hits
https://steelydanofficial.com
https://www.facebook.com/SteelyDan/
Dianna Augustine
The leader of the pack, shutter16.fam, head photographer and brain schemer has been a Charlotte based concert photographer since early 2002. Her passion for photography was cultivated by her grandfather, a very well known and decorated war photographer who put the first camera in her very small hands at the age of three.
Tequila! Beer! Dead people! The holiday season is here in LA.
October 18, 2018 Columns
Midnight by Joy Autumn. a lifetime of emotion compressed into six brilliant songs
Blink-182 & Lil Wayne Bring The Classics To New Jersey
Rockstar Disrupts The Garden State
New Kids on the Block – The Mixtape Tour
National Music Coverage
SHINEDOWN … MORE THAN A ROCK BAND


You're totally ready to burrow into your blanket fort until spring with Netflix on a constant loop—but no one says you have to look frumpy while you hibernate.
The trick? Elevating your lounging look from ancient oversized sweatshirt (you know you have one in your drawer) to polished, flowy sets that still bring all the snuggly vibes. Enter BB Dakota's just-launched line of seriously soft loungewear pieces, designed in premium fabrics that will make you feel like you just stepped out of a cozy-chic Pinterest board.
These seriously soft pieces will make you feel like you just stepped out of a cozy-chic Pinterest board.
Taking time out for yourself is not only necessary, but a real part of your self-care routine—so why not prioritize feeling gorgeous and relaxed at the same time? BB Dakota is made for the modern woman's busy life, with inclusive, fashion-forward pieces that come in sizes 0 to 16.
With luxe kimono sweaters and live-in bralettes, you may or may not want to leave your house ever again—unless you decide to go on a coffee run. Because who says you can't go in public in your PJs?
Check out our editor-curated selection of cozy-cute looks below.
Pretty much nothing makes you feel more put together than a matching set (see: your fave leggings and sports bra combo for spin class). This slightly cropped top comes with chunky braided detailing that takes the duo from cute to Instagrammable.
Cozy in the front, party in the back. This classic striped tee adds ups the ante with a cut-out triangle on the reverse. Pair with the perfect binge-watch, matcha-cookie-eating pants for a Friday-night-in look on lock.
These aren't regular sweatpants, they're cool sweatpants. With snaps up the side (and on the coordinating longsleeve), you have an easy outfit exit—not that you'll ever want to take it off.
Too cute not to Instagram it. Nestle into your furry pillows with a turmeric latte, adjust the lighting scheme on your salt lamp, and post away.
Yes, wearing head-to-toe sweats can look this good. With a ridiculously soft brushed knit fabric, you'll want to rock grey on grey all day every day.
Weeknights and weekends are an official no-underwire zone. In that case, this super soft bralette will become the essential you change into the moment you get home from the office.