All of the following command line tools are accessed from the command prompt. You can open a command prompt window by selecting Start | All Programs | Accessories | Command Prompt.
You can also open the command prompt window by selecting Start | Run - and then entering CMD.EXE into the dialog box and pressing the Enter key or the OK button.
Each tool in this KB is given only a very basic overview and usage description. We would suggest that you research each of these in more detail to learn about advanced usage.
The ping command is a very simple connectivity testing tool. Ping verifies connectivity by sending Internet Control Message Protocol (ICMP) echo packets to a host and listening for an echo reply.
The ping command waits for each packet sent and prints the number of packets transmitted and received. Each received packet is validated against the sent packet. The default setting will send four echo packets containing 64 bytes of data. You can use the ping utility to test both the host name and IP address of the host for DNS resolution. A successful IP ping and failed host name ping could indicate name resolution issues.
In a command prompt window, enter Ping followed by the Fully Qualified Domain Name (FQDN) or IP address of the server you want to test. You may wish to use the –t command line switch to send continuous echo requests to a host.
It is a TCP based protocol that can also be used to test a variety of services for connectivity. You can use it to test for SMTP, SQL or Remote Desktop connectivity. This is a good test to use for service or port blocks resulting from a firewall configuration.
In a command prompt, enter TELNET, followed by the Fully Qualified Domain Name (FQDN) or IP address of the server you want to connect to - and then the port that the service uses.
The response of a successful connection will be different for each service, but a failed connection will always respond with a variation of the following message: "Could not open connection to the host, on port n: Connect failed"
Tracert is the Windows implementation of the traceroute tool that originated on UNIX and Cisco systems. Tracert is a Windows command-line tool that displays the path a packet takes to reach a destination from the machine that it is executed on. It does this by sending Internet Control Message Protocol (ICMP) echo request messages to the destination. It does this by incrementally increasing the Time To Live (TTL) values to find the path taken to the destination address. The path is displayed as a list in the order of which it heard back from each node that it passed through on its way to the destination.
When you run tracert, the top line shows the destination of the trace. It also lets us know that it stops if it reaches a maximum of 30 hops. Next you will see each hop it takes to reach the destination. The number of hops will go in order numerically from 1 to 30 depending on the path to the destination. Following this tracert will normally include at least 4 pieces of information for each hop; the number of the hop, the Round Trip Time (RTT is displayed in milliseconds or ms) it takes to get from the interface of the current hop and then back again to your machine, the IP address of the interface for that hop and the hostname corresponding to the IP address of the hop. The default is to send out 3 packets to each hop. This is done in case a packet is lost and allows you to get an idea of whether or not there is a variance in the time for a specific hop.
A high number on the first external hop from you machine is a good indication of possible Local Area Network (LAN) issues.
An asterisk (*) indicates an echo request that was lost. These can be the result of security implementations of firewalls or Access Control List (ACL's). Additionally, routers may be configured not to respond to this type of traffic. You may see a row of three asterisks with no IP address or hostname. The trace may then continue responding normally again and display the destination results.
In a command prompt window, enter TRACERT followed by the Fully Qualified Domain Name (FQDN) or IP address of the server you want to test. You may wish to use the –d command line switch to prevent Tracert from resolving the name of the nodes from the IP address in the trace route.
Additionally, you can use external tools such as www.traceroute.org or other 'looking glass' type sites to verify traces from different geographic locations throughout the world. You may want to select multiple sites to test connectivity to your server.
PathPing is a utility that combines many of the features of Ping and Tracert into one tool. You can use it to verify connectivity to a host as well as see if you are taking an optimal path to a remote host or suffering from a bottleneck somewhere in the connection route. The final output provides statistics on the latency (packet loss) by sending multiple echo requests over a period of time to each node between the local and remote host.
The "Source to Here" – is the first set of statistic after the hop number is equivalent to if you pinged the node directly.
The "This Node/Link" is the set of statistic before the pipe and is the column you want to pay the most attention to. This will show you the statistics for the links between the nodes.
In the above example, the link between 111.222.111.222 and 222.111.222.111 is dropping 33 percent of the packets. The router at hop 3 is dropping packets addressed to it, but this loss does not affect their ability to forward traffic.
In a command prompt window, enter PathPing followed by the Fully Qualified Domain Name (FQDN) or IP address of the server you want to test. You may wish to use the –n command line switch to prevent PathPing from resolving name from the IP address of the nodes in the connection route.
PathPing offers slightly more accurate output over Tracert because it provides averages based on multiple echo requests. One disadvantage to PathPing is that it can take longer to return results.
All of these Windows utilities are based on ICMP echo request over TCP/IP - otherwise known as ping packets. Many firewalls block ICMP traffic - so you may not get the response although the site is up and responsive. Access rules can cause false negatives with the reporting of from these network tools.


text:
Zero Degrees Aries, JJ Watt Turns Harvey Into A Herocane
| 1:16 AM
| Astrology, Sports
Robert Phoenix
journalist, blogger, interviewer, astrologer & psychic medium
The Moon slides into its ancient arc, gathering the silver light, swelling into Piscean fullness. We're emotional in the wake of Harvey, manufactured, steered, guided or not, the swelling of the gulf, with Irma lurking offshore, is the manifestation of the rising tides in our lives. We're not in Kansas anymore.
In the wake of the eclipse, we've witnessed the cyclonic mutation of Harvey act like a bomb on the Houston area. Houston was incorporated on August 24th, 1836 at 12:38PM (see chart below). With it's Sun in Virgo at 1 degree, the solar eclipse took place just two degrees off it's natal Sun and was in a 4 degree orb natal Uranus in Pisces in the 4th House. It's fascinating to look at a chart like this and examine the cosmological make up of the city, apart from the disaster. Houston is one of the most diverse cities in the country, where Whites/Caucasians do not comprise the majority. A port city, it's been a magnet for people from places as close as Mexico and as far away as Vietnam. After Katrina, it became home to refugees from New Orleans, displaced by their own meteorological assault. It's also a quirky city and we can see this in the opposition of Sun/Uranus. Houston's last two mayors have both been gay and the quirky nature doesn't stop at city hall.
Given it's widespread publicity in the past week, Houston has been the wild west of zoning, and while it's not unincorporated, it's about as close as it gets. That's Uranus again in the 4th House. Oil is obviously king in Houston and the watery grand trine of Uranus/Saturn/Mars. Uranus provides the technology, Mars the industrial muscle and Saturn, the administrative powers. Houston, based on it's chart built for success, and oil and gas course though that grand trine like liquid gold, the Piscean pyramid for not only it but the rest of the country.
This is not a city that will be held down. Sag rising has an indomitable spirit and also underscores the international flavor of the city. Good will follows Sag Rising and it's been pouring into the city, not just monetarily, but also from the people side of the equation.
JJ Watt of the Houston, Texans has his Jupiter in Gemini, right on the DSC of Houston. Watt's Jupiter; 1 degree Gemini. Houston's DSC; 2 Gemini. Jupiter, is expansion, generosity and largesse. To date, Watt has raised over $20 million dollars for the beleaguered city. Watt's TN is in Pisces at 4 degrees, and it's conjunct Houston's Uranus at 2 degrees. Watt has immersed himself in his true purpose through rapid response to the crisis. Born on March 22, 1989 at 0 Aries, he has always epitomized the expression of will through sport on the football field. Up until last year, Watt couldn't be stopped. He was one of those rare players who could singlehandedly take over a game. Part Spartacus, part Paul Bunyan, part Babe Ruth, his larger than life persona had already established itself on the field, but last year, his back gave out during the pre-season and he spent the entire year rehabbing.
Now, even before the season has begun, he's become the main story of the NFL, not Colin Kaepernick or Michael Bennett (both Scorpios) and their ever vigilant pursuit of social change. (Bennett, to his credit has donated $25,000 to the Houston area).
In a time where white men are vilified and scorned as the perpetrators of everything evil, and must be exterminated at all cost, while suppressing their masculinity throughout the process, aka a collective castration, JJ Watt bursts onto the scene to offer a very different picture of what the hard left, ANTIFA, BLM, BAMN and fourth wave feminists are decrying as the plague of the planet, history and time.
As the Pisces Moon beams down upon us, bathing us in it's timeless glow, as the debris is cleared away from the devastation of Harvey, as the Hermit King launches his exploding phalluses, as the conductor of the tower blows hot winds from the west, Luna becomes a pale aureole in the night sky, suckling both nightmares and dreams, from the profane to the sacred, illuminating both our extreme vulnerability and our eternal strength.
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redpajama_set_name: RedPajamaCommonCrawl


Philip Stern& Compan is located at the address 1715 45th St in Brooklyn, New York 11204. They can be contacted via phone at (347) 221-0237 for pricing, hours and directions.
For maps and directions to Philip Stern& Compan view the map to the right. For reviews of Philip Stern& Compan see below.


text:
\section{Introduction}

A WHO report states that drug abuse, rather than illness, accounts for one-third of all deaths \cite{8679954}.
Additionally, roughly $6,000$ -- $8,000$ persons per year pass away due to drug errors, according to Yaniv et al. \cite{8010584}.
Medical errors could seriously damage the treatment effectiveness, cause unfavorable side effects, or even lead to death.
WHO has picked the theme Medication Without Harm for World Patient Safety Day 2022 to highlight the significance of taking medication properly.
Drug abuse can be brought on by various factors, including using pills other than those prescribed. To this end, this study concentrates on the issue of matching the pill names in a prescription to the corresponding pills in an image, thereby detecting missing or mistaken pills. We call this the \emph{pill-prescription matching} problem.
The problem's context can be described as follows.
The user has a prescription image and an image capturing pills that will be taken. We want to match each pill in the pill image with the corresponding name in the prescription.

The pill-prescription matching task is analogous to the well-known text-image matching task. In the text-image matching task, an input consists of an image containing numerous objects and a short paragraph of sentences. The text-image matching task aims to identify the keywords in the sentences and match them with the related objects in the image. The key issue in text-image matching is measuring the visual-semantic similarity between a text and an image. Frome et al. \cite{fromedeep} proposed a feature embedding framework that uses Skip-Gram and Convolutional Neural Network (CNN) to extract cross-modal feature representations. Then, the ranking loss is applied so that the distance between mismatched text-image pairs is greater than that between matched pairs. Kiros et al. \cite{kiros2014unifying} utilized a similar approach that leverages the Long Short-Term Memory (LSTM) \cite{hochreiter1997long} to generate text representations. With the recent success of pre-training and self-supervised learning, text-image matching has profited from the rich visual and linguistic representation of pre-trained models on large-scale datasets for downstream tasks. Radford et al. \cite{radford2021learning} proposed a Contrastive Language-Image Pre-Training (CLIP) model to learn visual concepts under language supervision. It is trained using 400 million (text, image) pairs collected from the Web. Gao et al. \cite{gao2020fashionbert} examined the text-image matching in cross-modal retrieval of the fashion industry. They used the pre-trained Bidirectional Encoder Representations from Transformers (BERT) \cite{devlin2018bert} as the backbone network to learn high-level representations of texts and images.

However, the pill-prescription matching task differs from the general text-image matching task in the following aspects.
First, unlike the common text-image matching task, pill names are typically lengthy phrases (instead of words like in the general text-image matching problem). Notably, the pill's name has almost no semantic meaning.
Additionally, the same pill name might be expressed in a variety of ways (depending on the doctors).
Moreover, many text boxes in the prescription (e.g., quantities, diagnostic, etc.) do not relate to the pill name.
Therefore, the conventional text-image matching approaches are inadequate for the pill-prescription matching issue. To this end, we propose a novel approach for dealing with the pill-prescription matching problem. Our main idea is to leverage a Graph Neural Network (GNN) for capturing the spatial relationship of text boxes in the prescription, thereby highlighting text boxes that contain pill names.
Moreover, we propose a cross-modal matching mechanism that employs a contrastive loss to encourage the distance between the mismatched pill image and pill name pairs while minimizing that of the matched pairs.

In summary, this work makes the following contributions:
\vspace{-3pt}
\begin{enumerate}
\item We propose PIMA, a novel deep learning framework based on GNN and contrastive learning for the pill-prescription matching problem. To the best of our knowledge, we are the first to define and address this challenging task on a real-world dataset. The method is applicable for real-world scenarios in clinical practice to improve medication safety.
\item We conduct comprehensive experiments to demonstrate the effectiveness of the proposed approach on a real-world pill-prescription dataset. The proposed PIMA outperforms baseline methods with significant improvements in performance.
\end{enumerate}

The remainder of the paper is organized as follows.
We briefly introduce GraphSAGE, a GNN used in our proposed model in Section \ref{sec:preliminaries}.
The details of our proposed method are described in Section \ref{sec:proposal}.
We perform experiment to evaluate the proposal in Section \ref{sec:evaluation} and conclude the paper in Section \ref{sec:conclusion}.

\vspace{-5pt}
\section{Preliminaries}
\label{sec:preliminaries}
\begin{figure}[tb]
    \centering
    \includegraphics[width=0.6\textwidth]{agg.pdf}
    \caption{\small{The left side represents a graph, whereas the right side depicts the aggregation process of GraphSAGE (with two convolution layers) to generate the embedding vector for vertex $A$.}\vspace{-10pt}}
    \label{fig:agg_graph}
\end{figure}
This section introduces \textit{Graph SAmple and aggreGatE} (GraphSAGE), which is one of the most well-known Graph Neural Network (GNN) developed by Hamilton et al. \cite{hamilton2017inductive}.
Similar to the convolution operation in Convolutional Neural Network (CNN), in the GraphSAGE, information relating to the local neighborhood of a node is collected and used to compute the node embeddings. For each node, the algorithm iteratively aggregates information from its neighbors.
At each iteration, the neighborhood of the node is initially sampled, and the information from the sampled nodes is aggregated into a single vector.
Specifically, at the $k$-th layer, the aggregated information $h^k_{N(v)}$ at a node $v$, based on the sampled neighborhood $N(v)$, can be expressed as:
\begin{equation}
    h^k_{N(v)} = \text{AGG}_k\left( \left\{ h_u^{k-1}, \forall{u} \in N(v) \right\} \right), \nonumber
\end{equation}
where $h_u^{k-1}$ is the embedding of node $u$ in the previous layer.
The aggregated embeddings of the sampled neighborhood $h^k_{N(v)}$ then is concatenated with the node's embedding at the previous layer $h_v^{k-1}$ to form its embedding at the current layer as follows.
\begin{equation}
    h_v^k = \sigma \left( W^k \cdot \text{CONCAT} \left( h_v^{k-1}, h_{N(v)}^k \right) \right), \nonumber
    \label{eq:node_concat}
\end{equation}
where $W^k$ is a trainable weight matrix and $\sigma$ is a non-linear activation function.
Figure~\ref{fig:agg_graph} illustrates the aggregation process of GraphSAGE.
GraphSAGE offers several aggregation methods, including mean, pooling, or neural networks (e.g. Long Short-Term Memory (LSTM) \cite{hochreiter1997long}).

\vspace{-5pt}
\section{Proposed Method}

\label{sec:proposal}
This section describes our proposed method named PIMA for the \textbf{PI}ll-prescription \textbf{MA}tching problem. We start by providing an overview of our solution in Section~\ref{subsec:overview}. We then describe the Pill Detector and Prescription Recognizer modules in Sections \ref{subsec:pill_image} and \ref{subsec:prescription}, respectively.
Finally, we explain the proposed loss function in Section \ref{subsec:loss}.

\vspace{-5pt}
\subsection{Overview}
\label{subsec:overview}
\begin{figure}[tb]
    \centering
    \includegraphics[width=1.0\textwidth]{model.pdf}
    \caption{\small{Overview of PIMA, which consists of three modules: Pill Detector, Prescription Recognizer, and Pill-Prescription Alignment. The Pill Detector is responsible for extracting visual features from pill images. The Prescription Recognizer is responsible for embedding each text box on the prescription and highlighting those with pill names. Finally, the textual and visual data are fed into Pill-Prescription Alignment to produce a matching result.\vspace{-10pt}

    }}
    \label{fig:proposed_framework}
\end{figure}
Figure \ref{fig:proposed_framework} illustrates the overview of our proposed model, which consists of three modules. The first module, named \textbf{Pill Detector}, uses Convolutional Neural Network (CNN) to create representations of pills. The second module, named  \textbf{Prescription Recognizer}, is responsible for extracting the textual information. Specifically, this module utilizes a transformer model to create embedding of the text boxes. Moreover, we leverage a Graph Neural Network (GNN) to capture spatial information among the text boxes, thereby highlighting boxes representing the pill names. Finally, the textual and visual features are projected into a shared space before being fed into a Pill-Prescription Alignment module. In the \textbf{Pill-Prescription Alignment} module, we employ two loss functions. The first loss, a Binary Cross-entropy loss, is responsible for classifying the text boxes containing the pill name, while the second loss, a Contrastive loss, is responsible for matching the pill names with the associated pill images.

\vspace{-5pt}
\subsection{Pill Detector}
\label{subsec:pill_image}
The Pill Detector consists of two main components: Pill Localization and Feature Extraction.
Firstly, an object detection model is applied to determine the location of every pill.
Assuming that there are $M$ pills cropped from an image, let's denote them as $\left \{ p_1, ..., p_M \right \}$.
We then leverage a CNN to extract visual features of the pills and obtain $M$ feature vectors $\left \{ \textbf{ie}_1, ..., \textbf{ie}_M \right \}$.
These feature vectors are then projected onto the same hyper-plane with their counterpart in the prescription via an Image projection module. Consequently, we come up with the final representation of the pills as $\textbf{IP} = \left [ \textbf{ip}_1, ..., \textbf{ip}_M\right ]$.

\vspace{-5pt}
\subsection{Prescription Recognizer}
\label{subsec:prescription}
The Prescription Recognizer comprises three sub-modules: Text Recognition, Text Embedding, and Pseudo-Classification.
Initially, the text recognition model is utilized to identify and localize each text box in the prescription.
Suppose the prescription contains $N$ text boxes, denoted as $\{s_1, ..., s_N\}$,
then they are put through a Transformer-based text embedding module~\cite{vaswani2017attention} to produce embedding vectors of the text boxes.
On the one hand, these embedding vectors, along with the coordinates of the text boxes, are utilized to construct a graph representing the spatial relationship between the text boxes. This graph is used as the pseudo-classifier's input to highlight the boxes containing the pill names. On the other hand, these embedding vectors capture contextual information of the text boxes, which is used for matching with pill images.

\textbf{\\Transformer-based Text Embedding.}
Given a text box $s_i = [w_1^{(i)}, ..., w_{l_i}^{(i)}]$, where $w_t^{(i)}$ ($t=1, ..., l_i$) represents the token embedding of the $t$-th word of $s_i$, the text embedding of $s_i$, denoted by $\textbf{te}_{i}$, is obtained by feeding $[w_1^{(i)}, ..., w_{l_i}^{(i)}]$ into a Transformer encoder.
These text embeddings are then projected to the same hyper-plane as their counterparts in the pill images with the aid of a text projection module.
Consequently, we get the final representations of the $N$ text boxes as $\textbf{TP} = \left[\textbf{tp}_1, \cdots, \textbf{tp}_N \right]$.

\textbf{\\Graph-based Pseudo Classifier.} We noticed that a prescription contains numerous text boxes without pill names.
Therefore, to improve the pill-prescription matching accuracy, a preprocessing step is required to highlight the boxes that are likely to contain pill names.
For this, we create a binary classifier based on a GNN.
Specifically, we first construct a graph $G$ representing the spatial relationship between the text boxes, i.e., $G=\{V, E\}$, where $V =\{v_1, ..., v_N\}$, with $v_i$ the $i$-th text box. The attribute of $v_i$ is the text embedding $\textbf{te}_i$.
Two vertices, $v_i$ and $v_j$, are connected if either $v_i$ or $v_j$ is the box with the shortest horizontal (or vertical) distance to the other.
Any network can be used for this purpose, and investigating GNN is out of the scope of this paper.
In this work, we utilize one of the most prominent GNN, namely GraphSAGE~\cite{hamilton2017inductive}, to convert from graph space to vector space.
Consequently, for every vertex $v_i$, we obtain a graph embedding vector $h_i$, which represents the relationship between $v_i$ and its $K$-hop neighbors.
Finally, these graph embedding vectors will then be input to a sigmoid layer to produce the classification results.
In particular, the output of the pseudo-classifier is a vector $g=\left \langle g_1, ...g_N \right \rangle$, where $g_i$ represents the likelihood that the $i$-th textbox has the pill name.
This pseudo-classifier will be trained via classification loss (see Section \ref{subsec:loss}).

Finally, the pseudo classification result is multiplied with the text embedding to obtain the weighted version, $\textbf{TP}' = \left [g_1 \times \textbf{tp}_1, ..., g_N \times \textbf{tp}_N  \right ]$. It is worth noting that as $g_i$ quantifies the likelihood that the $i$-th text box includes the pill name, the embedding vectors of the boxes having the pill name will be highlighted in the $\textbf{TP}'$, while the remainder will be grayed out.
\vspace{-5pt}
\subsection{Pill-Prescription Alignment}
\label{subsec:loss}
This module receives visual representations of pill images from the Pill Detector and textual representations of text boxes from the Prescription Recognizer. It matches pill names and pill images to generate the final result.
For this, we design an objective function consisting of two losses: classification loss and matching loss.

\textbf{\\Classification Loss.}
We adopt Binary cross-entropy loss to identify whether or not a text box contains a pill name.
We observe that the number of pill name boxes is significantly smaller than that of boxes without pill names.
For this reason, we employ the following weighted cross-entropy loss to mitigate the bias.

\begin{equation}
    \mathcal{L_\text{Classification}} = -\frac{1}{N} \sum_{i = 1}^{N} w_i\left[ y_i\text{log}(g_i) + (1-y_i) \text{log}(1 - g_i) \right], \nonumber
\end{equation}
where $y_i$ and $g_i$ indicate the ground-truth label and the predicted result concerning a text box $s_i$, respectively; $w_i$ represents the ratio of boxes with the label of $1-y_i$.
To be more specific, let $n_1$ be the number of text boxes containing a pill name, and $N$ be the total number of the text boxes, then we have

\begin{equation}
   w_i = \left\{\begin{matrix}
 1 - \frac{n_1}{N} & \text{,} & \text{ ~if the text box~} s_i \text{~contains a pill name,} \\
 \frac{n_1}{N} &  \text{,}& \text{~otherwise}.
\end{matrix}\right. \nonumber
\end{equation}
\noindent \textbf{Matching Loss.}
This loss aims to model the cross-modal similarity of pill name boxes and pill images' representations.
The principle is to encourage the distance between the representations of mismatched pill names and pill image pairs while minimizing the gap between that of matched pairs.
Specifically, let $\textbf{ip}_i$ and $\textbf{tp}_j$ be the representations of a pill image $p_i$ and a text box $s_j$, respectively, then their similarity is defined by the cosine similarity as follows.

\begin{equation}
S(\textbf{ip}_i, \textbf{tp}_j) = \frac{\textbf{ip}_i \cdot \textbf{tp}_j}{\text{max}(\left\| \textbf{ip}_i \right\|_2 \cdot \left\| \textbf{tp}_j \right\|_2, \epsilon)}, \nonumber
\end{equation}
where $\epsilon$ is a small value which is responsible for avoiding division by zero.
The matching loss is then defined as the sum of dis-similarities over all the matched pairs and similarities over all the mismatched pairs as follows.

\begin{equation*}
    \mathcal{L}_{\text{Matching}} = \frac{1}{M} \sum_{i = 1}^{M} \left [\frac{1}{2}\sum_{j \notin \mathbb{P}_i} S(\textbf{ip}_i, \textbf{tp}_j)^2 + \frac{1}{2}\sum_{k \in \mathbb{P}_i} \text{max}\left (0,m - S\left (\textbf{ip}_i, \textbf{tp}_k \right) \right )^2\right ],
\end{equation*}
where $\mathbb{P}_i$ is the set of all text boxes containing the pill name corresponding to the pill $p_i$, and $m$ is a positive margin specifying the radius surrounding $S\left (\textbf{ip}_i, \textbf{tp}_k \right)$. In our method, we set $m$ to $1$ as the similarity values range from $-1$ to $1$.

The total loss then is defined by the sum of the classification loss and the matching loss as follows.

\begin{equation}
    \mathcal{L}_{\text{Total}} = \mathcal{L}_{\text{Matching}} + \lambda\mathcal{L}_{\text{Classification}},
    \label{eq:loss_function}
\end{equation}
where $\lambda$ is a hyper parameter that balances these two losses.

\vspace{-5pt}
\section{Experiments}
\label{sec:evaluation}
In this section, we conduct comprehensive experiments to evaluate the proposed approach, PIMA. We carefully compare it to the state-of-the-art (SOTA) text-image matching methods under the same experimental settings. Moreover, we perform extensive ablation studies to provide a deeper understanding of some key properties of PIMA.
\vspace{-5pt}
\subsection{Dataset and Experimental Setup}
\label{subsub:dataset}
To the best of our knowledge, there is currently no dataset publicly available for the pill-prescription matching task. This motivates us to build an open large-scale dataset containing pill images and the corresponding prescriptions\footnote{The dataset can be downloaded from our project Web-page at \url{https://vaipe.org/#resource}.}. In particular, we collected $1,527$ prescriptions from anonymous patients in major hospitals between 2021 and 2022. After carefully checking data against the privacy concern, we performed the annotation process in which each pill image was assigned and annotated by a human annotator. We then separated the medication intakes for each prescription into morning, noon, and evening parts. For each pill intake, we took about $5$ pictures of the pills. Consequently, we collected $6,366$ pictures of pills, and the unique number of pills was $107$. Figure~\ref{fig:dataset-2} shows several representative examples from our pill dataset. For algorithm development and evaluation, we divided the prescriptions and the corresponding pills into two subsets for training and testing. Details are described in Table~\ref{tab:train_test_rate}.

\begin{figure}[tb]
  \centering
  \includegraphics[width=1.\linewidth]{dataset.png}
  \caption{\small{Representative examples from the pill image dataset. It was collected in real-world scenarios, where samples are taken in unconstrained environments.}}
  \label{fig:dataset-2}
\end{figure}

\textbf{\\Experimental Scenarios.} We evaluate the performance of the proposed PIMA in two distinct circumstances. In the first scenario, we consider settings where medications are taken exactly as prescribed. We assess the precision with which our algorithm could assist users in matching the pills they have taken with their prescription names. The second scenario refers to circumstances in which the pills consumed do not correspond to the prescription. In such a scenario, we examine the accuracy with which our algorithm can identify pills that have been improperly used. Specifically, the percentage of pills incorrectly taken in the second scenario is set to $50\%$. To better investigate the performance of the proposed approach on different data distributions, we divide the two scenarios into sub-scenarios 1-1, 1-2, 1-3, 2-1, and 2-2, which are described in detail in the following.
\begin{table}[tb]
\begin{tblr}{
  colspec = {p{3.3cm}|X[c]|X[c]|X[c]|X[c]},
}
\hline
\SetCell[r=2]{l}\textbf{Experimental Scenario} & \SetCell[c=2]{c}\textbf{Prescription Images} && \SetCell[c=2]{c}\textbf{Pill Images}\\
\cline{2-5}
 & Train (\%) & Test (\%) & Train (\%) & Test (\%) \\ \hline
Scenario 1-1 & 69.55 & 30.45 & 70.58 & 29.42 \\\hline
Scenario 1-2 & 38.89 & 61.11 & 45.04 & 54.96 \\ \hline
Scenario 1-3 & 3.86 & 96.14 & 6.12 & 93.88 \\ \hline
Scenario 2-1, 2-2 & 69.55 & 30.45 & 72.40 & 27.60 \\ \hline
\end{tblr}
\caption{\small{Details of the data partition.}}
\label{tab:train_test_rate}
\vspace{-10pt}
\end{table}

\textbf{\\\textit{Scenario 1:}}
\begin{itemize}
    \item[$\bullet$] Scenario 1-1. To split the prescription dataset where each prescription has multiple pill names, we use the stratified sampling method proposed by Sechidis et al. \cite{sechidis2011stratification} into two datasets for training and testing.

    \item[$\bullet$] Scenario 1-2. The prescription data is split so that the training set only has prescriptions that do not overlap, and the test set has all the remaining data.

    \item[$\bullet$] Scenario 1-3. The prescription data is split so that a pill name only appears once in the training set, and the test set has all the remaining data.

\end{itemize}
\textbf{\textit{Scenario 2:}} The training dataset is identical to the train set in Scenario 1-1; however, the test set in Scenario 2-1 contains $50\%$ of random pill images that are very similar to the pills in the prescription (in terms of both color and shape); and the test set in Scenario 2-2 contains $50\%$ of random pill images that have the same shape but a different color than the pills in the prescription. Figure~\ref{fig:change_exp_0405} illustrates some examples.

\begin{figure}[tb]
  \begin{subfigure}[b]{0.45\textwidth}
    \includegraphics[width=\textwidth]{exp04_2.pdf}
    \caption{\small{Illustration for Scenario 2-1.}}
    \label{fig:change_exp04}
  \end{subfigure}
  \hfill
  \begin{subfigure}[b]{0.45\textwidth}
    \includegraphics[width=\textwidth]{exp05_2.pdf}
    \caption{\small{Illustration for Scenario 2-2.}}
    \label{fig:change_exp05}
  \end{subfigure}
  \caption{\small{Visualization of several changes in experimental Scenarios 2-1 and 2-2.}\vspace{-10pt}}
  \label{fig:change_exp_0405}
\end{figure}

\textbf{\\Evaluation Metrics.} To evaluate the effectiveness of PIMA, we report the final test accuracy using \textit{F1-score} as the main metric, which is widely used in recognition tasks.

\textbf{\\Training Details.} In our implementation, the projection modules consist of two fully connected layers with the Gaussian Error Linear Units (GELU) activation \cite{hendrycks2016gaussian}. The output dimension is set to $256$.
We use AdamW \cite{loshchilov2017decoupled} as the optimizer and set the initial learning rate to $0.001$. The factor $\lambda$ (Eq.~\ref{eq:loss_function}) is set as $1$ for simplicity.  We train the model with the batch size of $4$ and the input image size of $224 \times 224$ pixels. The random rotation of $10^\mathrm{o}$ and horizontal flip are used as data augmentation techniques. All implementations are performed using the PyTorch framework, and the training process is conducted on a machine with an NVIDIA GeForce RTX 3080 GPU.

\vspace{-5pt}
\subsection{Experimental Results}
\label{sub:exp_baseline}
We provide in this section our experimental results and the comparison between our approach and
baseline methods. Because there is no other end-to-end method like ours, the baselines are performed independently in a two-step process. A deep learning-based pill detector is firstly trained to recognize pills from images. The predicted pills are then compared with the correctly identified pill name in the prescription.
\begin{table}[tb]
\begin{tblr}{
  colspec = {p{3.4cm}|p{1.9cm}|X[c]X[c]|X[c]X[c]},
}
\hline
\SetCell[r=2]{l}{\textbf{CNN Model}} & \SetCell[r=2]{c}\SetCell[c=1]{c}{\textbf{Number of Parameters}} & \SetCell[c=2]{c}{\textbf{Pretrained}} && \SetCell[c=2]{c}{\textbf{Non-Pretrained}}\\
\cline{3-6}
& & Train {\fontsize{8pt}{8pt}\selectfont (\%)} & Test {\fontsize{8pt}{8pt}\selectfont (\%)} & Train {\fontsize{8pt}{8pt}\selectfont (\%)} & Test {\fontsize{8pt}{8pt}\selectfont (\%)} \\ \hline
Resnet-18 \cite{he2016deep} & \SetCell[c=1]{r}11.7M & 99.87 & 86.06 & 67.39 & 67.27 \\
Resnet-34 \cite{he2016deep} & \SetCell[c=1]{r}21.8M & 96.28 & 82.27 & 68.01 & 66.68 \\
Resnet-50 \cite{he2016deep} & \SetCell[c=1]{r}25.6M & 86.08 & 77.04 & 68.39 & 67.27 \\
ViT-Small/16 \cite{dosovitskiy2020image} & \SetCell[c=1]{r}22.1M & 99.44 & 66.47 & 61.07 & 56.59 \\
MobileNet-V2 \cite{sandler2018mobilenetv2} & \SetCell[c=1]{r}3.5M & 99.39 & 89.48 & 75.96 & 70.63 \\
MobileNet-V3-small \cite{DBLP:journals/corr/abs-1905-02244} & \SetCell[c=1]{r}2.5M & 99.98 & \textbf{90.18} & 74.78 & \textbf{71.65} \\
MobileNet-V3-large \cite{DBLP:journals/corr/abs-1905-02244} & \SetCell[c=1]{r}5.5M & 99.93 & 90.01 & 70.31 & 70.30 \\ \hline
\end{tblr}
\caption{\small{Experimental results on the test set of our pill image dataset with different CNN models. The best results are highlighted in \textbf{bold}.\label{tab:experiment_cnn}}}
\end{table}
\vspace{-5pt}
\noindent \textbf{\\ \\Baseline Performance.} In this experiment, we train a set of SOTA Convolutional Neural Network (CNN) architectures (see Table~\ref{tab:experiment_cnn}) on our pill image dataset, in which the information from the prescription is not taken into account. Our experiments follow the Scenario 1-1 setting. During the training process, each learning model is initialized with pre-trained weights on the ImageNet dataset~\cite{deng2009imagenet}. We also investigate the learning performance of CNN models on the pill image dataset when training from scratch. We obtain the highest performance with \texttt{Mobilenet-V3-small} model. It reports an F1-score of $90.18\%$ when using ImageNet-trained deep features and an F1-score of $71.65\%$ when trained from scratch, respectively.
\begin{table}[tb]
\begin{tblr}{
  colspec = {p{5.2cm}|X[c]|X[c]X[c]},
}
\hline
\SetCell[r=2]{l}{\textbf{BERT Model}} & \SetCell[r=2]{c}{\textbf{Task}} &  \SetCell[c=2]{c}{\textbf{CNN Non-Pretrained}}\\
\cline{3-6}
& & Train {\fontsize{8pt}{8pt}\selectfont (\%)} & Test {\fontsize{8pt}{8pt}\selectfont (\%)} \\ \hline
BERT-base-uncased \cite{devlin2018bert} &  \SetCell[r=2]{c}{Fill-Mask}  & 97.57 & 76.16  \\
BERT-base-uncased-multilingual \cite{devlin2018bert} &  & 97.78 & 75.95 \\ \hline
MiniLM-L12-v2 \cite{reimers-2019-sentence-bert} & \SetCell[r=2]{c}{Sentence Similarity} &  98.14 & 76.52 \\
MiniLM-L12-v2-multilingual \cite{reimers-2019-sentence-bert} &  &  97.76 & \textbf{79.79} \\ \hline
\end{tblr}
\caption{\small{Experimental results on our pill dataset for the text-image matching task using CLIP model with different BERT pretrained models. Here, CNN model is \texttt{MobileNet-V3-small}. Best results are highlighted in \textbf{bold}.}}
\vspace{-15pt}
\label{tab:clip_experiment}
\end{table}
\textbf{\\ \\CLIP Model.} We use the SOTA model in text-image matching called Contrastive Language–Image Pre-training (CLIP)~\cite{radford2021learning} as the second baseline model in this research. It has been proposed to learn visual concepts with language supervision. We train CLIP model on the pill image dataset with different pre-trained language models (e.g., BERT \cite{devlin2018bert}), while the vision model is the \texttt{MobileNet-V3-small}, which has the best results on baseline classification. The results for Scenario 1-1 are shown in Table~\ref{tab:clip_experiment}. The results indicate that the pre-trained language models in different tasks (e.g., Fill-Mask, Sentence Similarity) are almost similar. The best test set result for the CNN model trained from scratch is $79.79\%$.

Based on the experimental results reported in Tables \ref{tab:experiment_cnn} and \ref{tab:clip_experiment}, we chose to use the trained from scratch CNN model \texttt{MobileNet-V3-small} and the pre-trained language model \texttt{MiniLM-L12-v2-multilingual} for our following experiments and comparisons.

\subsubsection{Comparison with Baseline Approaches}
\textbf{\\ \\Scenario 1.} Table~\ref{tab:exp_scenario_1} summarizes the results of the proposal PIMA and the baseline models concerning Scenarios 1-1, 1-2, and 1-3. For all scenarios, PIMA achieves the highest F1-score of $98.88\%$, $98.41\%$, and $89.77\%$, respectively. Even without using a GNN, PIMA w/o Graph still outperforms the others. Specifically, it outperforms the baseline classification by $23.94\%$, $29.10\%$, and $34.01\%$, respectively.
In comparison with CLIP, PIMA improves the F1-scores by $19.09\%, 25.78\%$ and $41,60\%$ concerning Scenarios 1-1, 1-2, and 1-3, respectively.
We observe that the proposed model achieves a high level of performance even when the number of pill images and prescription samples in Scenarios 1-2 and 1-3 are very limited.
\begin{table}[tb]
\begin{tblr}{
  colspec = {p{3.5cm}|X[c]|X[c]|X[c]},
}
\hline
\SetCell[r=2]{l}{\textbf{Model}} & \SetCell[c=3]{c}{\textbf{F1-Score (\%)}} \\ \cline{2-4}
& \textbf{Scenario 1-1} & \textbf{Scenario 1-2} &  \textbf{Scenario 1-3} \\
\hline
Baseline & 71.65 & 66.07 & 42.82 \\
CLIP & 79.79 & 72.63 & 48.17 \\
PIMA w/o Graph & 95.59 & 95.17 & 76.83 \\
PIMA (our proposal) & \textbf{98.88} & \textbf{98.41} & \textbf{89.77} \\\hline
\end{tblr}
\caption{\small{Experiment results concerning Scenario 1 on our pill image dataset. PIMA significantly outperforms other SOTA methods. Best results are highlighted in \textbf{bold}.}}
\label{tab:exp_scenario_1}
\end{table}

\begin{table}[tb]
\begin{tblr}{
  colspec = {p{3cm}|X[c]X[c]X[c]|X[c]X[c]X[c]},
}
\hline
\SetCell[r=2]{l}{\textbf{Model}} & \SetCell[c=3]{c}{\textbf{Scenario 2-1}} &&& \SetCell[c=3]{c}{\textbf{Scenario 2-2}}\\
\cline{2-7}
& \SetCell[c=1]{c}{F1(Cor) {\fontsize{8pt}{8pt}\selectfont (\%)}} & \SetCell[c=1]{c}{F1(Mis) {\fontsize{8pt}{8pt}\selectfont (\%)}} &
\SetCell[c=1]{c}{F1(Avg) {\fontsize{8pt}{8pt}\selectfont (\%)}} &
\SetCell[c=1]{c}{F1(Cor) {\fontsize{8pt}{8pt}\selectfont (\%)}} &
\SetCell[c=1]{c}{F1(Mis) {\fontsize{8pt}{8pt}\selectfont (\%)}} &
\SetCell[c=1]{c}{F1(Avg) {\fontsize{8pt}{8pt}\selectfont (\%)}} \\ \hline
CLIP & 77.47 & 31.42 & 54.46 & 82.81 & 54.84 & 68.83 \\
PIMA w/o Graph & 96.11 & 30.06 & 63.09 & 97.24 & 55.45 & 76.35 \\
PIMA (our proposal) & \textbf{96.85} & \textbf{62.10} & \textbf{79.48} & \textbf{97.99} & \textbf{94.35} & \textbf{96.17} \\ \hline
\end{tblr}
\caption{\small{Matching accuracy concerning Scenario 2.}}
\label{tab:exp_02}
\end{table}
\textbf{\\Scenario 2.}
In this experiment, we set the threshold to consider an image and text pair matching correctly as $\alpha$ (with $\alpha=0.8$ for all experimental scenarios).
To ease the presentation, we denote F1(Cor) as the F1-score for matching pills that correctly taken with theirs corresponding names in the prescription.
Besides, we use F1(Mis) to indicate the F1-score for detecting pills that are not in the prescription, and F1(Avg) to represent the mean of F1(Cor) and F1(Mis).
The results of Scenarios 2-1 and 2-2 are shown in Table~\ref{tab:exp_02}.
As shown, PIMA achieves the highest average accuracy in both scenarios, $79.48\%$ and $96.17\%$, respectively.
Specifically, PIMA's F1(Cor) is higher than that of CLIP by $19.38\%$ and $15.18\%$, respectively.
In addition, PIMA improves the accuracy of detecting the use of wrong pills by $30.68\%$ in Scenario 2-1 and $39.51\%$ in Scenario 2-2 compared to PIMA.
In comparison between F1(Cor) in the two scenarios, it can be seen that F1(Cor) in Scenario 2-1 is lower than that in Scenario 2-2 because the mistaken pills in Scenario 2-1 is much more similar to the prescribed pills.
Finally, it can be observed that PIMA w/o Graph achieves a higher F1(Cor) than CLIP. However, the F1(Mis) of PIMA w/o Graph is similar to that of CLIP's.

\vspace{-5pt}
\subsection{Discussion}
According to the experimental results, PIMA outperforms all other approaches in terms of both accuracy and convergence speed.
As stated in the previous section, PIMA enhanced the F1 score in terms of accuracy from $19.05\%$ to $46.95\%$ compared to comparison benchmarks.
These results are the consequence of contrastive learning's impacts.
In fact, the baseline model separates pill and text box recognition into two distinct phases. The error in pill-prescription matching is then accumulated by the sum of the errors generated by the two phases.
For the CLIP model, the matching loss only considers matched pairs. Specifically, it only intends to minimize the distance between pill images and pill names. This technique creates a paradox in the circumstance of pills with extremely similar appearances but significantly different names.
Specifically, in such a case, the two similar visual representations are pulled back to nearly two completely different textual representations.
In contrast to baseline and CLIP, PIMA employs contrastive loss, which takes into account both matched and mismatched pairs of pill names and images; additionally, we use weights while computing contrastive loss to balance the contribution of matched and mismatched pairs. This weighted contrastive loss increases the model's generalizability and avoids it from being skewed toward the mismatched cases. Thus, PIMA achieves higher precision.

\begin{figure}[tb]
  \begin{subfigure}[b]{0.45\textwidth}
    \includegraphics[width=\textwidth]{pp05_correct.pdf}
    \caption{\small{Accuracy for matching correctly taken pills}}
    \label{fig:f1}
  \end{subfigure}
  \hfill
  \begin{subfigure}[b]{0.45\textwidth}
    \includegraphics[width=\textwidth]{pp05_error.pdf}
    \caption{\small{Accuracy for detecting mistaken pills}}
    \label{fig:f2}
  \end{subfigure}
  \caption{\small{Convergence speed on Scenario 2-2.  \label{fig:traing_loss}}\vspace{-10pt}}
\end{figure}

Regarding convergence speed, Fig.~ \ref{fig:f1} demonstrates that after only $50$ epochs, PIMA has converged to an accuracy of about $97\%$ for matching correctly taken pills. In contrast, CLIP only achieves an accuracy of approximately $80\%$.
Although PIMA w/o Graph achieves significantly better performance than CLIP, its accuracy after $10$ epochs is only about $88\%$, whereas that of PIMA is about $95\%$.
Fig.~ \ref{fig:f2} depicts the convergence rate for the detection of mistaken pills.
It is apparent that PIMA outperforms the other two significantly.
In particular, after $50$ epochs, PIMA achieves an accuracy of approximately $90\%$, whereas CLIP and PIMA w/o Graph only reach $50\%$.
This can be explained by the GNN-based pseudo classifier's contribution.
By having this classifier highlight text boxes containing medicine names, we have narrowed the search space for the matching issue, allowing PIMA to converge significantly more quickly than comparison benchmarks.

\vspace{-5pt}
\section{Conclusion}
\label{sec:conclusion}
We presented PIMA, a novel method to solve the pill-prescription matching task. The key idea behind the PIMA learning framework is the use of a Graph Neural Network (GNN) architecture and contrastive learning to jointly learn text and image representations in order to enhance pill-prescription matching performance. Our extensive experiments on a real-world pill dataset, including pill and prescription images, show that the proposed approach significantly outperforms baseline approaches, enhancing the matching F1-score from $19.05\%$ to $46.95\%$. Additionally, we also demonstrated that the proposed PIMA is able to achieve a high level of performance while requiring less training costs compared to other benchmarks. We release our code at \url{https://github.com/AIoT-Lab-BKAI/PIMA}.

\section*{Acknowledgement}
This work was funded by Vingroup Joint Stock Company (Vingroup JSC), Vingroup, and supported by Vingroup Innovation Foundation (VINIF) under project code VINIF.2021.DA00128.

\bibliographystyle{splncs04}

redpajama_set_name: RedPajamaArXiv


BubbleUp hit the red carpet at the 2017 ACM Awards in Las Vegas! In addition to developing and hosting the ACM family of websites, BubbleUp staffers were on the ground to assist with keeping all of the ACM sites updated with the latest news and photos from the festivities, as well as posting winners to the web in real time as they were announced. Check out all of this year's winners (many who are also BubbleUp clients) at ACM Awards.


Cartograms provide a pleasing way to present geographical data. The idea is to redraw a map so that the relative sizes of its units (e.g. countries, states, counties, etc.) correspond to some measured quantity. A common example is a cartogram of population by country, in which the relative sizes of China and India become exaggerated, while large countries such as Russia and Canada shrink due to their small populations.
Various repositories of cartograms can be found online; a good place to start is worldmapper. Details about the nuts and bolts follows at the end of this post.
Instead of human population, let's consider global bird species distribution by country. The raw data needed to construct this image was pulled from eBird. The figure below shows the result, where several regions, such as the UK and Belgium, were broken into component states. While the size of the country represents the number of species reported, the color scheme is based on the number of eBird lists submitted, with lighter colors corresponding to fewer lists.
Cartogram of eBird species counts, by country. Darker colors correspond to higher numbers of lists submitted. Data is from early December, 2017.
The results are not surprising, but the map is striking nonetheless: Russia and Canada are barely perceptible, their landmasses reduced by a relative paucity of species; Central America bulges out and dwarfs the continental US; Australia shrinks in relation to the Indonesian island region; Northern Africa becomes compressed while the sub-Saharan countries bulk up; and northern South America shows its true dominance: little Ecuador is now practically as large as Brazil.
The color scheme illustrates how the majority of our worldwide birding efforts remain mostly in regions with less species diversity. It can also be viewed as an indicator of the infrastructure for birding that a given nation has, in some cases. Ghana, for example, has an adjusted size comparable to other West African countries, but its new size and color together show that it has developed into the most attractive destination in the region for global birders.
Constructing a cartogram requires an algorithm that will morph an existing map in such a way that the relative areas reflect the data. But a good cartogram must also produce an image where borders remain intact, and the component regions must retain enough of their original shape so that they are recognizable.
The process of making cartograms is not too difficult, but it isn't simple either. There does not exist a single program that allows one to merely enter data, push a button, and have an image. Instead, several steps using different packages and data sources are required. The work here started with a query of eBird by-country data as of early December 2017, and the initial maps were downloaded from the Natural Earth site. The QGIS package was used to join the eBird data in with the map shapefile. The plots for global data were produced using ScapeToad to perform the morphing, using the highest quality (longest-running) setting. Finally QGIS was used to edit the ScapeToad output and produce the shading in the final cartogram.
Additional novel cartograms related to birding are in the works.


The remnants of the old government have reorganized to form the vicious New United Nations Earth. The new government seeks complete control of the planet, in order to reignite the war against the Space Revolutionary Army. One side sees Newtypes as the future of humanity, while the other sees them as weapons threatening its very existence – and with the Frost Brothers manipulating the situation, the fighting is quickly spiraling out of control. Hoping to stop the war before it starts, Garrod, Tiffa, and the crew of the Freeden continue their search for Newtypes. If they bring enough Newtypes together, will they find a path to peace?


Injuries occur everyday in automobile related accidents. Don't let it interfere with your lifestyle!
On the job injuries occur everyday! Chiropractic care is a healthy alternative to get relief when things don't go as we plan.


A woman bearing gifts might not be the present-carrying-female that she seems. Be wary if she (if it is a she) tries making you any toast. You will be troubled by two legs on Tuesday and four legs on Friday. On a good day, a Barnacle and a Clam can be as an effective a double act as Sigmund Freud and Tiger Woods.
Running away from things will not help problems this week. Neither will staying where you are. The question everyone will be asking this week is 'are you Bob Newhart in disguise?'. No, I don't know what this means either. Watch out for a Barnacle in a hurry on Wednesday. If you time it correctly, you will only end up with a small stain to show for their clumsiness. If you get your timing wrong however, you might be facing a stay in the hospital and you won't be eating solids for a long time.
If you wear too much make-up on Thursday, you could be in for trouble when someone close to you mistakes you for someone even closer to them. You may say to others that you like cats, but this will be the week where you will be tested on how much you love cats. Particularly when a certain cat could unlock the secret to the whereabouts of a long-lost family member. 'Slow-but-steady' may be the motto of your so-called Snail 'friend'. But what if they are speeding around with you partner behind your back? Don't be heartbroken, just think of how much money they have and then think about that good old word 'blackmail'.
This is a good week to set sail on a new voyage of discovery and adventure...unless you are feeling tired, in which case you should stay at home. If there was ever one week in your life where you should eat cheese, this week is it. There is an old saying that goes something like this: 'A Clam, a Clam, a Clam! All I need is a Clam...but a Barnacle might be ok as well'. Heed this warning on Monday.
As the old saying goes 'You can hide a shrimp under a shell, but it's still a shrimp, just a shrimp under a shell'. Heed these words this week, especially if you have any run-ins with the police. Sunday afternoon (about 3:15) is the time for making a big decision about your life. Get together for a Scallop this week if you want to have a fun time that involves an activity that is not yet illegal in all countries.
You may be feeling down. You may be feeling that nothing good ever happens to you. But don't worry. Just remember, that 99.9% of the rest of the population are much happier than you. So at least it all balances out! Your friends will gather closely around you this week, so please take steps to lessen your foul odor. You need to talk to people to tell them how you really feel about things. They desperately want to know how you feel. Well, maybe not desperately. Actually, they don't really want to know how you feel...or even if you are still drawing breath.
Food may be important for you this week, so it might be preferable if you try eating it. Something involving the color green will be on your mind this week and you are not sure if you need a second opinion about what to do. The solution involves getting a second opinion from a friend as to whether you need to get a second opinion. On Sunday, your day will be swiftly ruined by an odious Slug that you know. You can't prevent what they are going to do, the only thing you can do is feel a small degree of satisfaction when you sue them for every penny they've got.
Why do you do what you think you should do when you don't do what you don't think that you should do? If you are driving and you see someone who looks like Chevy Chase driving a blue car, then it is time to leave town immediately otherwise you will be associated with a very bad smell for many months. A stupid Clam friend that you know will prove very bothersome on Friday. Just ignore them. Unless they start removing clothes in which case you should just run away.
You've always wanted to try drinking a pint of raw eggs...now is the time to try. Up, up, up, up, up, up! That's the direction your life will be heading in this week (terms and conditions may apply). Tell a loved one that you are going to take up base jumping. You're not going to do this of course, but it's good to keep people on their toes.
The ship of your dreams is sailing down the river of despair. It's time to take hold of the tiller of fate, and steer yourself to the calm waters of your future. Wear a smile on this week because you cannot fail* and everything you do will turn out to be magical and rewarding (* = terms and conditions may apply). When you say meet me at 3:47 pm, you of course mean 'meet me at 3:47 pm'. So when a stupid Slug that you know turns up at 3:49 pm, you are entitled to walk away and never talk to them again.


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The Peoples Democratic Party has charged President Muhammadu Buhari to immediately sack, order the arrest and trail of the Minister of Finance, Mrs. Kemi Adeosun, for alleged forgery of her NYSC certificate.
The PDP urged Buhari not to provide cover for Adeosun as has become the character of the President whenever close allies or agents were found wanting on issues of corruption.
A statement by the National Publicity Secretary of the party, Mr. Kola Ologbondiyan, in Abuja on Saturday, alleged that the revelation had vindicated the party's position on the Buhari's cabinet members.
He said, "The world can now see how the Buhari Presidency has been concealing sharp practices under it. We can now see why there is humongous corruption under President Buhari's watch and why Mr. President has refused to take any concrete steps to check the sleazes in his administrations.
The spokesman for the former ruling party said the President should also allow an open inquest into his handling, with Adeosun, of all the nation's depleted foreign financial instruments, including the Excess Crude Account, from which funds he said were allegedly taken without recourse to the National Assembly.
He also said Buhari should allow an inquest into his administration's borrowing of over N10tn in 30 months, with Adeosun as minister of finance and the purposes for which the money was used.


text:
Episode 109: Tasha Reiko Brown
Jun 17th
Tasha Reiko Brown is a Los Angeles based celebrity make-up artist who has worked with the likes of Alicia Keys, Yara Shahidi, Tracee Ellis Ross, Logan Laurice Browning and Michael B Jordan. You can find her work in The New York Times, Glamour, GQ, and The Hollywood Reporter.
In episode 109, Tasha explains the difference between a make-up artist and a technician. The two discuss why there's room for more voices in the industry and they discover how she uses creativity and art as a form of self-care and expression.
Creatives, listen up!
Follow Tasha on Instagram
Episode 110: Season Finale – Jameela Jamil
Episode 108: Seyi Akiwowo
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© Copyright Content is Queen 2022 Website by Jake Adams


Updated: 19th April, 2019 1:03 PM.
Want to know an easy way to bring a smile to a child's face? Have Santa send them a personalized letter!
It was the first time to buy a house for me so I was really confused on everything but she explained to me procedures and how to buy a house step by step and went over all the paper work.
Although we were very picky. She helped us find the perfect house in no time.
She was honest in her opinions and very knowledgeable about the areas where we looked.
With her knowledge of the market and an eye for what her clients want, finding the perfect place was easy. Couldn't have asked for a better realtor and friend!
She always gave me the proper advise to net the most money. The number one quality you look for in a great sales agent.
Ana Duarte helped me purchase my first home. She was very helpful and very knowledgeable. She answered all of my questions and got answers when she was unsure!


Many job seekers have found a career as a dental hygienist to be very pleasant and accommodating. To learn more about this stable, well paying career choice, visit the Recruiter section of our site.
We'll keep you up to date when we find more. Have a great weekend!


text:
Forever Rebel Blog
The Dark Side – Backstreet
Forever Rebel – A Nick Channel
AJ McLean
Brian Littrell
Howie Dorough
Kevin Richardson
A-Z of BSB Songs
Album News
Cruise Happenings
Solo Work Series
Album Reflections
Song Spotlights
Song Spotlight Solos
What We Learned Series
Thoughts Watching In…
Thoughts While Bored In 2020
Things Fans Forget
BSB Changed My Life
#BSB25 Throwbacks
The TDS/Forever Rebel Shop
Cause It's Always Darkest Before The Dawn…
The A-Z of BSB Songs
So you guys can complete your collections, I thought this was a page worth creating. Now, these are songs we have mp3s or mp4s of – some are clips, some are the full songs, some are live performances. I'm putting this disclaimer so that way you won't ask why I'm not adding the unreleased songs we wish the Boys would release. I already have a list of those. This is for those fans who aren't crazy like me, who don't have every song ever. I've been discovering recently you can be a die-hard fan and not know this list. So I want to help you guys out. Most, if not all, of these should be in the media archive in some way (unless they're on actual albums that you should buy, in which case go get them on Amazon or iTunes!), shape or form. But if they're not, you should be able to find them on YouTube. Happy hunting!
Disclaimer – I'm not listing the remixes or I'd be here for years, lol. Sorry!
Studio Quality Songs
19 In 99 (Nick)
100 (Howie)
10,000 Promises
Addicted (Nick)
All American (Nick)
All I Have To Give
All I Have To Give (Conversation mix)
All In My Head (NKOTBSB)
All In This Together
All Of Your Life (You Need Love)
Angels and Heroes (Brian)
Answer To Our Life
Any Other Way
Any Other Way (Nick version clip)
Anywhere For You
Arthur Theme Song
As Long As You Love Me
Baby I'm Yours
Baby it's cold outside (Brian featuring Leighanne)
Back Porch Bottle Service (AJ)
Back To Me (Howie)
Back To Your Heart
Back In The Day (Howie)
Back In The Day Reprise (Howie)
Beautiful Lie (Nick and Jennifer Paige)
Best of My Love
Best That I Can
Betcha By Golly Wow
Bigger
Blow Your Mind (Nick)
Boy and a Man (AJ)
Breaking Up Is Hard To Do (Nick Before BSB)
Breathe (on In A World Like This)
Breathe (on DNA)
Burning Up (Nick)
By His Wounds (Brian)
By My Side
Bye Bye Love
California (Nick)
Can We Get Back To Love Again
Can't Stop Thinkin' About You
Cherry Pie (Nick)
Children Need A Helping Hand
Children Of The World
Climbing The Walls
Close My Eyes
Clouds (Redrama featuring AJ)
Coffee and Cigarettes (AJ)
Coma (Nick)
Come Kiss Me (Brian and Baylee duet)
Crawling Back To You
Danny's Song (Kevin)
Darlin'
Do I Have to Cry For You (Nick)
Dominoes (Howie)
Don't Disturb This Groove
Don't Go Breaking My Heart
Don't Leave Me
Don't Turn Out The Lights (NKOTBSB)
Don't Wanna Lose You Now
Don't Want You Back
Donde Quieras Yo Ire
Drive By Love (AJ)
Drop The Ball (AJ)
Drowning (Country version)
End Of Forever (Nick)
Every Time I Close My Eyes
Everybody (Backstreet's Back)
Everything But Mine
Evidence of You (Howie)
Falling Down (Nick)
Falling in Love Again (Nick)
Figured You Out
Fly To Heaven (Howie)
Forever Rebel (Nick)
Free From Within (Howie)
Friends Never Say Goodbye
Get Another Boyfriend
Get Down (You're The One For Me)
Get Over Me (Nick)
Going Going Gone (Howie)
Gorgeous (AJ)
Girls of The USA (Nick)
Give Me Your Heart
Give You Away (AJ)
God Bless The U.S.A. (Nick Before BSB)
God, Your Mama, and Me (with FGL)
Gone Without Goodbye (Brian)
Grace of My Life (Brian)
Hard To Get (Nick solo Before BSB)
Hard To Get (Nick & Malia Tuaileva Before BSB)
Hate It When You're Gone (AJ)
Have It All (AJ)
Heart Without a Home (Nick)
Help Me (Nick)
Helpless Feat Pitbull (two versions)
Helpless When She Smiles
Hey, Mr DJ
Higher Love
Horoscope (Nick)
How Great (Brian)
How Did I Fall In Love With You?
I Got You (Nick)
I Got To Get It
I Gotta Get With You (Nick)
I Just Wanna Take You Home (Nick)
I Need You Tonight
I Promise You (With Everything I Am)
I Quit (AJ)
I Still
I Surrender All (Brian)
I Want It That Way
I Want It That Way (No Goodbyes)
I Want It That Way Reimagined
I Will Be Yours
I Will Wait (Nick)
I'd Do Anything
I'd Do Anything For You (Trey D & Kevin)
I'll Be Home For Christmas (Brian)
I'll Be There For You
I'll Never Break Your Heart
I'll Never Find a Girl Like You
I'll Never Find Someone Like You
I'm Alive (Brian)
I'm Taking Off (Nick)
In a World Like This
I've Got To Get It
If I Don't Have You
If I Ever Fall In Love
If I Knew Then
If I Say (Howie & Yu Shirota)
If You Knew What I Knew
If You Stay
If You Want It To Be Good Girl (Get Yourself A Bad Boy)
In Christ Alone (Brian)
In Pieces
In The End (Nick/Howie/AJ)
In Your Arms
In Over My Head (Nick)
Inconsolable
International Luv
Is It Saturday Yet? (Nick)
It Still Matters (Gospellers & Howie)
It's A Game
It's Beginning to look a lot like Christmas (Brian with Leighanne)
It's Gotta Be You
Jailhouse Rock (Nick Before BSB)
Jesus Loves You (Brian)
Jewel In Our Hearts (Nick)
Just A Gigolo /I Ain't Got Nobody (Nick Before BSB)
Just Like You Like It
Just One Kiss (Nick)
Just To Be Close
Just Want You To Know
Lager Than Life
Last Night You Saved My Life
Lay Down Beside Me
Lean On Me (Howie)
Left For Dead (Nick)
Let Me Be Your Loverboy
Let The Music Heal Your Soul
Let's Do It For Love
Let's Have A Party
Let's Have A Party (Acoustic – A Night Out With The BSB)
Let It Be Me (With Steve Aoki)
Let It Go (Nick)
Let's Make a Toast to Our Love
Lie To Me (Howie)
Life is Just (AJ)
Light On
Lights (Nick Before BSB)
Live Together (AJ)
London (AJ)
Love Can't Wait (Nick)
Love Crazy (AJ)
Love Is A Wonderful Thing (Nick Before BSB)
Love Knows I Love You
Love Somebody
Love Will Keep You Up All Night
Man On The Moon (Nick)
Mary Did You Know (Brian)
Mash Up (NKOTBSB)
Matches (with Britney Spears)
Miss America (Nick)
Monsters In My Head (Howie)
More Than That
More Today Than Yesterday (Nick Before BSB)
Movin' On
Mr. A (AJ)
Mr. A (BSB version)
My Answer is You (Brian)
My Beautiful Woman
My Confession (Nick)
My Heart Stays With You (Howie)
Night Visions (AJ)
No Hablo Espanol (Howie)
No More Games (Nick and Andrew Alvarado)
No One Else Comes Close
Nobody But You
No Place
Non Pour Lusciarmi Cosi
Not No More
Not The Other Guy (Nick)
Not too Young, Not too Old (Aaron + Nick)
Nothing (Nick and MBA)
Nothing Left To Lose (Nick)
Nothing's Gonna Change My Love For You (Nick)
Nunca Te Hare Llorar
Oh Aaron (Aaron + Nick)
On Without You
One Last Cry (Brian)
One Phone Call
One Small Voice (with Elmo)
Outlooks at Beals
Over Her
Over My Head (Howie)
Over My Head (Brian)
Over and Under (Howie)
Payback (Nick)
Permanent Stain
Pollyanna's Shadow (Howie)
Poster Girl
Prisoner (Nick)
Pure (Howie)
Quit Playin' Games (With My Heart)
Remember (Nick)
Rhythm Of My Heart (Nick Before BSB)
Right Here Waiting For You (Nick, Live)
Rockin' Around The Christmas Tree (Nick Before BSB)
Rockstar Baby (Nick)
Rub A Dub Dub
Runaround Sue (Nick Before BSB)
Rush Over Me
Safest Place To Hide
Say Goodbye (Nick & Tommy Lee)
Scandilicious (Nick)
School Bell Tango (Howie)
Scream (Nick)
Second Win (Nick)
Set Adrift On Memory Bliss
Set It Off
Shatterproof (Howie)
She Wants Me (Aaron & Nick)
She's A Dream
She's Got A Way (Kevin)
She's Like The Sun (Howie)
Show 'Em What You're Made Of
Show Me What You Got (Bratz Feat Boa & Howie)
Show Me The Meaning (Of Being Lonely)
Shut Up and Listen (AJ)
Shy (Howie)
Sick As My Secrets
Sincerely Yours (AJ)
Sleepwalking (Howie)
Small Time Blues (Howie)
So Far Away (Nick)
Something That I Already Know
Song For The Unloved
Special (Nick)
Star Spangled Banner (Nick Before BSB)
Stay (Howie)
Stay In My Life
Straight Through My Heart
Stay the Same (AJ)
Story of My Life
Summer! (Nick Before BSB)
Sunday Morning Bells (Nick Before BSB)
Swet (Nick)
Swing It Out
Teenage Wildlife (AJ)
Tell Me That I'm Dreamin'
That's The Way I Like It
That's What She Said
The Bloody Indulgent (Kevin)
The Great Divide (Nick)
The Me I'm Meant To Be (Howie)
The Perfect Fan
The Way It Was
There For Me (Nick)
There Goes My Baby (Kevin demo)
There's Us
This Is Just What I Needed (Howie)
This Is Us (three versions of this song)
Tijuana (Nick)
Trouble Is
Twas the Night Before Christmas (Brian, Leighanne & Baylee)
Undone (Nick Solo version)
Unica (Howie)
Unmistakable
Unmistakable (Nick Solo version)
Unsuspecting Sunday Afternoon
Uptown Girl (Nick Before BSB)
Useless (AJ)
Way To Your Heart (Howie)
We've Got It Goin' On
Welcome Home (You) (Brian)
We Lift You Up
Weird World
Welcome To My Heart
What If (AJ)
What It Do (AJ)
What Makes You Different (Makes You Beautiful)
What More Can I Give
What's Goin' On (Collaboration with Various Artists)
When Kiss Comes To Love (Harold and Brian Littrell)
Which One Am I? (Howie)
Who Needs The World (Nick)
Why I (AJ)
Will You Be There (Howie)
Wish (Brian)
Worth Fighting For (Yu Shiorta & Howie)
Worry (Howie)
Yes, I Will
You Alone (Brian)
You Are (Brian)
You Can Let Go
You Keep Giving Me Love
You Wrote The Book On Love
You're The Reason (Krystal and AJ)
You're The Reason (Krystal and BSB on her album)
You (AJ and Shawn from BIIM)
Songs performed (live songs we never got studios of, or covers):
America The Beautiful (Live)
All I Care About (Kevin Chicago Musical)
Aren't They Our Children (Live + Various Artists)
Baby Rise (AJ + Sarah Martin, Live)
Chain of Fools (Live With Aretha Franklin)
Don't Stand So Close To Me (live with Sting at Men Strike Back)
Don't Take The Girl (Brian, Live)
Dream (Nick, Live)
Every Breath You Take (live with Sting at Men Strike Back)
From This Moment (With Shania Twain)
Get Outta My Dreams (And into My Car) (Don't Forget The Lyrics)
Heaven In Your Eyes (Nick, Live)
Hey Joe (Nick, Live)
How Deep Is your Love (live at the Grammys)
I'll Make Love To You (live at the Grammys)
I'm The Only One (Don't Forget The Lyrics)
I Believe In A Thing Called Love (Don't Forget The Lyrics)
I Like To Sing (Nick & Aaron on Sesame Street)
I Want You Back (Live)
It doesn't matter (AJ Live)
It's Alright (Acapella)
Just Kids (Nick sang during Hang with the Band Event)
Just What I Needed (Nick, Live)
Lean On Me (Acapella)
Manager Of My Dreams (Arthur episode)
Not Like You (Nick, Live)
No Diggity (live cover)
Papa Was A Rolling Stone (live at the Grammys)
Philadelphia Freedom (live with Elton John at the Grammys)
The Most Beautiful Girl In The World (Howie, Live)
Touch Of Your Hand (written by Brian for Millennium)
True Love (Nick, Live)
Truly (Don't Forget The Lyrics)
Turn The Page (Nick, Live)
We Didn't Start The Fire (Don't Forget The Lyrics)
When I Grow Up To Be a Man (Live)
White Wedding (Nick, Live)
Would You Like to Swing on a Star? (BSB & Tony Bennett) – TB's Storytellers 1998
Everything(you are) (Brian)
Ain't With Me (AJ)
How deep is your love (Brian)
Total Eclipse of the heart(Brian & Leighanne)
You Really Got A Hold On Me (live cover)
Cruisin' (live cover)
Night Visions (AJ, live)
Drunkard's Prayer (AJ, live cover)
Boots On (AJ, live)
Boy and a Man (AJ, live)
Brick House (AJ)
I Am (AJ)
Saints and Sinners (AJ)
Sorry In Advance (AJ)
Where Can We Go From Here?
Who Do You Love? (Brian)
H.O.L.Y (FGL cover solo)
Razzle Dazzle (Kevin Chicago Musical)
Kevin (On Broadway live cover)
All I Have To Give (With FGL)
As Long As You Love Me (With FGL)
Cruise (With FGL)
Dirt (With FGL)
Everybody (With FGL)
H.O.L.Y. (With FGL)
I Want It That Way (With FGL)
Smooth (With FGL)
If there's any songs I'm missing please let me know!
Follow TDS
Backstreet Boys Go Nostalgic With the "Christmas In New York" Music Video
How Fans Can Support Nick, and Honor Aaron Carter.
The Boys Shine in a Whole New Way on "A Very Backstreet Christmas"
Things Fans Want To See For Backstreet Boys' Thirtieth Anniversary
Awesome Sites To Visit
The Official BSB Website
Nick Carter's Fan Club Page
What Happens On The Backstreet
Weird World Chile
Absolute Chaos
Dreamer's Sanctuary
Double Rainbow Fiction
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'Highway Diaries' reaches Kashmir
Dec 8, 2013 IANS
New Delhi, Dec 8 (IANS) Director Imtiaz Ali's latest episode of "Highway Diaries", short video snippets capturing the journey of his upcoming film "Highway", being shot in six states, presents the crew's activities in Kashmir.
In the latest episode of Highway Diaries titled "Songs of the Valley", the director is seen with folk singer Begum Jaan and her group.
He looks engrossed in their music while shooting for "Highway" with Randeep Hooda and Alia Bhatt at Aru valley in Kashmir.
"...while it (the episode) should connect with the larger experience that Veera (Alia's character) has in the film, it should still be about that time and place and its music," Ali said in a statement.
Presented by Sajid Nadiadwala, the film captures the landscape of the various regions in the country. The crew has already shot in Jammu, Delhi, Punjab and Himachal Pradesh and will bring it to the audience when it will come out Feb 21 next year.
Imtiaz Ali Pictures


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UPDATE: ATF called to assist investigation of Camino del Rio fire
Firefighters continue to monitor, douse hot spots
By Forrest Stone Herald Staff Writer
Monday, May 22, 2017 10:32 AM
Updated: Monday, May 22, 2017 8:57 PM
Two agents with the Bureau of Alcohol, Tobbacco, Firearms and Explosives are expected to arrive in Durango Monday afternoon to assist with the investigation of the fire at a vacant building on Camino del Rio.
Durango police and fire officials believe the fire was "likely not accidental" because all power and utilities to the building had been turned off in anticipation of its demolition in early June, said Deputy Chief Randy Black, of the Durango Fire Protection District.
The ATF has the equipment and a laboratory that can determine if an accelerant was used at the site, he said.
The fire at 1111 Camino del Rio was reported just before midnight Sunday and firefighters arrived less than two minutes later to find much of the structure ablaze. Black said it would be unusual for a fire in that location to have been burning long without being noticed because of its proximity to the highway, river trail and downtown.
One southbound lane of Camino del Rio from Ninth Street to 12th Street remained closed Monday afternoon as the fire continued to smolder and firefighters attacked hot spots. Black said he expects the highway to fully reopen Tuesday.
Fire Marshal Karola Hanks said Monday that there is no evidence that anyone was in the building during the fire.
The last of about a dozen businesses in the building, just south of Natural Grocer's, vacated in mid-May. The building was sold on March 31, and the new owners planned to demolish it and build an 86-room hotel. The brand of the hotel has not been announced.
Black said no one could benefit from the blaze, and he expects the investigation could slow the developer's plans to begin construction.
Black said a small storage shed toward the back of the same property at 1111 Camino del Rio was set on fire last week, causing damage to the interior of the structure. He was unaware if there were any suspects in that fire.
Black said vagrants had been using the area as a hang out in recent weeks, possibily living in the recently abandoned structure.
"Basically, we've heard there were vagrants living in there, but we've heard a lot of things," Black said. "That's why we investigate: to separate the myth from fact."
Police and fire investigators were following leads on the fire and conducting interviews on Monday, Black said.
Because the building was mostly empty, the fire was burning the structure itself, which was built in the mid-1960s, he said. The building would likely be considered a total loss.
Durango fire had 45 firefighters at the scene, as well as three chief officers, two investigators, two ladder trucks, four engines and four ambulances, Black said.
Staff writer Jonathan Romeo contributed to this report.
A fire that broke out Sunday night continues to smolder Monday, and traffic on Camino del Rio from Ninth Street to 12th Street is reduced to one lane in each direction. The cause of the fire remains under investigation.
Jonathan Romeo/Durango Herald
Fire engulfs recently vacated building on Camino del Rio
Firefighters work to contain a fire at a vacant building at 1111 Camino del Rio early Monday.
Shaianne Ganey/Durango Herald
EPA is committed to mine clean-up


text:
Condemned by the PTA
by Dan Moray
"I wrote frantically on the train all the way so we could have a script when I got back," said Hal Roach, who produced Babes in Toyland, a 1934 film adaptation of the Victor Herbert operetta that is every bit as busy as Roach himself on his trip back to Hollywood from New York, where he had seen it on the stage. Part Mother Goose, part Brothers Grimm, and part Disney, the film is full of storybook characters, children, and a host of extras. Two directors were used: one to direct the children and one for Laurel and Hardy.
Acting without their usual costumes, our comedic heroes play Stanley Dee and Ollie Dum, who live in the Old Woman's shoe with her bevy of children. They also work at a toy factory, which is making 100 six-foot-tall toy soldiers for Santa Claus. The trouble is Santa wanted 600 one-foot tall soldiers. Since our boys were the ones who got the order mixed up, they are fired on the spot — as Santa laughs in the background.
One of the funny things about this film is the large number of nursery characters — from Little Miss Muffet to the Cat and the Fiddle — who turn up in adult guise. Stan and Ollie had intended to give some cash to the Old Woman in the Shoe (Mrs. Peep) because the dastardly Mr. Silas Barnaby, spurned by Little Bo Peep (who prefers Tom Tom, the Piper's Son), wants to foreclose on her shoe. But since the boys lost their jobs, there's no cash to be had. Hence, to the horror of Toyland, Bo Peep must marry Barnaby, whose grisly face is shown in many frightful close-ups. Tom Tom, meanwhile, is banished to Bogeyland — a place full of "half-man, half-beast hairy monsters with sharp teeth and long sharp claws" — on trumped-up charges of having murdered one of the Three Little Pigs.
The childlike innocence of Toyland, coupled
with the ferocious violence of Bogeyland and the many sexual innuendoes, some ambiguous and some blatant, earned the film condemnation from the Parent-Teacher Association of America. Critics, however, loved it, even though it missed the advent of Technicolor by six months (with its windmills, spider webs, clocks, costumes, and sets, it would have rivaled the Munchkinland scenes from The Wizard of Oz for color and imagery). As Barnaby, Henry Kleinbach is one of the nastiest screen villains ever. The look on his face when he lifts Bo Peep's veil to "kiss the bride" is priceless.
For a children's film, Babes in Toyland sure has a lot of grown-up themes, but the only part that gave me a hard time was the singing. When Tom Tom breaks out in song in Bogeyland, it seems a little inappropriate. Victor Herbert's musical score may have worked on the stage but is somewhat out of place in the film. I wish Roach had not felt the need to produce it as an abbreviated operetta.
Nonetheless, this is an extremely enjoyable film. It's at the Michigan Theater on Sunday, April 7.


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Homewind
Weather warning for Somerset as 'strong gusty winds' expected
A yellow weather warning for wind is in place across South West England including Somerset from midnight until Friday. The warning, which comes as 'some strong gusty winds are expected', states: "Easterly winds will become […]
Warning of strong winds for Somerset
25th April 2019 0
A yellow weather warning for rain is in place across South West England including Somerset from tomorrow (Friday). The warning comes as 'a spell of strong winds is expected across parts of south Wales, south […]
Overnight wind warning for Somerset
A yellow weather warning for wind is in place across South West England including Somerset from tonight (Wednesday) until tomorrow morning. The warning states: "A period of very strong and gusty winds is likely on […]
Highways teams respond to over 500 calls as severe weather batters Somerset
Highways teams responded to more than 500 calls this week as Somerset was battered by severe wet and windy weather. More than 100mm of rain fell across Somerset between Saturday and Monday night – that's […]
Weather warning as Storm Angus to bring heavy rain and very strong winds
The Met Office has announced the first named storm of autumn 2016, Storm Angus. Storm Angus is expected to sweep through south-eastern England bringing some very strong south-westerly winds and heavy rain to affected parts […]
Weather warning as 'very strong winds' expected
A yellow weather warning of wind has been issued by the Met Office for today (Thursday). The Met Office said: "A brief spell of very strong winds is expected as a line of heavy rain […]
Yellow Weather Warning for Wind across South West on Friday
The Met Office has issued a yellow alert of wind for South West England tomorrow (Friday). The areas affected are likely to be: Bath and North East Somerset, Bristol, Cornwall, Devon, Gloucestershire, North Somerset, Somerset, South […]
Yellow Weather Warning for Wind across South West on Monday
26th July 2015 0
The Met Office has issued a yellow alert of wind across South West England tomorrow (Monday 27 July). The areas affected are likely to be: Gloucestershire, Swindon, Wiltshire, Bath and North East Somerset, Bristol, South […]
Yellow Weather Warning for Wind across South West on Monday and Tuesday
31st May 2015 0
The Met Office has issued a yellow alert of wind across South West England tomorrow (Monday 1 June) and Tuesday 2 June. The areas affected are likely to be: Bath and North East Somerset, Bournemouth, […]
Yellow Weather Warning for Wind across South West tomorrow
4th May 2015 0
The Met Office has issued a yellow alert of wind across South West England tomorrow (Tuesday 5 May). The areas affected are likely to be: Bournemouth, Cornwall, Devon, Dorset, Isles of Scilly, Plymouth, Poole, Somerset […]


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Mike Gordon Solo Album
Team JamBase
MIKE GORDON SET TO RELEASE DEBUT SOLO ALBUM
THE GREEN SPARROW OUT ON ROUNDER RECORDS AUGUST 5
Rounder Records is proud to announce the release of Mike Gordon's solo album, The Green Sparrow. Set for an August 5 release, The Green Sparrow finds the accomplished Phish bassist showcasing his wide range of influences in a confident ten-track album that reminds us that Gordon is one of the most imaginative and mold-breaking musicians in music today. Recorded at Gordon's home studio in Vermont and mixed at The Barn, his former band's studio and art space, The Green Sparrow features special guests Chuck Leavell, Phish's Trey Anastasio and Page McConnell, Bill Kreutzmann, Ivan Neville and horn players from Antibalas.
Since the break up of Phish in 2004, Gordon has participated in many projects, none of which have been purely solo. An artist known for his endless creativity and touring stamina, Gordon took 2007 off from touring and dedicated his time to writing this record. The end result is an eclectic album boasting Gordon's skillful assimilation of free-form rock & roll, calypso, pop and funk that showcases a unexpected side to Gordon¹s songwriting.
From the deeply rooted funk of "Radar Blip" to the rousing intricacies of "Andelman's Yard," from the spirited rock of "Traveled Too Far" to the punch of "Another Door," The Green Sparrow is a playful album from an artist who continually reveals new layers of far-reaching invention.
Gordon will play select festivals this July and will embark on a nationwide club tour in August and September. Tour dates to be announced shortly.
Upcoming Festival Dates:
07.04 Rothbury Festival Rothbury, MI
07.06 High Sierra Music Festival Quincy, CA
07.12 All Good Music Festival Masontown, WV
07.19 Mile High Music Festival Denver, CO
08.02 Gathering of The Vibes Bridgeport, CT
Happy Birthday Questlove: The Roots With Trey Anastasio, Warren Haynes & Bob Weir
Brandi Carlile Covers Aretha Franklin With The War & Treaty At The Ryman
Brock Butler Covers Prince's 'I Would Die 4 U'
Goose Sports Jorts For Playing In The Sand Finale
Bruce Springsteen Makes Surprise Appearance At Light Of Day Benefit In Asbury Park
Dark Star Orchestra Hosts Jam In The Sand 2020
Black Pumas Perform In Minneapolis: Pro-Shot Full Show Video
Cover Alert
Reviews, Recaps & Photos
Scott Bernstein
Brandi Carlile's third of six 2020 Ryman Auditorium concerts featured guest appearances from The War & Treaty and Sheryl Crow.
Dark Star Orchestra recreated two classic Grateful Dead shows and played a pair of elective concerts as part of their Jam In The Sand 2020 destination event in Mexico last week.
Mike Gordon Busts Out 'Soulfood Man' In Pittsburgh
Mike Gordon dusted off "Soulfood Man" for the first time since 2017 and just second time since 2014 as part of his band's Sunday night show at Mr. Small's in Pittsburgh.
Wilco Keeps Bust Outs Coming At Sky Blue Sky In Mexico
Wilco loaded their setlist with bust outs and rarities on Sunday at their Sky Blue Sky destination event in Mexico.
Dead & Company Concludes Playing In The Sand 2020 In Mexico
Dead & Company focused on Jerry songs over the course of Sunday's matinee at Playing In The Sand in Mexico.
Trey Anastasio Band Debuts Original & Performs 'Over The Rainbow' Cover In Los Angeles
Saturday's Trey Anastasio Band concert at The Wiltern in Los Angeles included the debut of a new original entitled "All Pretending" and a cover of "Over The Rainbow" from 'The Wizard Of Oz.'


After quite a dull but chilly feeling week, the weather is set to be brighter over the weekend on Merseyside.
Both Saturday and Sunday will see some brighter spells with temperatures averaging around 7C.
The Met Office say: "It will stay dry for the next few days with increasing amounts of sunshine and mostly light winds.
"Chilly nights are likely, with some frost and patchy fog".


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Q: ESQL code for CSV to XML conversion in IIB v10 I want to convert input CSV file to XML file using ESQL in IIB v10. Can you please help me with the ESQL code to achieve the same. I've provided the Input CSV file sample and Output XML file sample as below:
Input CSV file
Output XML file

A: Your question is fundamentally wrong. Using ESQL only to do it on Integration Bus is like using a knife to cut down a tree (when you have the choice with a chainsaw). If you want to convert a csv file to an xml, the proper solution is the following :
1) Define a new DFDL schema to parse the CSV file
2) Define your xsd for the output XML
3) Use the DFDL parser when you read the CSV, and use the structure you created (on the fileInput node for example, I don't know your exact case)
4) Use a mapping node to map from your DFDL structure to your XML structure (defined in the xsd)
Note : the last step can be done with alternatives solution, like compute Nodes (ESQL, Java, C#, php).
If you have any additional questions, feel free to contact me

redpajama_set_name: RedPajamaStackExchange


text:
Burkina Faso is een van de landen die deelneemt aan de Olympische Spelen. Burkina Faso debuteerde op de Zomerspelen van 1972 als Opper-Volta. Het heeft nog nooit deelgenomen aan de Winterspelen.

Tokio 2020 was voor Burkina Faso de tiende deelname aan de Zomerspelen. Op de Spelen van 2020 werd ook de eerste medaille behaald. Hugues Fabrice Zango behaalde de bronzen medaille in de atletiek bij het hink-stap-springen.

Medailles en deelnames
De tabel geeft een overzicht van de jaren waarin werd deelgenomen, het aantal gewonnen medailles en de eventuele plaats in het medailleklassement.

Per deelnemer

redpajama_set_name: RedPajamaWikipedia


text:
est un daimyo japonais du milieu de l'époque d'Edo, qui dirige le domaine d'Oshi. Il occupe également la fonction de Kyoto shoshidai.

Notes et références

Daimyo
Kyoto shoshidai
Naissance en décembre 1769
Décès en novembre 1808
Décès à 38 ans