text:
Small Business Living in Louisiana
Key Industries Overview
Strategic Partnership With LSU Sways IBM »
LED FastStart
LED Faststart Overview
Why FastStart
Traditional And Emerging Industries Are Growing »
Sites Overview
Certified Sites Programs
Certified Business Sites
Search Buildings
Louisiana's Incentives Offer Competitive Edge »
Living in Louisiana
Louisiana's Cost Of Living Is 4th-Lowest In U.S. »
The 2013 state-level ACCRA cost of living index ranked Louisiana as the 4th-lowest cost of living in the country.
Incentives Overview
Louisiana SmallBiz
Federal Opportunity Zones
Federal Opportunity Zones Drive Long-Term Investment »
LED FastStart Ranked No. 1 Ten Years Running »
International Commerce
STEP Grant
Benteler Group Expands To Louisiana »
EQ: Louisiana Economic Quarterly
Louisiana in the News
CSRA Chooses Bossier For Next-Gen Tech Center »
News Overview »
EQ: Louisiana Economic Quarterly »
Louisiana in the News »
News Releases »
Gary Perilloux
Louisiana-Filmed 'Class Rank' Premieres In U.S. Theaters This Week
High school comedy filmed in producer Shaun Sanghani's hometown of Alexandria
BATON ROUGE, La. — Class Rank, an independent film produced by Louisiana native Shaun Sanghani and shot in Central Louisiana, will premiere this week in U.S. theaters. Distributed by Cinedigm Corp., the movie will open Friday in select major markets across the country, as well as in Sanghani's hometown of Alexandria, Louisiana, where much of the principal photography took place.
"Louisiana is home to many talented individuals across the entertainment spectrum, and there is no better place than here for creative endeavors like making feature films," Louisiana Economic Development Secretary Don Pierson said. "Our entertainment program is encouraging production companies to put down roots and create jobs in Louisiana. Producer Shaun Sanghani's film is another good example of putting Louisiana's abundant creative talent to work."
Class Rank is the story of two high school outsiders who join forces to take on their local school board and its system of class rankings. The film stars Olivia Holt, who also will star in the television series Marvel's Cloak and Dagger, which filmed in Louisiana in 2017 and will launch on the Freeform network next month, and Skyler Gisondo (Santa Clarita Diet). Co-stars are Academy Award-nominee Bruce Dern (Nebraska, Chappaquiddick) and Kristin Chenoweth (Glee, Pushing Daisies). It was directed by Eric Stoltz (Madame Secretary, Pulp Fiction).
The movie was filmed in 2015 with estimated Louisiana spending of $528,000, including $142,000 in Louisiana resident payroll. It provided jobs for about 100 Louisiana residents and used about 500 extras, most of them students at a high school in Alexandria where several scenes were shot.
Sanghani credited Louisiana's Motion Picture Production Tax Credit for creating the opportunity to make Class Rank, and for encouraging production companies and creative individuals in the entertainment industry to stay in Louisiana.
"Most of my investors were Louisiana folks, just local people who believed in me – doctors, lawyers, just people who I've known for a while, who had seen my other work," said Sanghani, who was named by Variety this week as one of the "10 Producers to Watch in 2018." "When I make a movie here, it's a multiplying effect for me because I live here. When I make money, I spend more money, I pay more taxes and I make more movies. The same goes for the people that I hire that live here. I'm glad that Louisiana is building something here that is sustainable."
Fourteen projects are in production in Louisiana at this time, including seven feature films, six scripted television series and a reality TV series. Collectively, these productions are expected to spend more than $249 million in Louisiana, including more than $126 million in Louisiana resident payroll.
Feature films under way in the state include Open World (also known as Captain Marvel), You Might Be the Killer, The Secret, Foe Paw (also known as Happy Death Day 2), Five Feet Apart, Darkest Day and Fonzo. Scripted television series in production include NCIS: New Orleans (Season 4) for CBS, Queen Sugar (Season 3) for the Oprah Winfrey Network (OWN), Claws (Season 2) for TNT; ZeroZeroZero for Amazon; Deadlier Than the Male for TNT and The Purge for USA Network and Syfy. The reality TV series being filmed in the state now is Pit Bulls and Parolees for Animal Planet.
Among the high-profile motion pictures that completed filming in Louisiana in recent weeks were Greyhound, starring Tom Hanks, and The Highwaymen, starring Kevin Costner and Woody Harrelson.
Louisiana is one of the top destinations in the world for motion picture production; digital interactive and software development projects; live performance; and sound recording. With state-of-the-art facilities, a wealth of talent, production-friendly communities and a competitive incentive program, Louisiana is the perfect location for the entertainment industries. For more information on Louisiana film, digital interactive, live performance and sound recording programs, visit Louisiana Entertainment, a division of Louisiana Economic Development, at LouisianaEntertainment.gov.
Advanced Aero Services Announces Aviation Facility In Louisiana
At Shreveport Regional Airport, AAS will create an initial 60 jobs, with plans for major expansion
IBM To Conduct Career Fair For Client Innovation Center In Baton Rouge
Event at Baton Rouge Community College will help fill 35 information technology positions
Base Craft Creating Entertainment Jobs In Jefferson Parish
Film equipment company is latest participant in Louisiana's Entertainment Job Creation Program
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Select a country below for more information about how Louisiana can support your business.
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redpajama_set_name: RedPajamaCommonCrawl


Import images or Flash animations as foreground objects, customize their appearance and duration on the screen, add optional background music and copyright information, and then generate a standalone SCR screensaver file for distribution. !Easy ScreenSaver Station also allows you to include a license agreement, password-protect the screensaver, and create shareware screensavers with registration codes based on a mask code you specify.
We have 1 file for "!Easy ScreenSaver Station"


Yards certified through the National Wildlife Federation can post this sign.
Whether you fret over dwindling rainforests or attribute disappearance of neighborhood cats to displaced coyotes, most of us recognize loss of wildlife habitats as a growing environmental concern.
As an alternative to hand-wringing, the National Wildlife Federation offers ordinary citizens the means to take action by establishing a Certified Wildlife Habitat in their own backyard. It's not only enjoyable but very easy. I know because I did it in a matter of weeks despite starting out a gardening illiterate unable to name one in ten plants in my own yard.
Here's how the program works. A yard has to qualify in all five areas outlined below, but each offers a wide range of options and only one to three is required per category. Then there's a two-page checklist to fill out – works on the honor system – and a $15 processing fee. That's it.


text:
Who gets CPR from bystanders? Depends if it's a man or woman
By AMERICAN HEART ASSOCIATION NEWS
Women are less likely than men to receive bystander CPR in public and far more likely to die afterward, according to a new study.
Researchers analyzed 19,331 cardiac events that occurred between 2011 and 2015 and released their preliminary results Saturday at American Heart Association's Scientific Sessions.
"CPR involves pushing on the chest so that could make people less certain whether they can or should do CPR in public on women," said Audrey Blewer, M.P.H., the study's lead author and assistant director for educational programs at the Center for Resuscitation Science at the University of Pennsylvania in Philadelphia.
The data for the study was compiled by the Resuscitation Outcomes Consortium, a network of regional clinical centers in the United States and Canada that study out-of-hospital treatments of cardiac arrest and trauma.
The preliminary results they released showed 45 percent of men received bystander CPR in public settings compared to just 39 percent of women. By the time men were discharged from the hospital, their odds of survival were 23 percent greater compared to women.
The disparity narrowed to 35 percent of women and 36 percent of men receiving CPR when the emergencies occurred at home. There was no significant statistical difference.
"Ultimately this highlights a key knowledge gap in resuscitation science, that we need to characterize the bystander to better understand the barriers and biases to CPR delivery. From there this can help inform public messaging and public CPR training," said Blewer.
Disparities for men and women exist throughout healthcare, experts say.
"Studies have shown that gender disparities exist in cardiovascular disease, as well as response to time-sensitive conditions. To our knowledge, no study has examined whether gender variation exists with receipt of layperson bystander CPR," Blewer said.
According to a study released last year, women are less likely to receive several different potentially life-saving procedures after cardiac arrest. That research showed women were less likely to receive coronary angiography, which can be used to look for blocked arteries; angioplasty, which is the repair or unblocking of an artery; or to have their body temperature lowered to increase the chance of survival reduce the risk of brain damage.
Some of the problems begin early in the process. While heart disease is the No. 1 killer of women, only one-third of all heart research subjects are women even though genders exhibit different symptoms and reactions to medications, treatments and medical devices.
"As long as women are underrepresented in clinical trials, we will continue to lack data to make accurate clinical decisions on 51 percent of the world's population," said authors in a specialized women's issue of Circulation: Cardiovascular Quality and Outcomes in 2016 that drew attention to the issue.
November 12th, 2017|Cardiac Arrest, Health, Research, Scientific Sessions 2017, Women|Comments Off on Who gets CPR from bystanders? Depends if it's a man or woman


text:
package org.apache.flink.api.java;

import org.apache.flink.api.scala.FlinkILoop;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.configuration.JobManagerOptions;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.TestLogger;

import org.junit.Test;

import scala.Option;

import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertTrue;

/**
 * Integration tests for {@link FlinkILoop}.
 */
public class FlinkILoopTest extends TestLogger {

	@Test
	public void testConfigurationForwarding() {
		Configuration configuration = new Configuration();
		configuration.setString("foobar", "foobar");
		configuration.setString(JobManagerOptions.ADDRESS, "localhost");
		configuration.setInteger(JobManagerOptions.PORT, 6123);

		FlinkILoop flinkILoop = new FlinkILoop(configuration, Option.<String[]>empty());

		ExecutionEnvironment env = flinkILoop.scalaBenv().getJavaEnv();
		assertTrue(env instanceof ScalaShellEnvironment);

		Configuration forwardedConfiguration = env.getConfiguration();
		assertEquals(configuration, forwardedConfiguration);
	}

	@Test
	public void testConfigurationForwardingStreamEnvironment() {
		Configuration configuration = new Configuration();
		configuration.setString("foobar", "foobar");
		configuration.setString(JobManagerOptions.ADDRESS, "localhost");
		configuration.setInteger(JobManagerOptions.PORT, 6123);

		FlinkILoop flinkILoop = new FlinkILoop(configuration, Option.<String[]>empty());

		StreamExecutionEnvironment streamEnv = flinkILoop.scalaSenv().getJavaEnv();
		assertTrue(streamEnv instanceof ScalaShellStreamEnvironment);

		ScalaShellStreamEnvironment remoteStreamEnv = (ScalaShellStreamEnvironment) streamEnv;
		Configuration forwardedConfiguration = remoteStreamEnv.getClientConfiguration();
		assertEquals(configuration, forwardedConfiguration);
	}
}

redpajama_set_name: RedPajamaGithub


Community-based organizations often use phone and internet outreach to reach low-income seniors who may be missing out on benefits for which they are eligible. However, a substantial proportion of low-income seniors lack access to the internet in particular, either because they do not own a computer, do not know how to use a computer, or do not purchase an internet subscription.
This visualization can help organizations gauge how effective such outreach may be in their target area. In addition, community-based organizations such as senior centers may find it useful for understanding and adapting to the technological needs of their communities, e.g., hosting a computer lab.
The first tab, computer access, estimates the proportion of these households who did not have access to a computing device of any type at home – a laptop, desktop, smartphone, tablet, and so on.
The second tab, Internet access, estimates the proportion of these households that did not access the internet in 2013.
The third tab, phone access, estimates the proportion of these households that did not have access to any sort of phone in 2013.
The fourth tab, handheld device access, estimates the proportion of these households that did not have access to a handheld computing device such as a smartphone or tablet in 2013.
Unfortunately, limitations in the sample (described in more detail below) require analysis to be limited to Congressional Districts, which are often quite large geographical regions.


text:
\section{Introduction}

Explainability in recommendation systems (RS) has been a topic of great interest for a long time now~\cite{ex1,ex2,ex3}. Although the topic of explainability is fairly ubiquitous and is of general interest in the Artificial Intelligence (AI) community~\cite{xai1,xai2,xaideep}, it is especially important for recommendation to persuade users into accepting the recommended items. Since the aim of most recommendation systems is greater commercial viability, an ill-informed recommendation could lead to a long-term loss of users' trust. Hence, the stakes in modern recommendation systems are quite high with robust and interpretable recommendations being the need of the hour.

There have been recent works leveraging images for recommendation~\cite{shankar2017deep,mcauley2015image}. Deep Convolutional Neural Networks (DCNNs) have especially been of great utility in this regard and have proved to be more robust and reliable. For recommendations, ideas for using pixel-level attention have been explored in a few recent papers~\cite{zhou2017deep,chen2017attentive}. Explainability for deep learning based models is extremely challenging and is an active topic of current research~\cite{lapuschkin2016analyzing,samek2017evaluating,bach2015pixel}. However, there have been recent attempts to explain the predictions of classifiers in terms of features of the input. Layer-wise relevance propagation seeks to explain a classifier's decisions by decomposition~\cite{lapuschkin2016analyzing,bach2015pixel}. It redistributes the final prediction output backwards in the network so as to eventually assign relevant scores to each input variable (in the case of images, input variables are the image pixels).

In this work, we extend the framework of Layer-wise relevance propagation to the RS setting. The task of inferring items (images) relevant to a query item (image), such that the inferred items complement / substitute the query item is an important task in online shopping platforms like Amazon. We adopt a similar problem statement as McAuley et al.~\cite{mcauley2015image} and develop a DCNN based architecture similar to that proposed in the paper. The novelty in our approach is that we train our model end-to-end and incorporate layer-wise relevance propagation in this system so that features of the recommended images which were most central to the recommendation task are identified.  Through detailed simulation results on the dataset used by McAuley et al.~\cite{mcauley2015image}, we show that including the layer-wise relevance feedback in our model does produce effective explanations by employing the method of perturbations~\cite{lapuschkin2016analyzing}. We elaborate in the future works that the information of relevance of pixels in the explanations can be further integrated into the training method so as to enable a more robust identification scheme.

\section{The Methodology}

Our basic aim is to develop a method for inferring preference of users for the visual appearance of one object given a query image. To begin, we project every image in the dataset into a $K$-dimensional feature vector with the help of a DCNN. An important distinction between~\cite{mcauley2015image} and our method is that we do not use a pre-trained network. This is because, we intend to integrate layer-wise relevance propagation to improve the predictions of the model. Our approach is independent of the specific architecture of the DCNN used. The second last (fully-connected) layer results in the desired features of each input image. Similar to~\cite{mcauley2015image}, we learn a distance transform between feature vectors (that correspond to images) and use the distances to infer the probability of relevance of one image to another. The distance metric is personalized for each user since we are interested in evaluating personalized recommendations.

\subsection{Personalized Recommendations}

Let $\mathbf{x_i}$ and $\mathbf{x_j}$ denote the feature vectors of the $i^{th}$ item and the $j^{th}$ item respectively. Then, we define the distance function $d_u(\mathbf{x_i},\mathbf{x_j})$ to represent the distance between the $i^{th}$ item and the $j^{th}$ item according to the user $u$ as
\begin{equation}
    d_u(\mathbf{x_i},\mathbf{x_j}) = (\mathbf{x_i} - \mathbf{x_j}) \mathbf{M}^{(u)} (\mathbf{x_i} - \mathbf{x_j})^T
\end{equation}
Here, $\mathbf{M}^{(u)}$ is a personalized weighing of the importance of each feature for user $u$. $\mathbf{M}$ is a square matrix of $D$ dimensions. This distance metric is used to infer whether objects $i$ and $j$ are related. Let $R^u$ be the set of relationships such that $r_{ij}\in R^u$ relates objects $i$ and $j$ for user $u$. Section 3.1 describes the various relationships present in the dataset. We use a shifted sigmoid function of the distance $d_u(\mathbf{x_i},\mathbf{x_j})$ to infer $P_u(r_{i,j}\in R )$ i.e. the probability of objects $i$ and $j$ being related.

\begin{equation}
    P_u(r_{i,j}\in R) = \frac{1}{1 + e^{d_u(\mathbf{x_i},\mathbf{x_j})-q}}
\end{equation}
Here, $q$ is tuned during training so as to maximize prediction accuracy.

\subsection{Training}

Our complete model consists of a DCNN architecture to obtain relevant features from the image followed by a layer for computing the distances between the different feature vectors obtained from the DCNN. A VGG-16 model is used and the last fully-connected layer serves as the feature extractor. For training, we have an objective similar to that proposed in~\cite{mcauley2015image}, but a major difference is that we propagate gradients back through the DCNN as well and we have explicitly considered personalization. We first randomly sample a negative set $S = \{ r_{ij} | r_{ij} \notin R \}$ by maintaining $|R| = |S|$. This is a standard practice in training, wherein the probability of positive examples in maximized while minimizing the probability of neagtive examples.
\begin{align*}
    L(\mathbf{M},\mathbf{\Theta},q|R,S) = \sum_{u} (\sum_{r_{ij}\in R}-\log(1 + e^{d_u(\mathbf{x_i},\mathbf{x_j})-q} ) + \\ \sum_{r_{ij}\in S}(1 + \log(1 + e^{d_u(\mathbf{x_i},\mathbf{x_j})-q} ))  )
\end{align*}
Here, $\mathbf{\Theta}$ refers to all the parameters of the DCNN from the first layer to the feature extraction (pre-final) layer. We train our model end-to-end by performing standard backpropagation over the entire network by taking gradients of $L$ wrt the relevant parameters. We use ADAM optimizer for gradient descent~\cite{kingma2014adam}.

\subsection{Layer-wise relevance propagation}

This is a general technique that can be applied to most NN architectures~\cite{arras2017explaining,lapuschkin2016analyzing,bach2015pixel}. Here, we apply it to our entire DCNN framework. The model infers relationships between pairs of input images and eventually also recommends items based on a query image ( Section 3.4). Let $g(\mathbf{x})$ be the model's prediction. This prediction is redistributed to each input pixel and a relevance score $R_i$ is assigned to each input pixel $i$. The central idea of this relevance propagation is relevance conservation i.e. $ \sum_i R_i^{(1)} =  .... = \sum_j R_j^{(l)} = .... = g(\mathbf{x})$, where $l$ denotes a generic layer of the network. This implies that the total relevance is conserved at each layer.

This essentially means that at each layer of the DCNN, the total relevance which equals the prediction $g(\mathbf{x})$ is conserved. The relevance score of each input variable determines how much this variable has contributed to the prediction. Consider a neuron in our DCNN. It maps a set of inputs $x_i$ to an output $x_j$ through a combination of weights $w_{ij}$ and an activation function, let us call it $h(.)$. So,
\begin{equation}
    x_j = h \left(\sum_i w_{ij}x_i \right)
\end{equation}

The Relevance assignment mechanism works by computing a relevance $R_i$ for neuron $x_i$ (input) when all the relevances $R_j$ of outputs $x_j$ are given. We use the following formula for this propagation, although many variants exist~\cite{bach2015pixel}.

\begin{equation}
    R_i^{(l)} = \sum_j \frac{x_i^{(l)} w_{ij}^{(l,l+1)}}{\sum_k x_k^{(l)} w_{kj}^{(l,l+1)} + \epsilon\times sign(\sum_k x_k^{(l)} w_{kj}^{(l,l+1)})} R_j^{(l+1)}
\end{equation}

The output of layer-wise relevance propagation is essentially a heatmap of the image. Evaluating how good (informative) is the heatmap is tricky. The method of perturbations was proposed in~\cite{samek2017evaluating}. The key idea is that if one perturbs (changes the value of) the highly important input variables as predicted by the model, the decline in prediction score should be steeper than if other less important variables are perturbed. Using an iterative scheme to perturb input variables, we have an objective measure of explanation quality - larger decline in prediction accuracy corresponds to a more successful explanation scheme.

\section{Experiments}

\subsection{Setup}

We used Tensorflow r1.4~\cite{abadi2016tensorflow} and Python3 for all relevant programming. The dataset used is from~\cite{mcauley2015image}. The details of the dataset including the various sub-categories are illustrated in detail in this paper~\cite{mcauley2015image}. The open source code available at~\cite{lapuschkin2016analyzing} was used (after significant modifications) for incorporating the layer-wise relevance propagation in our architecture. There are four types of relationships between items (say A and B) in the dataset, namely  1) People viewed A and also viewed B, 2) People viewed A and ended up buying B, 3) People bought A and also later bought B, 4) People bought A and B together. The categories 1) and 2) are broadly categorized as `substitutes' as the products A and B have some notion of being substitutable while 3) and 4) are broadly categorized as `complements'. Section 1.1 of~\cite{mcauley2015image} elaborates more on the dataset. We perform a two-fold analysis, namely generating recommendations and inferring explanations. For effective comparison and benchmarking, all our experiments follow the same protocol, including the same train-test split of data outlined in section 4.1 of~\cite{mcauley2015image}.

\subsection{Results}
Our recommendation method differs from~\cite{mcauley2015image} with respect to the architecture of the DCNN and the fact that we did not use a pre-trained DCNN model as a feature extractor. By incorporating an end-to-end training mechanism, we obtained slightly better results that that reported in~\cite{mcauley2015image}. We used a VGG-16 model in the initial part of our pipeline such that its pre-final fully connected layer acts as a feature extractor. For initialization, all the model weights $\mathbf{\Theta}$ are assigned as per the pre-trained model on Imagenet dataset~\cite{deng2009imagenet}. To make things clear, this network is not fixed in our pipeline, but the parameters $\mathbf{\Theta}$ are updated during training unlike~\cite{mcauley2015image} (We use the pre-trained model to only initialize the weights). Table~\ref{tb:results} shows the results of this model on the test set after end-to-end training.

\begin{table}[]
\centering
\setlength\tabcolsep{2pt}
\caption{Results ($\%$ accuracy) averaged over items in each category for each of the four relationship types. $D$ refers to the dimension of the weighing matrix defined in Section 2.1. End-to-end training leads to better results than that reported in~\cite{mcauley2015image}. }
\label{tb:results}
\begin{tabular}{cccccc}
\hline
Category                                                                                & D & \begin{tabular}[c]{@{}c@{}}Buy after\\ viewing\end{tabular} & \begin{tabular}[c]{@{}c@{}}Also\\ viewed\end{tabular} & \begin{tabular}[c]{@{}c@{}}Also\\ bought\end{tabular} & \begin{tabular}[c]{@{}c@{}}Bought\\ together\end{tabular} \\ \hline
\multirow{3}{*}{Books}                                                                   & 10         & 70.3                                                        & 68.9                                                  & 69.1                                                  & 68.8                                                      \\
                                                                                & 100        & 72.6                                                        & 70.4                                                  & 72.2                                                  & 68.3                                                      \\
                                                                                & 500        & 72.4                                                        & 70.6                                                  & 72.3                                                  & 68.3                                                      \\ \hline
\multirow{3}{*}{\begin{tabular}[c]{@{}c@{}}Cell Phones\\ and Accessories\end{tabular}}   & 10         & 84.4                                                        & 78.8                                                  & 78.8                                                  & 84.2                                                      \\
                                                                                & 100        & 86.0                                                        & 83.7                                                  & 84.1                                                  & 88.3                                                      \\
                                                                                & 500        & 86.3                                                        & 83.5                                                  & 84.2                                                  & 88.3                                                      \\ \hline
\multirow{3}{*}{\begin{tabular}[c]{@{}c@{}}Clothing, Shoes\\ and\\ Jewelry\end{tabular}} & 10         & -                                                           & 77.3                                                  & 74.1                                                  & 78.4                                                      \\
                                                                                & 100        & -                                                           & 87.8                                                  & 85.0                                                  & 89.8                                                      \\
                                                                                & 500        & -                                                           & 88.1                                                  & 85.4                                                  & 89.9                                                      \\ \hline
\multirow{3}{*}{\begin{tabular}[c]{@{}c@{}}Digital \\ Music\end{tabular}}                & 10         & 68.7                                                        & 61.1                                                  & 74.6                                                  & 55.2                                                      \\
                                                                                & 100        & 73.1                                                        & 63.9                                                  & 76.8                                                  & 61.2                                                      \\
                                                                                & 500        & 73.3                                                        & 63.9                                                  & 76.7                                                  & 62.2                                                      \\ \hline
\multirow{3}{*}{\begin{tabular}[c]{@{}c@{}}Electronic\\ Items\end{tabular}}              & 10         & 83.9                                                        & 80.5                                                  & 78.0                                                  & 79.4                                                      \\
                                                                                & 100        & 85.8                                                        & 84.1                                                  & 83.2                                                  & 85.1                                                      \\
                                                                                & 500        & 86.1                                                        & 84.3                                                  & 83.2                                                  & 85.1                                                      \\ \hline
\multirow{3}{*}{\begin{tabular}[c]{@{}c@{}}Grocery and \\ Gourmet Food\end{tabular}}     & 10         & -                                                           & 78.1                                                  & 82.7                                                  & 79.6                                                      \\
                                                                                & 100        & -                                                           & 79.2                                                  & 86.9                                                  & 86.4                                                      \\
                                                                                & 500        & -                                                           & 83.1                                                  & 86.8                                                  & 86.3                                                      \\ \hline
\multirow{3}{*}{\begin{tabular}[c]{@{}c@{}}Home and \\ Kitchen\end{tabular}}             & 10         & 78.5                                                        & 81.3                                                  & 79.7                                                  & 79.5                                                      \\
                                                                                & 100        & 82.4                                                        & 83.8                                                  & 80.8                                                  & 84.2                                                      \\
                                                                                & 500        & 82.6                                                        & 84.1                                                  & 81.0                                                  & 84.7                                                      \\ \hline
\multirow{3}{*}{\begin{tabular}[c]{@{}c@{}}Movies \\ and TV\end{tabular}}                & 10         & 72.4                                                        & 69.9                                                  & 73.2                                                  & 68.4                                                      \\
                                                                                & 100        & 73.8                                                        & 69.8                                                  & 79.4                                                  & 69.6                                                      \\
                                                                                & 500        & 73.9                                                        & 70.3                                                  & 79.2                                                  & 69.8                                                      \\ \hline
\multirow{3}{*}{\begin{tabular}[c]{@{}c@{}}Musical \\ Instruments\end{tabular}}          & 10         & 84.6                                                        & 87.9                                                  & 85.7                                                  & 82.2                                                      \\
                                                                                & 100        & 90.3                                                        & 88.5                                                  & 85.9                                                  & 84.6                                                      \\
                                                                                & 500        & 90.9                                                        & 88.5                                                  & 86.4                                                  & 84.7                                                      \\ \hline
\multirow{3}{*}{\begin{tabular}[c]{@{}c@{}}Office \\ Products\end{tabular}}              & 10         & 81.9                                                        & 84.6                                                  & 84.5                                                  & 78.5                                                      \\
                                                                                & 100        & 86.8                                                        & 88.6                                                  & 87.2                                                  & 81.8                                                      \\
                                                                                & 500        & 87.1                                                        & 88.8                                                  & 87.6                                                  & 82.3                                                      \\ \hline
\multirow{3}{*}{\begin{tabular}[c]{@{}c@{}}Toys and \\ Games\end{tabular}}               & 10         & 75.7                                                        & 79.1                                                  & 78.8                                                  & 81.1                                                      \\
                                                                                & 100        & 77.2                                                        & 82.8                                                  & 82.4                                                  & 83.7                                                      \\
                                                                                & 500        & 77.5                                                        & 83.0                                                  & 82.7                                                  & 83.6                                                      \\ \hline
\end{tabular}
\end{table}

\subsection{Explanations for item relationships}

This section presents our results on how well layer-wise relevance propagation is able to generate explanations. Let us first clearly define what the explanations mean in our context. For each category, say the category `also viewed', the image pixels of both the images between whom the relationship is being considered which are more relevant to the relationship prediction task are highlighted. Note that this is different from the conventional relevance prediction of image pixels for a Deep Neural Network classifier, where the image pixels of the image that are instrumental to identification of the correct object in the image ( say a 'man') are highlighted. In our context, the pixels in both the images which lead to a correct identification of their relationship are highlighted.

To judge the efficacy of the explanations (highlighted pixels), the method of perturbations is employed~\cite{lapuschkin2016analyzing}. A random perturbation scheme is employed, in that input variables (pixels) are replaced by a random sample from a uniform distribution. We perform a category-wise analysis. For example, given an image of a book, we look at an image that is related to this image by the relation `buy after viewing' and is actually predicted correctly by the model. Now, after perturbation, we look at what relationship this image has with the original image (as predicted by the model). This is done for all the images in each category and we report the results averaged over all categories in Figure~\ref{fig:lrp}. Figures~\ref{fig:features} and~\ref{fig:explain} present anecdotal evidence of the explanations for two `camera' items.

\begin{figure}[t]
    \centering
    \begin{subfigure}[b]{0.45\columnwidth}
        \includegraphics[width=1\columnwidth]{camera1.png}

    \end{subfigure}
    ~
    \begin{subfigure}[b]{0.5\columnwidth}
        \includegraphics[width=1\columnwidth]{camera2.png}

    \end{subfigure}

    \caption{Two images of camera (in category Electronics) that are related via `also viewed' which the model predicts correctly}\label{fig:features}
\end{figure}

\begin{figure}[t]
    \centering
    \begin{subfigure}[b]{0.45\columnwidth}
        \includegraphics[width=1\columnwidth]{camera1new.png}

    \end{subfigure}
    ~
    \begin{subfigure}[b]{0.5\columnwidth}
        \includegraphics[width=1\columnwidth]{camera2new.png}

    \end{subfigure}

    \caption{Heatmaps of the two images superimposed on the original image for clarity. The highlighted pixels are those that layer-wise relevance propagation deems most suitable for informing the model about the relationship between these two images}\label{fig:explain}
\end{figure}

 \begin{figure}[t]
 \centering
        \includegraphics[width=1\columnwidth]{main.png}
        \caption{Layer-wise relevance propagation for different values of $\epsilon$. The decrease in accuracy averaged over the entire test-set for all categories is shown as a function of the number of perturbations. The greater the decrease in accuracy the better, because it implies that the explanations indicating relevancy of pixels is more accurate.}
        \label{fig:lrp}
    \end{figure}

\subsection{Recommendations and their explanations}

The pipeline described so far identifies relationships between the products but does not explicitly recommend items. In this section, we show how this model can be used for generating recommendations and also present anecdotal evidence of the process.  The recommendation task is formulated as, given a query item ( which could be a product being currently looked upon by the user, or a recently purchased) , we recommend a set of other items `to go along with it' or that the user might be interested in. Since we have already leveraged the relationships between items, this task is straightforward.

Following the approach of~\cite{mcauley2015image}, for the user $u$ given the feature vector $\mathbf{x_i}$ of the query item, the following is used to generate recommendations for each category $C$,

\begin{equation}
    \arg\max_{j\in C} P_u(r_{i,j}\in R)
\end{equation}

The authors of~\cite{mcauley2015image} do not consider personalization here, but in our approach we have used the personalized distance metric for inferring  $P_u(r_{i,j}\in R)$ as mentioned in Section 3. Figure~\ref{fig:rec} shows anecdotal evidence for this recommendation process. Without showing the long temporal history of users (which is not possible to illustrate here due to space constraint), how personalized the recommendations are cannot be ascertained. However, we can clearly see that complementary items to the query item are being recommended by the model.

In Section 3.3, we illustrated explanations for the task of inferring correct relationships between items. In Figure~\ref{fig:recexplain} we demonstrate the application of layer-wise relevance propagation to the above mentioned recommendation task. The heatmaps do appear a little noisy, indicating less certainty about relevant pixels. It is a part of our future work to investigate better schemes for tuning the layer-wise relevance propagation. To be clear about what explanations mean in this context, the highlighted pixels are the most informative (as predicted by layer-wise relevance propagation) for inferring which items will be complementary to the query item. We restrict the number of outputs to three here, but it is a modelling choice and can be varied.

\begin{figure}[t]
    \centering
    \begin{subfigure}[b]{0.22\columnwidth}
        \includegraphics[width=1\columnwidth]{stick.png}

    \end{subfigure}
    ~
    \begin{subfigure}[b]{0.22\columnwidth}
        \includegraphics[width=1\columnwidth]{ball.png}

    \end{subfigure}
    ~
    \begin{subfigure}[b]{0.22\columnwidth}
        \includegraphics[width=1\columnwidth]{bag.png}

    \end{subfigure}
    ~
    \begin{subfigure}[b]{0.22\columnwidth}
        \includegraphics[width=1\columnwidth]{golfbook.png}

    \end{subfigure}

    \caption{The first image on the left is the query. The remaining three are recommendations. The model correctly recommends `golf' related items given the query image. }\label{fig:rec}
\end{figure}

\begin{figure}[t]
    \centering
    \begin{subfigure}[b]{0.22\columnwidth}
        \includegraphics[width=1\columnwidth]{stick1.png}

    \end{subfigure}
    ~
    \begin{subfigure}[b]{0.22\columnwidth}
        \includegraphics[width=1\columnwidth]{ball1.png}

    \end{subfigure}
    ~
    \begin{subfigure}[b]{0.22\columnwidth}
        \includegraphics[width=1\columnwidth]{bag1.png}

    \end{subfigure}
    ~
    \begin{subfigure}[b]{0.22\columnwidth}
        \includegraphics[width=1\columnwidth]{golfbook1.png}

    \end{subfigure}

    \caption{Application of layer-wise relevance propagation denotes highlighted pixels that are likely to have been most informative for the recommendation task. The heatmaps are superimposed on the original images for clarity and better comparison.  }\label{fig:recexplain}
\end{figure}

\section{Future Works}

The major future work with respect to the method outlined in this paper is using the explanations to improve the model's predictions. Improving predictions include improving the extracted features and eventually inferring correctly the relationships between different items. These will eventually enable the model to give better recommendations. This is technically not a `future' work because we are already in the process of implementing it. We are incorporating the relevant pixels by configuring the convolutional layers to give more weight to the regions of the image where highly relevant pixels are present.

This is essentially a meta-learning process and the integration of relevant pixels in the training should be done through the validation set. The model is allowed to make predictions on the validation set, the explanations for these predictions are generated by layer-wise relevance feedback and the information from the relevant pixels are used to modify the weights in the convolutional layers of the network. So, including explanations for training essentially entails an outer training loop over the existing framework.

\section{Conclusion}
In this paper, we investigated the application of layer-wise relevance propagation for generating explanations in a deep-learning based recommendation framework. We developed an end-to-end mechanism for training the DCNN to generate relevant features and infer relationships between items. By applying layer-wise relevance propagataion to the entire framework, we showed through simulation studies that informative pixels are identified by the model. In addition, we explicitly considered the task of recommendation and generated explanations for the same. As highlighted in the Future Works, a lot remains to be done and we are hopeful that this direction of research will help in using explanations for improving modelling accuracy as well.

\bibliographystyle{abbrv}

redpajama_set_name: RedPajamaArXiv


The speech on Sunday comes after Arab leaders called on the United Nations to act to stop bloodshed that has persisted for nearly 15 months despite a UN-backed peace plan that includes the deployment of nearly 300 observers.
Since the so-called ceasefire began on April 12, as many as 2 300 people have been killed out of the more than 13 400 to have died in Syria since the uprising against Assad's regime began in March 2011.
Last month, Assad said his government was capable of finding a way out of the crisis gripping his country.
"Syria has been able to overcome the pressures and threats it has faced for years and is able to get out of this crisis thanks to the strength of its people and commitment to unity and independence," he said.
Assad's comments came as parliament convened for the first time since a controversial May 7 election boycotted by the opposition and dismissed by the West as a farce.


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Home Movies Movie on Sanjay Dutt Earned 180 Crores Before Release
imran+ Jun 9th, 2017 Comments Off
Movie on Sanjay Dutt Earned 180 Crores Before Release
imran+
Mumbai, The Indian leading director, Raj Kumar Heerani is going to direct the new movie, which will make on the personal life of Sanjay Dutt. This movie will release after few months but this movie is still nameless. This nameless movie has earned about 180 crore. It was considered very important to earn 100 crore and now it becomes common that the Indian movies are earning about 200 crores. The movie, which is going to make on the life of Sanjay Dutt, is going to set a new record.
The great expectations are being attached with the movie, which is going to make on the personal life of Sanjay Dutt. Raj Kumar Heerani is the director of the movie and he has already released the movies, Munna Bhai series, 3 idiots and PK. Ranbeer Kapoor will perform the role of Sanjay Dutt in this movie.
The film making company, Fox Star India has bought the rights of the release of the movie in 180 crore, which is a new record the satellite rights are not included in it. These rights are sold with the proportion of 85% and director raj Kumar Heerani will get 85% of the profit and Fox Star India will get 15% of the total profit.
The movies of 3 Idiots and PK were made under the direction of Raj Kumar Heerani, which were sold very heavily and the rights of the movie, 'PK' were sold in 110 crore and the satellite rights were also included in it. It is worth mentioned that Amer Khan performed in the movies, 3 Idiots and PK but in the recent movie on the life of Sanjay Dutt, Amer Khan is not included in this movie.
Tags: Featured, Movies, Sanjay Dutt


Betaine - alkaloid compound, found in sugar beet and other plants.
Many famous brand products are produced in third world countries where labour is cheaper and environmental standards may not be as stringent as their home countries. Such was the case with Union Carbide, whose low standards of safety in their plant in Bopal, India, resulted in the fatal poisoning of thousands of people with cyanide gas in the 1980's.
Clothing and footwear manufacturers have been known to exploit workers in Asia, many of whom are very young, paying them as little as a dollar for fourteen hours work, to produce shoes such as Nike which sell for up to $200 a pair in the Western world. The exposure of such unethical practices has resulted in the industry being forced to make guarantees that this would not continue.
Nestle contributed to the deaths of infants in Africa, by their militant marketing of milk formulas as being better for babies than breast-milk, but not allowing for the inadequacies of education and water sterilisation in many places.
"... most transnationals do not distain to fake their own goods. Using their own logos and brand-names, inferior products are substituted for the originals...Savlon, from Johnson&Johnson...in India...contains both Quinoline Yellow WS and Sunset Yellow FCF (banned in Norway). The same thing is true of Dettol. Colgate toothpaste appears to consist principally of chalk and cloves. (Unilever) Wheel detergent is 42 percent salt, which adds nothing to its cleaning capacity. In the West, detergents must contain less than four percent insoluble matter."
Vitamin B12 is not found in vegetables, but for vegetarians can be found in spirulina, eggs, milk, fortified cereals or fortified soy milk.


NEW DELHI: The Environment Pollution Control Authority (EPCA) wants the Centre to set a "ceiling and floor to the pricing structure" so that the taxi service aggregators can charge fares within this bracket.
The body also doesn't want surge pricing to be done away with altogether, arguing that it is "an inalienable component of the aggregators' business". These suggestions have been sent to Delhi, Haryana and Uttar Pradesh too.
The body has suggested that the government could fix a cap of 10% on the maximum permissible rate fixed by the government as surge pricing. When demand is high, the aggregators resort to surge pricing, that is multiple times the normal fare.
EPCA also feels the need to install fixed electronic meters for fare calculation in app-based taxis. Sources said a provision for fixed meters already existed in the guideline issued by the Centre.
A Central government official said, "When GPS is used to calculate the distance travelled, there is a margin of error of up to 30%. EPCA has recommended installation of BIS standard meters with 3% margin of error."
The current rates in Delhi under the radio taxi scheme are Rs 70 for the first three kilometres and Rs 25/km thereafter. In the economy taxi scheme, the base fare is Rs 25 and Rs 14/km for non-AC and Rs 16/ km for AC cabs thereafter. For some time, Delhi government has been mulling a uniform scheme for all cabs.
Explaining that the business model of aggregators require flexibility in the number of taxis operating at a given place and time, EPCA has suggested that, depending on demand, the limit on the number of taxis operated by a licensee could be removed or relaxed. Till now, a cap had been placed to disallow market capture and monopoly by any player.
However, there is one contentious recommendation too. Since the aggregators don't own any vehicles, they do not provide any parking space for taxis operating under its licence.
However, EPCA says, "The licensee, being responsible for providing the final service, must be held responsible for ensuring taxis operating under it have parking spaces."


Olympic champion Abhinav Bindra won the gold medal in men's 10-metre air rifle event in the ongoing 8th Asian Air Gun Championship in New Delhi on Sunday.
The 32-year-old Indian ace shooter produced an impressive final tally of 208.3 in the 22-men field to liven up the contest, which saw his compatriots Gagan Narang and Chain Singh finishing fourth and sixth respectively.
All the three Indian shooters have already secured quota places for next year's Rio Olympics.
The opening day of the competition also witnessed Indian Satyajeet Khandol winning the gold medal in 10m Men's Youth event. India team also won the Men's 10m event.
Bindra, who won gold at the 2008 Beijing Games, is one of the star attractions in an otherwise weak field. China, possibly the strongest team, opted to stay away from the competition, and South Korea and Japan have also sent their seconfd string teams.
The timing of the event can be questioned for the below par turn out, as national teams are focusing on the upcoming the Asian Championships in Kuwait — which will be the last qualifying event for the 2016 Rio Olympics.
The event at Dr Karni Singh Shooting Range will not offer any qualification spot for the Olympics. Strangely, winners in a couple of events will not be eligible for medals, and some world records will not get recognized.
However, there will be enough Indian stars in the fray, with the likes of top shooters such as Vijay Kumar, Jitu Rai, Henna Sidhu and Ayonika Paul also taking part in the competition.


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text:
TONIGHT! Wednesday 31st July 2019 – 8-10pm, Purple Haze with Mark Copolov on 88.3 Southern FM, Melbourne, Australia – Beatles cover band Come Together, James MacDonald, Jacobs Run and Peter Freebairn
On this version of 'Purple Haze', I'll have:
Live-in the-studio:
Beatles cover band Come Together
James MacDonald
Jacobs Run
Peter Freebairn
Beatles cover band Come Together:
Phil Smith presents the music of his all time favourite band, The Beatles. Over 60 Sold Out shows, so far, have had audiences raving. Joining Phil onstage are Keyboard maestro Cres Crisp, Gary Young on guitar and vocals, & Nigel Davis on Drums & Vocals.
This amazing line-up promise a Magical Mystery Tour through the Fab's repertoire, utilising great vocals and some unplugged arrangements of classic songs.
James MacDonald:
James is a founding & current member of Australia's top Abba cover band, Babba. On this show, James wil be playing some original songs as well as some covers
Jacobs Run:
Jacobs Run will be playing some original songs.
Here's the band's bio:
After just one week of release, Melbourne indie rock band jacobs run's new single Superman is the #2 Most Streamed and the #4 Most Downloaded on the Australian Play MPE Top 20 Singles Charts today! See links here: https://daily.plaympe.com/australian-weekly-top-20-streams/https://daily.plaympe.com/australian-weekly-top-20-downloads/
"We are absolutely over the moon and are extremely grateful for the support national radio has given us to date!" – Jacobs run. Featuring layers of guitar, infectious melodic hooks and glorious harmonies, Superman is another fine example of the exceptional songwriting talents of singer songwriter Michael Jacobs. In addition to Australia, the band's previous singles, Hold On A Minute, Use, Sleepwalking, Better Days and So Beautiful have also attracted generous airplay across the USA and Canada. In October 2018 and again in March this year, the band embarked on promotional trips to the USA to support the positive attention they're receiving there; performing industry showcase gigs in Los Angeles and New York, as well as playing a number of promotional shows in Texas, where the band are building a loyal following. The band will return to the US for a run of shows February 2020.
While in America, jacobs run were featured in the US trade publication, Radioinfo Talkers/Radioinfo Music Editor, Duane Doobie Jr. wrote, "jacob run's music blends layers of guitar and orchestration with organic classical melodies creating a powerful sound that highlights their Brit Pop influences while showcasing the Australian indie/alt-rock scene. The single' Sleepwalking' has the drive of a James Bond soundtrack that worms its way to listeners' ears just as offerings from similar artists, Oasis and Snow Patrol have done repeatedly within the genre". Superman was released via Play Mpe Thursday 27th June, with their debut album scheduled for a September release. Jacobs run are; Michael Jacobs (guitar/vocals), Fabian Bucci (drums) and Peter Curigliano (bass)
Jacobs run EPK: https://onepagelink.com/jacobsrun/
Peter Freebairn:
Peter will be performing some original songs from his self-titled new release.
Melbourne singer, songwriter, guitarist Peter Freebairn, has recorded his first solo work in the form of a self-titled six-track EP. Freebairn first came to prominence in the early 2000s with his band The Wish. After signing a lucrative publishing agreement with BMG, The Wish recorded with Daniel Jones (Savage Garden) in the producers chair which resulted in the national radio hits; Love Is Alive, Gotta Let You Know and Don't Change.
Following the 9/11 terrorist attacks in 2001, Peter was moved to write the heartfelt song, Faith Alive. The songs lyrics encourage people to keep their faith alive through times of grief, loss and tragedy. In 2006, the US comedy drama TV program Studio 60 On The Sunset Strip picked up the song, which made a big impact on US viewers. The program starred Matthew Perry who made his name playing the role of Chandler in the long-running US hit comedy series Friends.
When The Wish disbanded in 2006, Freebairn formed popular rock band Radio Vertigo who independently released the critically acclaimed album, Town Of Deceivers.
The new EP is another fine example of Freebairn's exceptional melodic songwriting, musicianship and vocal abilities. The songs on the new EP are largely written about love and loss. The track Billy is a beautiful heart-warming tribute to Peter's Dad.
An invitation to you from Jacobs Run and 88.3 Southern FM
Mark Copolov's 'Live In The Studio' show – Podcast (2.5 hours) – Purple Haze
Mike Heatley (co-producer of The Beatles new cd set 'On Air Live At The BBC Vol 2') interview with Mark Copolov on Purple Haze – Podcast (51 minutes 28 secs)
TONIGHT! Purple Haze with Mark Copolov, Wednesday 30th October 8-11pm. LEO SAYER, Russell Morris, Marty Rhone, Jacobs Run,Victor Stranges & more!
TONIGHT! 'Purple Haze' next Wednesday 29th May 2019 with presenter Mark Copolov – special guests Marty Rhone, Wild Dreamers (performing 'live in the studio') & new cds by Kevin Johnson, Rick Price and Peter Freebairn
tagged with Come Together (Beatle tribute band), James MacDonald, local band Jacobs Run, Mark Copolov, Peter Freebairn, Purple Haze


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Lines of patrons reliably spilled out onto sidewalk and neighboring courtyard outside the bar, which was one of the busiest restaurants in Coral Gables, particularly on weekends.
A single's scene, lunch go-to and after-work spot, Tarpon Bend was one of the largest and longest-running venues on Miracle Mile. It retained weekend crowds even after the city's $21 million, year-long renovation tore up the road out front.
"We were an anchor to that building, and we were an anchor to Miracle Mile," Eldred said.
But clearly change was afoot.
Eldred said after the building in which Tarpon Bend resided was sold five years ago, his expenses started to go up. The building was sold to New York based CF Miracle Mile Holding Company in 2013 for $21.5 million, up from the $14.6 million for which it sold two years previous. Eldred said the landlord's higher taxes and higher maintenance costs were passed on to his Tarpon Bend.
"We went back and forth with the landlord for the last two years and it just ended in a stalemate," he said.
Eldred said his costs went up "almost a million dollars" combined in the last five years. "That's a lot of fish fingers and French fries," he said.
In May, the restaurant's original location in Fort Lauderdale closed after its parent company, the Restaurant People, sold the space to a real-estate development group. But Eldred said his business was separate from the Fort Lauderdale spot.
Eldred said he will focus on his catering company and restaurant consulting to keep other businesses from having to go through what his did. The Tarpon Bend brand isn't dead, he said, and he may open a new version in a new location. If he does, the happy hour tokens he gave to regular patrons for discounted drinks will be honored.


text:
(DEPARTMENT OF AGRICULTURE & COOPERATION)
New Delhi, dated the 30th Dec. 1983
GSR 932 (E) � In exercise of the powers conferred by Section 3 of Essential Commodities Act, 1955 (10 of 1955), the Central Government hereby makes the following order, namely:-
1. Short title and extent
This order may be called the Seeds (Control) Order, 1983. It extends to the whole of India. It shall come into force on the 30th December of 1983.
In this Order, unless the context otherwise requires:
(a) "Act" means the Essential Commodities Act, 1955 (10 of 1955);
(b) "Controller" means a person appointed as Controller of Seeds by the Central Government and includes any person empowered by the Central Government to exercise all or any functions of the Controller under this Order;
(c) "Dealer" means a person carrying on the business of selling, exporting or importing seeds, and includes an agent of a dealer
(d) "Export" means to take or cause to be taken out from any place in India to a place outside India.
(e) "Form" means a form appended to this Order.
(f) "Import" means to bring or cause to be brought to any place in India from outside India.
(g) "Inspector" means an inspector of seeds appointed under Clause 12;
(h) "Registering authority" means a licensing authority appointed under clause 11;
(i) "Seeds" means the seed as defined in the Seed Act, 1966 (54 of 1966)
(j) "State Government" in relation to a Union Territory means the Administrator hereto by whatever designation known.
DEALER IN SEEDS TO BE LICENSED
3. Dealer to obtain license
No person shall carry on the business of selling, exporting or importing seeds at any place except under and in accordance with the terms and conditions of license granted to him under this Order.
Notwithstanding any thing contained in sub-clause (1), the State Government may by notification in the official Gazette, exempt from the provision of that sub-clause such class of dealer in such areas and subject to such conditions as may be specified in the notification.
4. Application for license
Every person desiring to obtain a license for selling, exporting or importing of seeds shall make an application in duplicate in Form "A" together with a fee of Rs.50/- for license to licensing authority.
5. Grant and refusal of license
1) The licensing authority may, after making such enquiry as it thinks fit, grant a license in Form "B" to any person who applies for it under Clause 4. Provided that a license shall not be issued to a person:-
a. Whose earlier license granted under this Order is under suspension, during the period of such suspension;
b. Whose earlier license granted under this Order has been cancelled, within a period of one year from the date of cancellation;
c. Who has been convicted under the Essential Commodities Act, 1955(10 of 1955) or any order issued there under within three years preceding the date of application.
2) When the licensing authority refused to grant license to a person who applied for it under clause 4, he shall record his reasons for doing so.
6. Period of validity of license
Every license under this order, shall, unless previously suspended or cancelled, remain valid for three years from the date of its issue.
7. Renewal of license
1) Every holder of license desiring to renew the license, shall before the date of expiry of the license, make an application for renewal in duplicate, to the licensing authority in Form "C" together with a fee of rupees twenty for renewal. On receipt of such application, together with such fee, the licensing authority may renew the license.
2) If any application for renewal is not made before the expiry of the license, but is made within one month from the date of expiry of the license, the license may be renewed on payment of additional fee of rupees twenty-five, in addition to the fee for renewal of license.
8. Dealers to display stock and price list
Every dealer of seed shall display in his place of business:
a) The opening and closing stocks, in daily basis, of different seeds held by him.
b) A list indicating prices or rates of different seeds.
9. Dealers to give memorandum to purchaser
Every dealer shall give a cash or credit memorandum to a purchaser of seeds.
10. Power to distribute seeds
Where it is considered necessary to do so in public interest, the Controller may, by an order in writing direct any producer or dealer to sell or distribute any seed in such manner as may be specified therein.
11. Appointment of licensing authority
The State Government may by notification in the official Gazette appoint such number of persons as it thinks necessary to be licensing authority and may also define in the notification the area within which each such licensing authority shall exercise his jurisdiction.
12. Appointment of Inspectors
The State Government may by notification in the official Gazette appoint such number of persons as it thinks necessary to be Inspector and may in such notification define the local area within which each such Inspector shall exercise his jurisdiction.
13. Inspection and punishment:
1) An Inspector may with a view to securing compliance with this order:
a. Require any dealer to give any information is his possession with respect to purchase, storage and sale of seeds by him;
b. Enter upon and search any premises where any seed is stored or exhibited for sale to ensure compliance with the provisions of this Order;
c. Draw samples of seeds meant for sale, export and seeds imported, and send the same in accordance with the procedure laid down in Schedule I, to a laboratory notified under the Seeds Act, 1966 (54 of 1966) to ensure that the sample conforms to standard of quality claimed.
d. Seize or detain any seed in respect of which he has reason to believe that contravention of this order has been committed or is being committed,
e. Seize any books of accounts or document relating to any seed in respect of which he has reason to believe that a contravention of this Order has been committed and is being committed.
f. Provided that the Inspector shall give receipt, in respect of the books of accounts or documents seized, to the person from whom they have been seized.
g. Provided further that the seized books of account or documents shall be returned to the person from whom the same had been sized after copies thereof or extracts there from as certified by such person have be taken.
2) Subject to the provision of paragraph (d) of sub-clause(1),the provision, of section 100 of the Code of Criminal Procedure,1973(2 of 1974)relating to search and seizure shall so for as may be, apply to searches and seizures under this clause.
3) Where any seed is seized by an Inspector under this clause, he shall forthwith report the fact of such seizure to a Magistrate where-upon the provision of section 457 and 458 of the Code of Criminal Procedure,1973(2 of 1974) shall, so far as may be ,apply to the custody and disposal of such seed.
4) Every person, if is so required by an Inspector, shall be bound to offer all necessary facilities to him for the purpose of enabling him to exercise his power under this clause.
14. Time limit for analysis
The laboratory to which a sample has been sent by an Inspector for analysis under this order shall analyze the said samples and send the analysis report to the concerned Inspector within 60 days from the date of receipt of the sample in the laboratory.
15. Suspension/Cancellation of license
The licensing authority may, after giving the holder of the license an opportunity of being heard, suspend or cancel the license on the following grounds, namely
a. That the License had been obtained by misrepresentation as to a material particular; or
b. That any of the provisions of this Order or any condition of license has been contravened.
16. Appeal
Any person aggrieved by an order-
a. Refusing to grant, amend or renew the license for sale, export or import of seeds.
b. Suspending or canceling any license, may within sixty days from the date of the order, appeal to such authority as the State Government may specify in this behalf, and the decision of such authority shall be final.
Provided that an application for appeal shall accompany an appeal fee of rupees fifty.
17. Amendment of license
The licensing authority may, on receipt of a request in writing together with a fee of rupees ten from a dealer, amend the license of such dealer.
18 Maintenance of records and submission of returns etc
1. Every dealer shall maintain such books, accounts and record relating to his business as may be directed by the State Government.
2. Every dealer shall submit monthly return relating to his business for the proceeding month in Form "D" to the licensing authority by the 5th day of every month.
R. K. Srivastava,
Joint Secretary to the Govt. of India
FORM "A"
(See Clause 4)
FORM OF APPLICATION TO OBTAIN DEALER�S LICENSE
The Registering Authority,
.................. (Place)
State of / U. T. of
1. Full name and address of the applicant :
2. Name and postal address
3. Place of business (Please give exact address)
a. For sale
b. for storage
4. Is it a proprietary/ partnership/ Limited Company/Hindu undivided family concern? Give the name (s) and address (es) of the proprietor/partner/Manager/Karta.
5. In what capacity this application is made :
a. Proprietor
b. Partner
c. Manager
d. Karta
6. Was the applicant ever convicted under the Essential Commodities Act,1955(10 of 1955) or any order issued there under during the last three years preceding the date of application? If so, give details.
7. Give the details of seeds to be handled.
8. Sr. No. Name of seed
I /We have deposited the license fee of rupees fifty vide challan No.______________ dated __________ in treasury/bank _________________________.
I/We declare that the information given above is true to the best of my/our knowledge and belief and no part thereof is false.
I /We have carefully read the terms and conditions of the license given in the form "B" appended to the Seeds (Control) Order, 1983, and agree to abide by them.
Signature of Applicant
Date _______________
Place________________
Note: (1) Where the business of selling /exporting /importing seeds is intended to be carried on at more than one place, a separate license should be obtained for each place
For use in the office of Licensing Authority
Date of receipt : ____________
Name and designation of officer receiving the application:
FORM "B"
LICENSE TO CARRY ON THE BUSINESS OF A DEALER IN SEEDS
LICENSE No.: __________ Date: _____________
Subject to the provision of the Seeds (Control) Order, 1983 and to the terms and conditions of the License Shri/ M/s. ____________________________ is hereby granted license to sell, export, import and store for the said purposes of seeds.
2. The license shall carry on the aforesaid business at ______________ (Place for storage and place of sale) ____________________ (Tehsil or District) ______________________.
Seal:
Licensing Authority
State of ____________
Terms and conditions of license
The license shall be displayed at a prominent and conspicuous place in a part of the business premises open to the public.
The holder of the license shall comply with the provisions of the Seeds (Control) Order, 1983 and the notifications issued there under and for the time being in force.
This license comes into force with immediate effect and shall be valid unto _________________ unless previously cancelled or suspended.
The holder of the license shall from time to time report to the licensing authority any change in the premises where he carries on his business of sale, export, import or storage for the said purposes of seeds.
The licensee shall give facility to the licensing authority or any other officer acting under his Authority for the purpose of inspecting his stock in any shop, depot or godown or other place/places used by him for the purpose of storage, sale or export of seeds.
FORM "C"
Application for renewal of license to carry on the business of a dealer in seeds
The Licensing Authority
______________ (Place)
State of /U. T. of _______
I/We hereby apply for renewal of the License to carry on the business of dealer in seeds under the name and style of Shri./M/s. ________________ ___________________________. The license, desired to be renewed, was granted by the Licensing Authority for the State of _____________________ and allotted License No._________________ on the ________________ day of ______________________19
Signature of Applicant (s)
Full name and address of the applicant (s) :___________________
Date and Place:
Certified that the License bearing No.__________________ granted on to carry on the business of a dealer in seeds at the premises situated ________________ is hereby renewed up to ________________________, unless previously cancelled or suspended under the provisions of the Seeds (Control) Order, 1983.
Date: ____________
Renewal No._____________
State of _________________
FORM "D"
Format of submitting Monthly Return relating to the seed business by seed dealer
(See Clause 18)
Month and year
Name and designation of Licensee:
(Quantity in Quintals)
Crop/ variety
Class of seed
Opening Stock on the first day of the month
Quantity purchased during the month
Total quantity imported
Total quantity sold
Total quantity exported
Closing stock on the last day of the month
3+4+5 6-(7+8)
Signature of Dealer
Name:-
SEALING, FASTENING, DISPATCH AND ANALYSIS OF SAMPLES
Manner of taking samples
Samples of any seed for the purpose of analysis shall be taken in a clean dry container which shall be closed sufficiently tight to prevent leakage and entrance of moisture and shall be carefully sealed.
II. Sampling Intensity
Bulk sampling � When sampling seeds lots are stored in bulk (heaps, bins, wagons, etc.) the following sampling intensity should be regarded as a minimum requirement for obtaining the "bulk sample".
Unless doubt exists about the homogeneity of a lot, all such primary samples should be combined to make a composite sample of the lot for submitting to the seed testing laboratory. Of the individual or primary samples are not sufficiently homogenous, they may be sent to the laboratory for a heterogeneity test.
a. Up to 500 kilograms- at least 5 individual samples except in case of small lots up to 50 kilograms where a smaller number of samples is sufficient not less than, 3 samples needs be taken.
b. b.501 to 3,000 kilogram- one individual samples for each 300 kilograms, but not less than 5 individual samples.
c. 3,001 to 20,000 kilograms- one individual sample for each 500 kilogram not less than 10 individual samples.
For seed in bulk the individual samples should be distributed at random all over the bulk and the samples drawn from varying depths.
Bag sampling- For seed lots in bags or other containers the following sampling intensity should be regarded as a minimum requirement:
d. Up to 5 containers- sample each container but always take at least 5 individual samples.
e. From 6 to 30 containers- samples at least one in every three containers but never less than 5.
f. 31 containers or more- samples at least in every 5 container but never less than 10.
III. Containers to be labeled and addressed- All containers containing samples for analysis shall be properly labeled and the parcels shall be properly addressed. The label on any sample of seed sent for analysis shall bear-
1. Serial number
2. Name of the sender with official designations, if any�
3. Name of the person from whom the samples has been taken
4. Date and place of taking the sample;
5. Kind or variety of the seed for analysis
6. Nature and quantity of preservative, if any, added to the sample.
IV. Manner of Packing, Fastening and sealing the samples- All samples of seed sent for analysis shall be packed, fastened and sealed in the following manner-
1. The stopper shall first be securely fastened so as to prevent leakage of the container in transit.
2. The container shall then be completely wrapped in fairly strong thick paper. The ends of the paper shall be neatly folded in and affixed by means of gum or other adhesive.
3. The paper cover shall be further secured by means of, strong twine or thread both above and across the container, and the twine or thread shall then be fastened on the paper cover by means of sealing wax on which there shall be least four distinct and clear impressions of the seal of the sender, of which, one shall be at the top of packet one at the bottom and the other two on the body of the packet. The knots of the twine or thr4ad shall be covered by means of sealing wax bearing the impression of seal of the sender.


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IBPS RRB Office Assistant
Questions asked in IBPS RRB Assistant Prelims Exam: 19th August 2018
Updated : Aug 19, 2018, 20:24
By : Kanika Ralhan
IBPS RRB Assistant Prelims exam is going to be held in 4 shifts today. Students having an exam in upcoming shifts must attempt these memory based questions so as to plan their exam strategy in a better way. In this article, we are sharing the important questions based on the IBPS RRB Office AssistantPrelims given by the students. Alos, We will be providing you with the section-wise questions which were asked from different topics across different shifts of the IBPS RRB Office Assistant Prelims Exam conducted, i.e 19 August 2018.
Check exam analysis:
IBPS RRB Office Assistant Exam Analysis 2018: 19th Aug (4th Shift)
IBPS RRB Office Assistant Exam Analysis 2018: 19th Aug (3rd Shift)
IBPS RRB Office Assistant Exam Analysis 2018: 19th Aug (2nd Shift)
IBPS RRB Office Assistant Exam Analysis 2018: 19th Aug (1st Shift)
Questions asked in IBPS RRB Officer Prelims 2018
Memory Based Caselete DI
City X has a population of 8000 in which the ratio of Males and Females is 3:5. Another City Y has a population which is 50% more than that of the City X and it has 500 males more than city X. (The given data is of the year 2013)
Q1. How many females are there in City Y?
C. 12000
E. None of these
Q2. What is the ratio of total males in City X and total females in City Y?
A. 5:12
B. 12:5
C. 6:17
D. 17:6
Q3. Females in City X are approximate what percentage of males in City Y?
A. 135
B. 122
D. 143
Q4. If the population of City X increased by 30% in 2014 and the ratio of M:F remained the same as for City X in 2013. Then, what were the total number of females in City X in 2014?
Q5. What is the ratio of females in City Y to the total population of City X?
B 17:18
C 17:16
D 16:15
E None of these
Caselete Example: MR. X Have To Type Different Type Of Words Per Day. The number of words typed by Mr. A on Monday is 40% of the total words typed in four days. Also, a number of words typed by him on three different days: Tuesday, Wednesday, and Thursday are in the ratio 2:3:1. The no. of words typed by Mr. X on Thursday is 300.
Memory-Based Arithmetic Question
Q. The ratio of the speed of upstream and speed of the stream is 5:6. If the speed of the stream is 30km/hr. Find the speed of still water.
Q. Find the circumference of circle which is inscribed in a square with an area of 196 square metre.
Q. A person sold 2 articles in 60000 each, on which he gain 20% profit and 20% loss. Find the difference of cost prices.
Q. The ratio of upstream speed and the speed of boat in still water is 5:6. The speed of the boat is 30 km/h. Find the speed of the stream.
Q. A and B can fill a tank in 11 days and A can fill an empty tank in 60 days so find in how many days can B fill the empty tank
Q. If area of the Circle is 616 Sqm and cost of fencing the Wall is Rs 2/M, then what is the total cost of fencing the wall?
Q. IF 2 is added to the numerator and 3 is added to the denominator, then ratio become 1/3. Also, the difference between the numerator and denominator is 17. Find the fracion.
Q. If the total Simple Interest on sum P at 10% per annum rate and at Sum P+300 n same rate for 4 years is280. Then find the value of P.
Memory-Based Month Based Puzzle
Q. There were eight persons born in a different month from January to August. A was born in the month having 31days. There were three persons born between A and D. Persons born before C are same the persons born after E. There were two persons born between C and D. G was born after D but before H.
Reasoning Questions:
1. If Surface is coded as QSPFCEG, then MUNDANE is coded as?
Memory-Based Number Series
1. 23, 33, 18, 38, 13, ?
Pattern: +10, -15, +20, -25, +30
Ans: 43
Pattern: +5, +7, +11, +13, +17
3. 10, 9, 16, 45, ?
Pattern: 10 x 1 - 1, 9 x 2 - 2, 16 x 3 - 3, 45 x 4 - 4
Ans: 176
4. 121, ?, 169, 196, 225, 256
Pattern: Squares of different numbers
Ans: 12 square = 144
5. 5, 30, 150, 600, 1800, ?
Pattern: x6, x5, x4, x3, x2
Ans: 3600
These questions were framed as per the reviews of the students. We will keep uploading more questions for all the shifts as per your feedback
Note: If you remember any question from the exam, feel free to share it in the comments section!
Attempt Free Mock for IBPS RRB 2018 here!
All the best for your exams,
Team gradeup!
Aug 19Bank & Insurance
Kanika RalhanCommunity Manager- Banking & Insurance exams
1361 Questions Answered Correctly


MD Tax Free Weekend Event ends today.
Enjoy Tax Free savings in Maryland for less than 12 more hours. Maryland's annual sales tax holiday ends tomorrow and there are many deals going on in select Maryland retailers.
What's Tax Exempt for Maryland Tax Free Week?
While school uniforms, shirts, shoes, flip-flops and underwear are tax exempt, you might be surprised that pajamas, cloth diapers and bathrobes are also exempt that week. Many medical items such as adult diapers and braces are exempt, along with golf clothes, martial arts uniforms and sports uniforms.
But items on many back-to-school shopping lists — sports cleats, baseball and lacrosse gloves, bookbags and backpacks — all remain taxable that week.
Connecticut Tax Free Week 2016 starts tomorrow! More details.


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Sabatierite (Cu6TlSe4) is a mineral found in the Czech Republic. The composition of the mineral is more likely (Cu4TlSe3) that has been chemically and crystalographically characterized having tetragonal symmetry.</ref> It is named for the French mineralogist Germain Sabatier (born 1923).

See also

 List of minerals
 List of minerals named after people

References

Copper(I,II) minerals
Thallium minerals
Selenide minerals
Orthorhombic minerals

redpajama_set_name: RedPajamaWikipedia


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Throughout Don's career, well known businesses have hired him to take care of their business tinting needs, including Abdalla's (when they were downtown), Mosley & Hollard (River Ranch), Pieces of Eight (Oil Center), and S & K Menswear, etc. These and several businesses choose to use tint in order to protect their merchandise, preventing clothes from fading, and art from loosing it's coloring and value. All of these businesses were pleased with the overall outcome, product and service! To learn more about the quality of the product used in commercial applications visit our commercial tinting page.
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Sanofi Pasteur to leverage its strong vaccine legacy in hunt for Zika vaccine
- Building on the company's successful history in developing vaccines against similar viruses, most recently the introduction of Dengvaxia® against dengue, Sanofi Pasteur is launching a Zika vaccine project -
Lyon, France - February 2, 2016 - Sanofi Pasteur, the vaccines division of Sanofi, announced today that it has launched a vaccine research and development project targeting the prevention of Zika virus infection and disease.
Sanofi Pasteur leads the vaccine field for viruses in the same family as Zika virus (ZIKV), with licensed vaccines against Yellow Fever, Japanese Encephalitis and, most recently, Dengue. Importantly, Sanofi Pasteur's expertise and established R&D and industrial infrastructure for the newly licensed vaccine for dengue, Dengvaxia®, can be rapidly leveraged to help understand the spread of ZIKV and potentially speed identification of a vaccine candidate for further clinical development.
"Our invaluable collaborations with scientific and public health experts, both globally and in the regions affected by the outbreaks of ZIKV, together with the mobilization of our best experts will expedite efforts to research and develop a vaccine for this disease," said Dr. John Shiver, Global Head of R&D, Sanofi Pasteur.
The ZIKV is closely related to Dengue; it belongs to the same Flavivirus genus, is spread by the same species of mosquito and has a similar acute clinical presentation. Common symptoms caused by a Zika infection include fever, rash, joint swelling, conjunctivitis and headaches. However, there is a growing body of evidence linking Zika infection in pregnant women with an increased risk of a severe congenital complication at birth called microcephaly. Normally a rare condition, microcephaly results in an abnormally small head impairing brain development.
"Sanofi Pasteur is responding to the global call to action to develop a Zika vaccine given the disease's rapid spread and possible medical complications," says Dr. Nicholas Jackson, Global Head of Research for Sanofi Pasteur who will be driving the new ZIKV vaccine project. "In addition to the serious possibility of congenital complications associated with Zika, investigations are also underway to assess another reported connection between Zika and a dangerous neurological disorder".
Until recently, ZIKV was considered a rare and seemingly benign virus. However in May 2015, the Pan American Health Organization (PAHO) issued an alert regarding the first confirmed ZIKV infection in Brazil, and since then it has spread across the Americas. In the United States, authorities have reported a locally transmitted case of Zika in Puerto Rico, with reports of cases in continental United States in returning travelers.
At a briefing session during the 138th Executive Board of the World Health Organization (WHO), the WHO Director General, Dr. Margaret Chan, stated that the WHO is deeply concerned about ZIKV for four main reasons:
the possible association of infection with birth malformations and neurological syndromes;
the potential for further international spread given the wide geographical distribution of the mosquito vector;
the lack of population immunity in newly affected areas;
absence of vaccines, specific treatments, and rapid diagnostic tests.
In addition, the Centers for Disease Control and Prevention (CDC) have issued travel recommendations for pregnant women to post-pone travel to countries in Latin America and the Caribbean where ZIKV transmission is ongoing.
Presently there is no vaccine or specific treatment for Zika. Vector control remains an important means of potentially controlling the mosquitoes responsible for spreading Zika.
Sanofi, a global healthcare leader, discovers, develops and distributes therapeutic solutions focused on patients' needs. Sanofi has core strengths in diabetes solutions, human vaccines, innovative drugs, consumer healthcare, emerging markets, animal health and Genzyme. Sanofi is listed in Paris (EURONEXT: SAN) and in New York (NYSE: SNY).
Sanofi Pasteur, the vaccines division of Sanofi, provides more than 1 billion doses of vaccine each year, making it possible to immunize more than 500 million people across the globe. A world leader in the vaccine industry, Sanofi Pasteur offers a broad range of vaccines protecting against 20 infectious diseases. The company's heritage, to create vaccines that protect life, dates back more than a century. Sanofi Pasteur is the largest company entirely dedicated to vaccines. Every day, the company invests more than EUR 1 million in research and development. Sanofi Pasteur is committed to supporting the global public health community fight preventable disease and supports public private partnerships that leverage diverse capabilities to expeditiously respond to potential epidemics. For more information, please visit: www.sanofipasteur.com or www.sanofipasteur.us
Global Media Relations U.S. /Country Media Relations
Alain Bernal Susan Watkins
T. +33-4-37-37-50-38 T. +570-957-2563
alain.bernal@sanofipasteur.com susan.watkins@sanofipasteur.com
www.sanofipasteur.com www.sanofipasteur.us
T. + 33 1 53 77 45 45


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legendary sports cars
The 2023 Ford Mustang redefines the sports car category
Thu, 09/15/2022 - 10:26 in
At the North American International Auto Show (NAIAS), Ford Motor Company unveiled the seventh iteration of their iconic sports car. The newest model of the best-selling sports car in the world, the Ford Mustang, will be available for...read more
The Mazda MX-5 is an icon that, along with the Ford Mustang and Porsche 911, is part of the most popular sports cars in history. The fourth generation of the Japanese roadster is currently in top shape, however, Mazda is...read more