text:
For the official Meteor ui-router package:
[https://atmospherejs.com/angularui/angular-ui-router](https://atmospherejs.com/angularui/angular-ui-router)

meteor-angular-ui-router
========================

This package adds the ability to use Blaze templates with ui-router
> [angular-ui-router](https://github.com/angular-ui/ui-router "angular-ui-router") package for Meteor.

## Install
```
meteor add urigo:angular-ui-router
```

Depends on [https://atmospherejs.com/angularui/angular-ui-router](https://atmospherejs.com/angularui/angular-ui-router)

## Usage

### app.js
```
angular.module('myApp', ['angular-meteor', 'ui.router']);

angular.module("myApp").config(['$stateProvider', '$urlRouterProvider',
    function ($stateProvider, $urlRouterProvider) {
        $urlRouterProvider.otherwise("/");

        $stateProvider
            .state('home', {
                url: "/",
                template: UiRouter.template('home.html')
            })
            .state('state1', {
                url: "/state1",
                template: UiRouter.template('state1.html')
            })
            .state('state1.list', {
                url: "/list",
                template: UiRouter.template('state1.list1.html')
            })
            .state('state2', {
                url: "/state2",
                template: UiRouter.template('state2.html')
            });
    }
]);
```

### partials/state1.html
```
<template name="state1.html">
    <div>
        <h1>State 1</h1>

        <h3>[[title && title || 'Title is empty']]</h3>
        <span>Input a title: </span>
        <input type="text" ng-model="title">

        <hr/>

        <a ui-sref="state1.list">Show List</a>
        <div ui-view></div>

    </div>
</template>
```

redpajama_set_name: RedPajamaGithub


Saturday, June 1, 2019 - The Mound Fire Department and its firefighters will once again be hosting the MFD Fish Fry, with 2019 marking the 68th Anniversary of good times, good fish, and good community. We welcome you to join us as we work to reach 3,000 fish dinners served!
Tickets for the event include dinner, featuring the traditional fish meal, and an evening dance with live music by Pop Rocks Band!
Tickets will be sold in advance by firefighters canvassing neighborhoods for $10.00 each and will also be available at the door for $12.00. If you need tickets, you may also call Vicki Weber at the Mound Fire Station administrative office at 952-472-3555.
The crowd-pleasing event packs in the flavor and fun for the whole family.
Thank you for your continued support, the Mound Fire Department looks forward to seeing you again! Please bring the family and join us for a fun day!
"I'm sorry, you said they CAUGHT the fish??"
The Mound Fire Department Fish Fry and Dance has been a long standing event and a tradition within the fire department and the community. In June 2014, the Mound Fire Department Relief Association hosted the 63rd Annual Fish Fry and Dance and served dinners to 2,541 people.
This very successful event was started by Mound Firefighters many years ago as a fund raiser for the fire department. Since the very first fish fry and dance, the firefighters have served dinners to over 110,000 people!
The first Fish Fry and Dance was held in the early 1950's. Over the years, the Mound Fire Department Fish Fry event has received enormous support from the Greater Mound Community. Many items, including tools, equipment, station supplies, and firefighter safety gear, have been purchased with the proceeds.
In the early years of the Fish Fry and Dance, the Mound firefighters would catch all of the fish to be fried. In 2012, the fire department fish fry needed about 2,600 pounds of fish to feed the hungry customers. WOW, how times have changed!
The Mound Firefighters will host the 68th Fish Fry and Dance on June 1, 2019 and truly appreciate the ongoing support!
We've been doing this for a while now, and we think we're pretty good at it. Go ahead - click one.
In 2017, the Mound firefighters and their families hosted the 66th Annual Fish Fry and Dance. Area residents turned out in large numbers and supported the event as they always do. We served 2,827 fish dinners, which made the event a huge success.
The Mound firefighters would like to say thank you to all of our friends, neighbors, and Lake Minnetonka area residents that attended. We would also like to thank the many local businesses that contributed to our event with ticket purchases, and also those that donated prizes to our silent auction and raffle. Your continued support of the fire department is greatly appreciated.
Again, thank you for support the Mound Fire Department and Relief Association!
Over the years, proceeds from the Fish Fry have been used to purchase a plethora of needed equipment for the Mound Fire Department.
One such example, the Mound Fire Department purchased two new Scott Eagle Attack Thermal Imaging Cameras at a price of $6,100 each.
A Thermal Imaging Camera (TIC) is an important safety tool and technologically-advanced tool available to our department. A TIC has many uses in fire department operations, with a primary function being search and rescue for victims. In a fire, a firefighter may not be able to see though smoke, but the TIC can assist in locating a person by reading different temperatures. When investigating or looking for a possible fire, the TIC can detect "hot spots" inside or on the other side of a wall. A thermal imaging camera will also give useful information about parts of a structure that are still hot.
This purchase was largely (75%) funded by 2013 Fish Fry proceeds and a donation from the MFD Auxiliary.
Thank you for supporting the Mound Fire Department!


text:
Joe Canning Poetry
Stair na hÉireann Photography
Stair na hÉireann | History of Ireland
Irish History, Culture, Heritage, Language, Mythology
#OTD in 1754 – Birth of Dr. William Drennan in Belfast; physician, poet, educationalist political radical and one of the chief architects of the Society of United Irishmen.
Stair na hÉireannHistory, Ireland, Irish HistoryBelfast, Chief Architect of The United Irishmen Society, Co. Antrim, England, Ireland, physician, Poet, The Emerald Isle, The United Irishmen Rebellion, Wake of William Orr, William Drennan
William Drennan's poetic output included some powerful and moving pieces. He is chiefly remembered today for "Erin" written in 1800, in which he penned the first reference in print to Ireland as "The Emerald Isle":
"Nor one feeling of vengeance presume to defile
The cause, or the men, of the Emerald Isle."
Drennan came to national attention with the publication in 1784 and 1785 of his Letters of Orellana, an Irish Helot, the earliest expressions of his support for radical constitutional reform, Catholic emancipation and civil rights. By 1785 Drennan began to voice plans for an organisation of radicals dedicated to far-reaching political reform. His plan took concrete form in 1791 when he devised the manifesto of what became the Society of United Irishmen.
In May 1794 Drennan was arrested on a charge of seditious libel, the charge related to an address he had written for the Dublin Society of United Irishmen. He prepared his own defence for the trial, but his lawyer wisely prevented its delivery. Although Drennan was charged with publishing his address, rather than writing it, against all the odds his lawyer managed to secure his acquittal by exposing the false testimony of the prosecution's main witness.
The United Irishmen eventually moved away from Drennan's program of reform and closer to violence and rebellion. Although his poem, 'Wake of William Orr', written after the hanging of a Co Antrim farmer for administering the oath of the United Irishmen in 1797, had encouraged the rebels, he withdrew his support and played no part in the doomed uprising of 1798. While deploring the violence to which his countrymen and co-religionists resorted, he remained a consistent opponent of the union with Great Britain, brought about in 1801.
Some of its most famous lines went of the poem "The Wake of William Orr" went:
"Here our murdered brother lies,
Wake him not with women's cries;
Mourn the way that manhood ought,
Sit in silent trance of thought…"
Drennan died on 5 February 1820 and was buried in Clifton Street burial-ground in Belfast. With deliberate symbolism his coffin was borne to the grave by three Catholics and three Protestants.
Image | The Grave of William Drennan, Chief Architect of the United Irishmen
Posted by Stair na hÉireann
Stair na hÉireann is steeped in Ireland's turbulent history, culture, ancient secrets and thousands of places that link us to our past and the present. With insight to folklore, literature, art, and music, you'll experience an irresistible tour through the remarkable Emerald Isle.
#OTD in 1798 – The first clashes of The United Irish Rebellion begins in Wexford and in Leinster just after dawn.
#OTD in Irish History – 24 May:
Gold Winner – Arts & Culture and Education & Science Blogs 2018
Silver Winner – Education & Science blog 2017
Bronze Winner – Arts & Culture Blog 2016
Stair na hÉireann/History of Ireland
Category Search Select Category American History An t-Ár Mór Ancient History Ancient Irish History Ash Wednesday Án Ócras Mór Án Gorta Mór Án Ocras Mór Brehon Law British British History England Famine Father's Day French History Genocide Hibernophobia History History of England Holocausd na nGaedheal Home Rule Ireland Irish Arts Irish Curse Irish folklore Irish History Irish Language/Irish Gaelic Irish Legends and Folklore Irish Medieval History Irish Music Irish Mythology Irish Poetry Irish proverbs Irish recipe Irish Sports Irish Superstitions Irish Theatre Irish-American History Irish-Australian History Irish-British History Irish-Canadian History Irish-Chilean History Irish-English History Irish-Mexican History Irish-Scottish History Maritime History Merry Christmas Newfoundland History Northern Ireland Orange Order History Photography of Ireland Poetry by Joe Canning quotes sayings Science and Education Shrove Tuesday Uncategorized World History

redpajama_set_name: RedPajamaCommonCrawl


Using Magento 2 Custom Registration Fields module you can easily add custom address field on the customer registration page. Not only address field, you can create any type of custom field on the customer registration page to fetch extra details from the customers.
On the front-end, the customers will view these custom fields on the registration page. The customers can also update the custom registration field information from from their accounts.


text:
\section{Introduction}

Each year, 1.35 million people die on roadways around the world. Road traffic injuries are estimated to be the eighth leading cause of death globally for all age groups and the leading cause of death for children and young people 5–29 years of age \cite{world2018global}. During the past century, road traffic safety has indicated methods and measures to prevent road accidents. However, an important indicator of survival rates after occurrence of accidents is underestimated. That indicator is the time between when the accident occurs and when emergency medical personnel are dispatched to the scene. Minimizing the time between these two events decreases mortality rates by $6\%$ \cite{white2011wreckwatch}. Researchers have been exploring road infrastructure potentials with the hope to find a reliable approach for real-time sensing of the accident time. A reliable automatic accident detection enables immediate notification to the emergency personnel, and consequently, reduced dispatch time. Moreover, drivers approaching the site can be notified to possibly use alternative routes or at least slow down for safety.

Traffic flow data has been used widely to detect traffic events since the defined metrics on them show much correlation to those events. A considerable portion of the traffic flow data comes from loop detector sensors. These  sensors,  despite  their  noisy  data  and  lack of  high  spatial  coverage,  are  mounted  on  countless  roads. Hence,  developing  models  that  can  yield  good incident detection rates by using these sensors helps governments obtain valuable insights until new technologies start to dominate. Accidents can impact the traffic flow patterns in the upstream and downstream of the accident site. Capturing these patterns in the traffic flow and associating them to the occurrence of accidents is not an easy classification problem. The main objective of this study is to determine the degree to which deep representation of the features obtained from loop detectors can identify crashes automatically after their occurrence.

Another objective is to handle imbalanced spatio-temporal traffic flow data. Traffic accidents are naturally rare events such that the datasets are inevitably imbalanced. The main problem of training a model with imbalanced datasets is that the model cannot provide robust outputs for the minor class since it not exposed to sufficient amounts of data in the minor class to learn the pattern. Moreover, the low number of crash samples can increase the possibility of overfitting in the model.

To fulfil these objectives, We investigate an LSTM-based framework and explore its different settings to increase accident/no-accident class separability in the encoded feature space and to reduce the dimensionality of the traffic flow data. To test our experiments, We use automatic traffic recorder (ATR) data provided by Minnesota department of transportation.  These data are obtained from loop detectors: continuous count devices with loops in the pavement that collect traffic volume. Other useful attributes that are obtained from aggregated ATR data include capacity, density, flow, headway, occupancy, and speed. Since crashes disrupt normal traffic flows, we expect to be able to identify flow disruptions that are attributed to traffic accidents after a certain time past the accidents depending on the nature of them.

The remainder of this paper is organized as a follows. Section \ref{S:2} describes the related work previously done in the field of automatic accident detection. Section \ref{S:3} provides the description of the data and the challenges associated with concurrent using of crash and loop detector data. Section \ref{S:4} presents the modeling approach in detail and provides a short background on the methods incorporated in the model. The model evaluation strategy is also described at the end of this section. Section \ref{S:5} reports and interprets the results. Finally, a discussion on the results and concluding remarks are presented in section \ref{S:6}.

\section{Related Work}
\label{S:2}

Accident detection has been studied in the transportation community for at least 50 years. The California algorithm, developed in 1978, detects the occurrence of accidents when traffic-related variables exceed specific thresholds based on traffic flow theory \cite{payne1978freeway}. Similar algorithms were developed in 70s and 80s by applying the standard normal deviate \cite{dudek1974incident}, Bayesian algorithms \cite{levin1978incident}, and time series models \cite{ahmed1982application} to historical traffic data. The need for a quicker response and handling the interdependencies between input features in the presence of multiple inputs has motivated researchers to resort to more complex frameworks. A wide range of studies targeted real-time accident detection using more sophisticated approaches, including statistical techniques \cite{khan1998statistical,zhang2005towards}, image processing \cite{hoose1992incident,zifeng1997macro}, pattern recognition \cite{rong2013urban,zhang2004new}, and artificial intelligence \cite{abdulhai1999enhancing,adeli2000fuzzy,ccetiner2010neural,jin2001classification,lu2012hybrid}. While many of the proposed methods indicate a reasonable performance using image, social media content, and traffic count data, the viability of these methods in practice using imbalanced, heterogeneous, and real-time data remains uncertain. In many transportation applications, machine learning techniques have proven to handle the presence of high dimensional, heterogeneous, and imbalanced data \cite{lee2018comparison,bortnikov2019accident,parsa2019real}. However, a practical model with a reasonably low response time using the data coming from the already installed traffic infrastructure is still being sought for.

Traffic flow data are being continuously recorded for decades now, hence we normally face big data in this context. Advanced learning methods, like representation learning and deep learning, have shown either promising or much needed for solving the big data problems including traffic accident detection \cite{qiu2016survey}. Among these methods, specific architectures of recurrent neural networks gained increasing attention for their ability to exhibit temporal dynamic behavior and to process variable length sequences of inputs \cite{yadav2020accident}. Unlike classifiers such as support vector machines (SVMs) and logistic regression, deep learning does not seek direct functional relationships between the input features and the output classification results. Instead, it attempts to learn in multiple levels, corresponding to different levels of abstraction \cite{deng2014deep}.

One of the most recent studies employing deep representation for road accident detection is performed by Singh et al. using surveillance videos \cite{singh2018deep}. Their proposed framework automatically learns feature representation from the spatiotemporal volumes of raw pixel intensity which outperformes traditional hand-crafted features based approaches. Another study by Zhang et al. \cite{zhang2018deep} employed deep belief network (DBN) and long short-term memory (LSTM) in detecting the traffic accident from social media data. Their results show that their method outperforms support vector machines (SVMs) and supervised Latent Dirichlet allocation (sLDA) tested on 3 million tweet contents in two metropolitan areas in a 1-year time window. Hunag et al. \cite{huang2020intelligent} used an integrated two-stream convolutional networks architecture to detect near-accident events of road users in traffic video data. Their real-time deep-learning-based method comprises a spatial stream network and a temporal stream network, and shows competitive qualitative and quantitative performance at high frame rates. Ghosh et al. \cite{8816881} also used videos from a CCTV camera installed on a highway to train a  Convolutional Neural Networks (CNN) based model to classify individual frames to crash or non-crash events.

Traffic data collecting technologies are evaluated mainly based on reliability, performance under various environmental conditions, the accuracy of obtained data from detectors, and performance in real time \cite{nikolaev2017analysis}. Video image processing has the advantage of providing images of movement in real-time and no traffic interruption for installation and repair. However, video contents, just like social media contents, are expensive to access and process and the performance of models working based on them in many cases have shown inconsistent under various environmental and traffic circumstances. The usage of smartphones and GPS-based applications are also popular for their good accuracy and real-time performance, but smart phones have privacy complications, and due to false alarm filter, it may not detect all accidents \cite{amin2012accident}. In-vehicle emerging technologies appearing in, for example, eCall , Onstar, and Ford-Sync \cite{smruthi2020intelligent, lupinska2020car} can automatically make an emergency call after an accident. These services require cross-border interoperability, because vehicles cross national borders and because of the global and regional nature of the automotive market. Also, they must be physically robust to survive an accident, and since the expected lifespan of vehicles is longer than phones', backward compatibility with previous generations of cellular networks is required \cite{oorni2017vehicle}. In this study we use loop detectors as they are installed in abundance on roadways and are not sensitive to environmental and traffic factors such as rain, snow, and traffic jams. Despite collecting noisy data and lack of high spatial coverage, loop detectors, their installation, and their maintenance are relatively low cost and provide a large knowledgebase with relatively good performance \cite{nikolaev2017analysis}, which motivate us to find efficient approaches that can work based on loop detector data.

There are other challenges when dealing with accident data. Road accident datasets are highly imbalanced which yields biases down the road when the classification model is being trained. The number of non-accident samples is much larger than the number of accident cases in real accident datasets. The proportion of accident and non-accident records in the dataset needs to be meticulously assigned \cite{katrakazas2016real}, or proper oversampling and undersampling methods should be considered to deal with imbalanced data \cite{ozbayoglu2016real,mujalli2016bayes}. Another powerful method, synthetic minority over-sampling technique (SMOTE) \cite{chawla2002smote}, has shown to be efficient when only a few samples of one class are available, which is the case in accident datasets. Synthesizing minority class samples results in larger and less specific decision regions which resolves the overfitting issue of oversampling and the datapoint loss issue of undersampling. Generally, it has been observed that SMOTE is more robust, especially when dealing with noisy, large, and sparse datasets \cite{kaur2018comparing,vanhoeyveld2018imbalanced}, yet no satisfying feature representation method has been proposed in combination with these resampling techniques to process traffic count sensor data. Deep representation of traffic flow data incorporating networks, like LSTM or CNN, enables accident detection models to learn the spatio-temporal patterns in data and their dependencies to accident events \cite{wang2020deep}. A recent successful application of deep representation of data using the LSTM network is for short-term traffic forecast by Zhao et al. \cite{zhao2017lstm}.  Finding the proper combination of deep representation and resampling methods along with the proper network's structure and settings is a crucial task to overcome these challenges. We have investigated and compared these combinations and are proposing the one with the best outcome in section~\ref{S:4}.

\section{Data Preparation}
\label{S:3}

The data for this study is provided by the Minnesota Department of Transportation (MnDOT) which includes the traffic count data and the accident records data \cite{MnDoT} for a duration of 10 years from 2009 to 2019.

\subsection{Traffic Count Data}
\label{S:31}

The loop detector data are continuously collected by the regional transportation management center (RTMC), a division of MnDOT, at a 30-second interval from over 4,500 loop detectors located around the Twin Cities Metro freeways, seven days a week and all year round. All detectors produce vehicle volume data; however, some automatic traffic recorder stations collect additional types of data depending upon their equipment and sensors. In this study, we use 6 features extracted from MnDOT detector tools, namely: capacity, density, flow, headway, occupancy, and speed described as follows:

\begin{itemize}
    \item [--] Occupancy is the percentage of time a detector's field is occupied by a vehicle.
    \item [--]	Flow is the number of vehicles that pass through a detector per hour $(Volume \times Samples \; per \; Hour)$.
    \item [--] Headway is the number of seconds between each vehicle $(Seconds \; per \; Hour$ / $ Flow)$
    \item [--] Density is the number of vehicles per mile
    \item [--] Speed is the average speed of the vehicles that pass in a sampling period $(Flow / Density)$
    \item [--] Capacity is the amount of flow that can be added (or subtracted) to a roadway before reaching high congestion (or low traffic)
\end{itemize}

\begin{figure}[t]
\centering\includegraphics[width=1\linewidth]{Figures/example-site.png}
\caption{A bidirectional traffic site containing 16 sensors.}
\label{fig:figure1}
\end{figure}

We perform our experiments on the sections of freeways relatively far from ramps. More complex freeway structures and roads with intersections are left for future work. Figure~\ref{fig:figure1} shows an example site used for collecting detector data. This particular site is equipped with 16 sensors, each of which provides the features of interest for the lane that it is installed in. These sensors aggregate data in a preset interval of 30 seconds, but the data can be extracted in intervals of 0.5, 1, 1.5, 2, 2.5, 3, 4, and 5 minutes. The distances between detectors along the direction of the traffic flow are not the same. However, the average distance in the experiment sites is approximately 0.8 mile which is low enough to capture the fluctuations in the traffic flow by at least one sensor in the proximity not long after occurrence of an accident. The severity of an accident is also a factor that can affect the time to detection of the accident. We will discuss the time to detection in the results section and also the type and configuration  of the accidents that are difficult to detect by the proposed systems.

\subsection{Road Accident Data}
\label{S:32}

The accident records for the experiment sites were used to test the performance of the proposed method in this study. This accident dataset has the records of all accidents in the experiment sites in the past 10 years. The records include the date and time stamps of the accidents which are used to evaluate the accuracy of accident detection. A quick analysis of the accident time stamps in the dataset revealed that not all of them are accurate. Our understanding is that the accident time stamps are recorded based on the evidence found in the crash site or reported by witnesses. Sometimes, there are not enough evidence or witnesses available to capture an accurate accident time stamp. A comparative analysis of the accident data against the sensors data showed that the time stamps can have a shift of tens of minutes in some instances. Unfortunately, this inaccuracy can affect the evaluation of accident detection model, especially when minimizing the time to detection criterion is of interest. In order to decrease the effect of inaccurate time stamps, we added a degree of flexibility (DoF) parameter that helps to better reflect on the correctly identified accidents and the false alarms. We explain about how we adopted DoF in the methodology chapter in more details.

\section{Methodology}
\label{S:4}

Traffic accident detection is a binary classification problem in which there are two classes: crash and non-crash. There are two remarkable points about traffic accidents. First, traffic accidents are naturally rare events such that the datasets are inevitably imbalanced. The main problem of training a network with imbalanced dataset is that the network cannot provide optimal results for the minor class since it does not learn sufficient amounts of patterns in the minor class. To deal with this problem, we increase the weight of the samples in the minor class when training the network, and resample the dataset to generate more instances of the minor class.

Secondly, when a crash occurs, the density and speed of cars are affected in both directions of the crash site \cite{ golob2004method}. The patterns that are generated for the speed and density are sometimes modeled by simple distributions, e.g. normal or lognormal distribution, and sometimes by more complex models depending on the importance of the details in the simulation \cite{mehrannia2018dempster}. If there are traffic count sensors installed in the region where an accident has happened, these sensors would be able to capture the patterns if their sampling rate are high enough. Figure~\ref{fig:pattern} shows the speed, density, occupancy, and flow patterns captured by 8 adjacent sensors after occurrence of a crash (Figure~\ref{fig:pattern_accident}) and a rush-hour traffic jam (Figure~\ref{fig:pattern_trafficjam}) on a freeway.

 \begin{figure}[t]
 \begin{subfigure}{.5\textwidth}
    \centering
    \includegraphics[width=0.8\linewidth]{Figures/pattern_acc.png}
    \caption{Crash}
    \label{fig:pattern_accident}
\end{subfigure}%

\begin{subfigure}{.5\textwidth}
    \centering
    \includegraphics[width=0.8\linewidth]{Figures/pattern_jam.png}
    \caption{Traffic jam}
    \label{fig:pattern_trafficjam}
\end{subfigure}%

\caption{Patterns of sensor reading features before and after a crash and a rush-hour traffic jam that happened at 4:00 pm. Each color represents a particular sensor in the vicinity of the crash location.}
\label{fig:pattern}
\end{figure}

The temporal and spatial patterns of a crash can be clearly seen in Figure~\ref{fig:pattern_accident}. This figure illustrates different features of the sensors in the crash site. The sensors, represented by different colors, are spatially spaced, and their features shows that the average speed of vehicles in the crash area has been reduced drastically at the crash time. Furthermore, the traffic accident affects the sensors data in a certain period of time depending on the nature of the accident and the crash site characteristics. Traffic data are spatio-temporal; meaning that traffic parameters such as flow vary both in time and space. Therefore, it would be reasonable to consider time series of multiple adjacent sensors data to detect the occurrence of a traffic accident. There is one major difference between a traffic jam and an accident and that is their spatial patterns. In a traffic jam, caused by the rush hours of the day for example, all lanes of the highway would be crowded, hence, the traffic parameters of all loop detectors would fluctuate simultaneously, as shown in Figure~\ref{fig:pattern_trafficjam}. In contrast, when an accident occurs, it has exclusive effect on each of the loop detectors' readings. For example, in Figure~\ref{fig:pattern_accident}, occupancy, density, and speed measured by two of the loop detectors change significantly after the crash while they change only slightly in the other two.

 \begin{figure*}[!t]
\centering\includegraphics[width=\textwidth]{Figures/general_overview2.PNG}
\caption{An overview of the main steps for automatic detection of freeway accidents. }
\label{fig:genov}
\end{figure*}

Figure~\ref{fig:genov} shows an overview of the main steps to detect crashes by looking at 16 adjacent sensors data (8 in each direction) in consecutive time intervals. This classification problem can be divided into two phases: encoding phase and classification phase. In the encoding phase, a neural network learns which features are most significant for classification phase. This phase also eliminates correlations between the features in order to reduce the dimension of data and obtain a more efficient feature space. By creating a feature space with better separability, this step is crucial in applications with complex data. We will show that the proposed network is able to automatically learn the efficient features by comparing the feature spaces before and after encoding. Since LSTM has high capability in handling time series data, we incorporate it in our study for the encoding phase. It is evident from Figure~\ref{fig:pattern} that accidents create spatio-temporal patterns in traffic parameters such that loop detectors data are affected both through time and space. Furthermore, these patterns can be seen as sequences i.e. when accidents occur, flow increases over time then remains constant and then reduces over time. Any network has to learn these patterns in order to detect the accidents. LSTMs are capable of learning such dependencies by design and are used broadly for processing sequences.

Other memory neural networks such as simple recurrent neural networks (RNNs) are not as suitable for accident detection since they do not have long-term dependencies to previous samples. This is due to the gradient vanishing problem that appears in back propagation through time. In our study, we also encode the feature space using Convolutional Neural Network (CNN) and other conventional methods to compare the proposed network's capability in encoding feature space with theirs. A brief explanation of the resampling and encoding methods are provided in the following sections.

In the classification phase, the network learns to correctly detect a traffic accident using the features provided by the encoding phase. This phase can be implemented using various artificial neural networks, such as multi-layer perceptron (MLP), probabilistic neural network (PNN), and radial basis function (RBF) network. In our study, we use the MLP for the classification task for several reasons; Unlike RBF networks, MLPs are easier to train and can also classify non-convex feature spaces. Also, unlike PNNs which are feed forward neural networks, MLPs are trained using backpropagation algorithm, hence can be used in complex problems dealing with high dimensional data. Our proposed algorithm for accident detection is summarized in Algorithm~\ref{Alg:accdet}.

\begin{algorithm}[h]
\DontPrintSemicolon
\KwIn{Sensors raw data (Flow, Density, Speed, Capacity, headway, Occupancy)}
\KwOut{Class of sample (crash or non-crash)}
\;
\nl  Discarding irrelevant crashes and cleansing data, \;
\nl Data partitioning and splitting test and train data, \;
\nl Normalizing dataset using Z-normalization (zero mean and unit variance), \;
\nl Resampling training data, \;
\nl Time series generation, \;
\nl Encoding feature space using LSTM\;
\nl Classifying the encoded feature space and determine crash probability, \;
\nl Compare the crash probability with a threshold to decide whether an accident has occurred or not.

 \caption{Algorithm of accident detection}
 \label{Alg:accdet}
\end{algorithm}

First, we discard the crashes that have not occurred on the target highway, e.g., crashes on the roads that are in close vicinity or intersect the highway. This step is needed since the crash dataset used for training is populated based on GPS location. In practice, for the run of the trained model, this step becomes unnecessary since the input data is pulled from the loop detector data only. We also cleanse the data of malfunctioning loop detectors according to the acceptable ranges stated in the documents of MnDOT Regional Traffic Management Center (RTMC). The dataset contains accidents with different severity levels which can create different spatio-temporal patterns in loop detectors data. To ensure that the proposed model can achieve the desired performance in detecting all types of accidents, we use k-fold cross validation to split the dataset. By doing so, all types of accidents will be contained in test data. After normalizing data, the data is resampled to balance the number of samples in each class. In the sixth step, we encode the spatio-temporal features using LSTM to obtain a more efficient feature space. This step is crucial because it reduces the dimension of data and increases the class separability which contributes to higher detection rate. Finally, we determine the crash probability by using a sigmoid function in the output layer of the network. If the crash probability of a sample is higher than 0.5, it is decided that an accident has occurred, otherwise the sample is labeled non-crash.

\subsection{Resampling Methods}
\label{SS:42}

Resampling methods are in one of the categories of under-sampling methods, over-sampling methods, or a combination of both. Under-sampling methods balance the dataset by reducing the size of major class's samples while over-sampling methods balance the dataset by increasing the size of minor class's samples \cite{yuan2019real}. Some of these methods are explained below.

\begin{itemize}
    \item[-] Edited nearest neighbor (ENN): In this method, the points whose class label is different from majority of its $k$ nearest neighbors are removed \cite{more2016survey}.

    \item[-] Tomek Link Removal: In this method, the points that are in vicinity of each other and belong to different classes which is called a Tomek link are removed from dataset. As a substitute, one can only remove points belonging to the major class from Tomek link \cite{more2016survey}.

    \item[-] Synthetic Minority Oversampling Technique (SMOTE): In this method, first, $k$ nearest neighbors of point $p$ which belong to the minor class, are computed. To generate a synthetic point for each point $p$, a minor class point $p_z$  is randomly chosen from $k$ nearest neighbors of point $p$ and then the new synthetic point is calculated as follows \cite{chawla2002smote}:

    \begin{equation}
    \label{eq:e1}
    x_{new} = p + \rho(p_z-p)
    \end{equation}
    where $\rho$ is a random number in the range [0,1]. There are some variations of SMOTE such as Borderline SMOTE, SVM-SMOTE, and Adaptive Synthetic Sampling (ADASYN) which are discussed in details in \cite{more2016survey}, \cite{nguyen2011borderline}, and \cite{he2008adasyn}.

    \item[-] Combinational Methods:  These methods combine different resampling methods to improve efficiency in certain scenarios. Examples of  Combinational Methods are SMOTE-ENN  \cite{more2016survey} and SMOTE-Tomek link removal \cite{more2016survey}.

\end{itemize}

\subsection{Deep Representation of Features}
\label{SS:43}

In this study, we use LSTM to map the high dimensional features of loop detector data to a new optimized feature space with reduced features. LSTM consists of two main units, namely, gates and cell state. Input and output gates grant control over inputs and outputs of the cell, protecting it from any irrelevant perturbations \cite{hochreiter1997long}. On the other hand, cell state allows constant error flow through units to avoid gradient vanishing problem. Cell state and gates, together, form an LSTM cell as shown in Figure~\ref{fig:LSTM}.

 \begin{figure}[t]
\centering\includegraphics[width=0.95\linewidth]{Figures/LSTM.png}
\caption{LSTM cell}
\label{fig:LSTM}
\end{figure}

There are three main operations in gates of LSTM cells. In the forget and the input gates, it is decided that what information should be deleted or stored in the cell state, respectively. Finally, in the output gate, it is decided that what the memory cell is going to output. These operations are summarized in the equations below \cite{hochreiter1997long}:

\begin{equation}
\label{eq:e8}
i_t = \sigma(W_{ix} \times x_t + W_{is} \times s_{t-1} + b_i )
\end{equation}

\begin{equation}
\label{eq:e9}
f_t = \sigma(W_{fx} \times x_t + W_{fs} \times s_{t-1} + b_f )
\end{equation}

\begin{equation}
\label{eq:e10}
c_t = f_t \odot c_{t-1} + i_t \odot \tanh (W_{cx} \times x_t + W_{cs} \times s_{t-1} + b_c)
\end{equation}

\begin{equation}
\label{eq:e11}
o_t = \sigma(W_{ox} \times x_t + W_{os} \times s_{t-1} + W_{oc} \times c_t + b_o )
\end{equation}

\begin{equation}
\label{eq:e12}
s_t = o_t \odot \tanh{c_t}
\end{equation}
where $i_t$, $f_t$, $o_t$, $c_t$, $s_t$ denote for input gate, forget gate, output gate, memory cell, and internal state at time step $t$, respectively. $\sigma$ is the logistic sigmoid function and $\odot$ indicates Hadamard product.

\subsection{Network Architecture}
\label{SS:44}

Our proposed network includes an encoder and a classifier. As explained previously, we use MLP as the classifier and LSTM as encoder. It should be noted that the proposed network is similar to the well-known LSTM and CNN, however, we divide our network into encoder and classifier part to emphasize that the network performs two different tasks: feature learning and classification. An illustration of proposed network is given in Figure~\ref{fig:arch}.

 \begin{figure}[t]
\centering\includegraphics[width=1\linewidth]{Figures/network.png}
\caption{proposed network architecture}
\label{fig:arch}
\end{figure}

Figure~\ref{fig:arch} shows the layers used in our proposed network with their corresponding size and activation functions. There are three types of layers in the network: LSTM layer, dropout layers, and fully connected layers. The main motivation for using dropout layers is to prevent model from overfitting. There are few reasons for choosing Leaky ReLU as the activation function of fully connected layers. First, ReLU and Leaky ReLU does not bound the outputs of the layers, hence, they can reduce the effect of gradient vanishing problem. Secondly, the dataset is normalized using the Z-normalization method and it contains negative values. Consequently, when using ReLU, some neurons may be inactive no matter what input is being supplied, which prevents the gradient flow. Therefore, it is better to use Leaky ReLU instead of ReLU as the activation function. As shown in Figure~\ref{fig:arch}, first, input feature space is encoded using LSTM and then encoded features are passed on to the fully connected MLP layers. It is worthwhile noting that since our objective is to detect crashes and not predicting them, the time series data should be passed along to the encoder in reverse chronological order, e.g., first input to the encoder would be the last time slice. In other words, we detect accidents after they occur, hence, we present the data to the network backwards in time. The classifier has two hidden layers, because MLP with two hidden layers is more effective than MLP with one hidden layer in classifying non-convex feature spaces. The dimension of each layer in the classifier is greater than the one in the previous layer except for the output layer which only has one neuron, because there are only two classes. The size of each layer has been fine-tuned empirically, however, according to the Cover's theorem, linear separability of the feature space increases relatively to the space dimension.

\subsection{Training the Model}
\label{SS:45}

The proposed network is trained using backpropagation algorithm. To further deal with the imbalanced dataset and prevent overfitting, we use weighted binary cross-entropy loss function as expressed in Equation~\ref{eq:e13}:

\begin{equation}
\label{eq:e13}
L(y,\hat{y}) = -\frac{1}{N} \sum_{i=1}^{N} (w_1*y_i*\log (\hat{y_i})+w_2*(1-y_i)*\log(1-\hat{y_i}))
\end{equation}
where $y_i$ is the true label of the $i^{th}$ sample, $\hat{y_i}$ is the predicted probability of crash, N is number of samples, and $w_1$ and $w_2$ are class weights. By assigning high weight to the minor class which includes crash samples, the network puts more effort to correctly classify the samples of this class. The penalty of incorrectly predicting the label of these samples would be high when training the network using backpropagation. Consequently, even when network can detect non-crash samples correctly, the loss function's value will not diminish which is desirable for training the network.

\subsection{Model Evaluation}
\label{SS:46}

In the crash detection problem, it is possible to achieve high accuracy while true positive rate remains low due to imbalanced classes. However, it is essential to achieve high true positive and low false positive rates. Therefore, we will mainly focus on sensitivity and specificity when evaluating the models. As discussed in Section \ref{S:3}, in order to compensate for any errors in recording of crash events, we consider a Degree of Flexibility (DoF) in calculation of confusion matrix. By taking DoF into account, True Positives (TP), False Negatives (FN), True Negatives (TN), and False Positives (FP) are calculated as follows:

\scriptsize
\
\begin{equation}
\label{eq:e14}
\hspace{-0.85em}
  \begin{cases}
      TP_j=TP_{j-1}+1 & \text{if \{$t(j)=1$ and $P > 0$\}}\\
      FN_j=FN_{j-1}+1 & \text{if \{$t(j)=1$ and $P = 0$\}}\\
      TN_j=TN_{j-1}+1 & \text{if \{$t(j)=0$ and $p(j)=0$ or $t(j)=1$ and $T > 0$\}}\\
      FP_j=FP_{j-1}+1 & \text{if \{$t(j)=0$ and $p(j)=1$ and $T = 0$\}}
    \end{cases}
\end{equation}
\normalsize
where $$P=\sum\limits_{n=j-DoF}^{j+DoF} p(n),$$ $$T=\sum\limits_{n=j-DoF}^{j+DoF} t(n),$$ and $t(j)$ and $p(j)$ denote for true label and predicted label of $j^{th}$ sample, respectively.
Sensitivity determines network's ability to correctly predict crash samples:

\begin{equation}
\label{eq:e16}
\text{Sensitivity} = \frac{TP}{TP+FN}
\end{equation}

Specificity determines network's ability to correctly predict non-crash samples:

\begin{equation}
\label{eq:e17}
\text{Specificity} = \frac{TN}{TN+FP}
\end{equation}

\section{Experimental Results and Discussion}
\label{S:5}

In this section, we evaluate the LSTM-based accident detection method along with other competing methods and report their performance from various aspects. For the encoder implementation phase, the networks are trained using different optimization algorithms, resampling methods, time series length, and data aggregation interval. Furthermore, an analysis of the LSTM-based network's capability in encoding feature space and a comparison between detection rate of this network and other classifiers including CNN, PNN, AdaBoost, and GradientBoost is provided in this section. A 5-fold cross validation procedure is applied to the data gathered from the sensors of the site shown in Figure~\ref{fig:figure1}, such that the data is divided into 5 partitions randomly, and each time, 4 of the partitions are used for training and the other for testing. In the testing process, a confusion matrix is generated to show the number of correctly identified accidents and false alarms. The average accuracy, sensitivity, and specificity of the 5 folds are reported to have unbiased evaluation criteria.

\subsection{Model Performance with Different Optimization Algorithms and Resampling Methods}
\label{SS:51}

 \begin{figure}[t]
\centering\includegraphics[width=0.95\linewidth]{Figures/resampling.png}
\caption{training data before and after resampling}
\label{fig:resam}
\end{figure}

In this section, we only focus on the performance of resampling methods and optimization algorithms. The impact of other significant factors such as time series length are restricted by tuning them in their best possible arrangement. The first step after preprocessing the accident dataset is the resampling of the training data to deal with its imbalanced nature. To do so, we should determine which resampling method is more suitable to the case study. To see the impact of resampling on the training data, a 2D visualization of the feature space before and after the resampling is represented in Figure~\ref{fig:resam} using principal component analysis (PCA).

 \begin{figure}[t]
\centering\includegraphics[width=0.9\linewidth]{Figures/resam-results2.png}

\centering\includegraphics[width=0.95\linewidth]{Figures/CNN-resamp.PNG}
\caption{results of the LSTM- and CNN-based models trained using different resampling Techniques}
\label{fig:resamres}
\end{figure}

As explained in Section~\ref{S:4}, resampling methods balance the dataset by increasing the samples of minor class and/or reducing the samples of major class. This is why some crash samples have been generated such as the point with coordinates (-10, -5) and some non-crash samples have disappeared such as the point with coordinates (20, -20). It should be noted there are also some outliers generated by resampling methods such as the point with coordinates (10, 30) in non-crash samples. It should be determined that which resampling method works best in our problem. To do that, a comparison between the results of models trained with different resampled training data is performed. The results of the top two models (LSTM and CNN) are shown in Figure~\ref{fig:resamres}. It should be noted that resampling methods generate artificial data, hence, they might not be realistic. Nonetheless, they balance the dataset and eliminate the bias toward major class. In order to provide reliable results, we have left the test data untouched in all experiments.

As illustrated in Figure~\ref{fig:resamres}, ADASYN and SMOTE have better sensitivity and specificity compared to the other methods. Although, ADASYN outperforms all other methods in terms of sensitivity, its specificity is too low which increases the number of false alarms drastically. SMOTE, however, has a good balance of accuracy, sensitivity, and specificity. Therefore, SMOTE will be used to resample the training data in the next parts. It is observed that high accuracy does not guarantee high sensitivity due to low number of crash samples. Sensitivity is a good metric for measuring network's ability in classifying accidents, however, it cannot give insights about false alarm rate of the network. Therefore, specificity is required to measure network's ability in classifying non-crash samples.  So, the next evaluations will be based on sensitivity and specificity only.

The next step is to decide which optimizer provides the best outcome. To do so, we evaluated the performance of adaptive learning methods and non-adaptive learning methods in our accident detection case study. Stochastic Gradient Descent (SGD) with momentum is used here to represent non-adaptive learning methods. The reason we use momentum is to speed up the learning process and escape from the suboptimal local minima of the loss function. From now on, we will refer to SGD with momentum simply as SGD. Adaptive Moment Estimation (ADAM) \cite{kingma2014adam} performs well in practice and compares favorably to other adaptive learning methods such as Adagrad \cite{duchi2011adaptive}, Adadelta \cite{zeiler2012adadelta}, and RMSprop, hence, it is chosen to represent the adaptive learning methods. The proposed network is trained using ADAM and SGD optimizers with a learning rate of 0.00001, momentum of 0.8, batch size of 32, and class weight of 1:100 for 70 epochs. The values of these hyperparameters have been tuned empirically and will be used in all proceeding experiments.

\begin{table}[t]
\centering
\caption{Comparison of optimization algorithms for LSTM}
\vspace{-0.5\baselineskip}
\begin{center}
\begin{tabular}{|c| c| c| c| c| c| c| c|}
\hline
 & Accuracy & Sensitivity & Specificity & Training Time (s) \\
 \hline

 SGD  & 0.61  & 0.63  & 0.61 & 569 \\
 \hline
 ADAM  & 0.73  & 0.37  & 0.73 & 581 \\
\hline

\end{tabular}
\end{center}
\label{table:1}
\end{table}

The low number of crash samples can increase the possibility of overfitting in the models. According to Table~\ref{table:1}, sensitivity of the LSTM-based model reduces drastically when using ADAM optimizer. This happens because SGD has better generalization capability compared to ADAM. SGD is also computationally more efficient than ADAM based on the training times reported in Table~\ref{table:1}. In the next parts, we use SGD as the selected optimizer and report the results of evaluation for the other criteria.

\subsection{Model Performance with Different Data Aggregation Intervals and Time Series Length}
\label{SS:52}

 \begin{figure*}[!t]
\centering\includegraphics[width=0.6\linewidth]{Figures/results_LSTM.png}
\caption{LSTM performance for various TSL, DAI, and DoF}
\label{fig:result-LSTM}
\end{figure*}

Time to detection (TTD) is an important aspect of an accident detection model. TTD is the time needed by the model to be able to detect an accident after it occurs. A small TTD value is essential along with high sensitivity and specificity in accident detection models. The key parameters affecting TTD are data aggregation interval (DAI), time series length (TSL), and degree of flexibility (DoF). DAI is a tunable parameter in the data extraction tool. It has an impact on the smoothness of fluctuations in traffic data and together with TSL, they determine how many temporal samples are required to detect an accident. On the other hand, DoF accounts for model's delay in detecting the time of accident which can be imposed by inaccurate crash records in the dataset.

According to Figure~\ref{fig:result-LSTM}, specificity and sensitivity of the LSTM-based model increase with DoF. This is trivial since more delayed detections are accepted as DoF increases. Both models have achieved high specificity, so the first priority in determining the optimal TSL and DAI values is sensitivity. Having a look at the plots with DoFs of same value (i.e. DoF =1 or 6, or 12), it can be concluded that models trained with higher DAI have higher sensitivity. Therefore, there must be a trade-off between TTD and sensitivity of the models. Furthermore, sensitivity values are non-monotonic decreasing functions of TSL, hence the optimal time series length would be 6. Therefore, we will compare models Receiver Operating Characteristic (ROC) at TSL=6. Maximum sensitivity and specificity achieved by the LSTM model using optimal parameters are 0.8 and 0.7, respectively.

\subsection{Capability in Encoding Feature Space}
\label{SS:53}

As explained before, the main motivation for using LSTM network in our study was to encode the spatio-temporal features and obtain a more efficient feature space. In this section, we are going to demonstrate this network's capability in encoding the feature space by comparing the separability of classes in 3 feature spaces: original feature space (before encoding), encoded feature space with LSTM, and encoded feature space with CNN. The reason we choose CNN for comparison here is that CNN can also encode the spatio-temporal features with 1D convolutional layers and can handle time series data.

 \begin{figure}[t]
\centering\includegraphics[width=0.95\linewidth]{Figures/feature_space.png}
\caption{Distributions of crash and non-crash samples for 3 random features. First row, second row, and third row relate to the features of original feature space, encoded feature space with CNN, and encoded feature space with LSTM, respectively.}
\label{fig:fspace}
\end{figure}

To determine class separability in each feature space, we measure the difference between distributions of crash and non-crash samples using well-known statistical distances such as Wasserstein metric, Bhattacharya distance, and Jensen-Shannon divergence (JS divergence).

\begin{table*}[!t]
\centering
\caption{Metrics for class separability in each feature space}
\begin{center}
\begin{tabular}{|l|c| c| c|}
\hline
  & Wasserstein distance  & Bhattacharya distance  & JS divergence  \\
  &($\times 10^{-5}$) &($\times 10^{-5}$) &($\times 10^{-5}$) \\
 \hline
Original feature space & 26 & 91 & 165\\
\hline
Encoded feature space with CNN  & 184 & 761 & 609\\
\hline
Encoded feature space with LSTM  & \textbf{409} & \textbf{1445} & \textbf{707} \\
\hline
\end{tabular}
\end{center}
\label{table:metrics}
\end{table*}

To obtain the distributions, we divide the range of values into many equal-width bins. Then we calculate the number of samples in each bin whose values are contained in that bin. Finally, the number of samples in each bin is divided by the total number of samples and the bin width so that the area under the histogram integrates to 1. In each feature space, the statistical distance between distributions of crash and non-crash samples for each feature is calculated and the average of distances is reported. The results are shown in Table~\ref{table:metrics}. Distributions of crash and non-crash samples for 3 random features of each feature space are illustrated in Figure~\ref{fig:fspace}. According to Table~\ref{table:metrics} and Figure~\ref{fig:fspace}, it is evident that distributions have greater difference in the encoded feature space with LSTM. Furthermore, since the distance between distributions has increased, it can be inferred that the correlation between features has also been reduced automatically. For example, the original feature space contained colinear features such as speed, flow, and density, whose correlations are reduced after encoding.

According to Figure~\ref{fig:figure1}, data of 16 sensors is used in this study where each sensor provides 6 features. Furthermore, the optimal TSL value was determined to be 6 in the previous section. Therefore, each sample will have shape of (6,96) containing spatio-temporal features. This means that the dimension of original features space is 576. On the other hand, as shown in Figure~\ref{fig:arch}, the encoder outputs a feature space with dimension of 100. It can be concluded that the encoder not only increases class separability in feature space, but also reduces the dimension of data.

\subsection{Comparison with Other Artificial Intelligence (AI) Methods}
\label{SS:54}

 \begin{figure}[t]
\centering\includegraphics[width=1\linewidth]{Figures/ROC-newnew.png}
\caption{Comparison of ROC curves for different encoders.}
\label{fig:ROC}
\end{figure}

\begin{table*}[!t]
\centering
\caption{Performance of encoders for various DAI}
\vspace{-1\baselineskip}
\begin{center}
\begin{tabular}{l l l l l l l l l l l}
\hline
 & \multicolumn{3}{c}{6 minutes after} & \multicolumn{3}{c}{18 minutes after} & \multicolumn{3}{c}{30 minutes after} \\
 \cline{2-10}
 & FPR & TPR & AUC & FPR & TPR & AUC & FPR & TPR & AUC \\
 \hline
 LSTM & 0.115 & 0.48 & \textbf{0.183} & 0.25 & \textbf{0.705} & \textbf{0.228} & 0.325 & \textbf{0.737} & 0.206\\
 CNN & 0.038 & 0.291 & 0.127 & \textbf{0.085} & 0.505 & 0.21 & 0.177 & 0.68 & \textbf{0.252} \\
 RNN & \textbf{0.036} & 0.336 & 0.15 & 0.107 & 0.541 & 0.217 & 0.223 & 0.63 & 0.204\\
 PNN & 0.263 & \textbf{0.525} & 0.130 & 0.184 & 0.554 & 0.185 & \textbf{0.152} & 0.516 & 0.182\\
 AdaBoost & 0.219 & 0.518 & 0.150 & 0.260 & 0.539 & 0.139 & 0.219 & 0.541 & 0.161\\
 GradientBoost & 0.162 & 0.436 & 0.137 & 0.167 & 0.496 & 0.165 & 0.153 & 0.413 & 0.130\\
\hline

\end{tabular}
\end{center}
\label{table:2}
\end{table*}

Some frameworks have reported good performance using other AI methods but on different input data. Parsa et al. \cite{parsa2019real}, for example, have reported a relatively high accuracy using PNN on weather condition, accident, and loop detector data. Therefore, in this section we compare the LSTM-based method with other AI methods that are found promising in other accident detection studies. For a fair comparison, we implemented all the models and tested them on the same site shown in Figure~\ref{fig:figure1} which is a section of a freeway with no ramps or intersections. In theory, existence of intersections or ramps will disrupt the flow and makes the scenarios more complicated. To elaborate, the out-flow of the road does not match its in-flow which causes the counts from sensor readings in different sections to be different. However, we leave these other site scenarios for our future work. For comparing the performance of the LSTM network with other deep learning methods (including CNN, PNN, RNN and ensemble methods including AdaBoost \cite{freund1997decision} and GradientBoost \cite{friedman2001greedy}), the best outcome of each model tested on a variety of sampling methods, optimizers, and network settings is reported. The ROC curves of each method for optimal TSL (TSL=6) and various DAI are shown in Figure~\ref{fig:ROC}. The Area Under Curve (AUC), False Positive Rate (FPR), and True Positive Rate (TPR) of Figure~\ref{fig:ROC} are represented in Table~\ref{table:2}. By comparing the AUC of each method, it can be concluded that LSTM is performing better at lower TTD. Furthermore, TPR of these networks at DAI=1 (6 minutes after detection) is noticeably low. Therefore, in our case study, the reasonable DAI would be 3 which means that optimal results are attained by looking into 18 minutes of loop detector data after an accident happens. It is worthwhile to mention that the results for lower TTDs are still able to detect accident patterns with reasonable accuracy. However, for comparative purposes we report 18 minutes since less false alarms and more true positives are generated at this setting.

\subsection{Generalizability of the Model}
\label{SS:55}

As concluded in the previous part, LSTM performs better at lower TTD and the optimal results were attained at DAI=3 and TSL=6. In this part, we will examine the generalizability of the LSTM encoder by training the network using optimal parameters and data of two different sites with similar characteristics. The intent is to show that a model that is trained on a site with specific characteristics in terms of road type, number of lanes, number of sensors, and distances between the sensors, can detect the accidents on other sites with similar characteristics. To this end, we study two experiments; In the first experiment, we train the network using data of site1 (shown in Figure~\ref{fig:figure1}) and test it using data of another site (site2) in which sensors are placed in a manner similar to site1. Site2 has the same characteristics as site1: Both are sections of a bidirectional freeway with limited ramps and intersection, 2 lanes, and 16 mounted loop detector sensors. The distances between the spots where sensors are mounted are not exactly the same, but yet in the same scale. In the second experiment, we train and test the network using the mix of accumulated data from both sites.

The first experiment evaluates the ability of the model to detect the accident patterns in a site different from the one it is trained on. It is trivial that the change in the distance between sensors will affect the performance of the model. However, we are interested to know how reliable the model can be when it is used to monitor a different site which has the same structure but is slightly different in arrangement of the sensors. The second experiment evaluates the model when exposed to training data that is augmented using the data of the second site described in the first experiment. Augmenting data is one way to increase the population of samples in the class with insufficient samples. However, the sensitivity of the model to the augmented data should be measured.

  \begin{figure}[t]
\centering\includegraphics[width=0.95\linewidth]{Figures/gener.png}
\caption{Generalizability with augmented data and using a second site for test  }
\label{fig:gen}
\end{figure}

 Figure~\ref{fig:gen} compares sensitivity and specificity of the three experiments to test the generalizability of the model. As expected, the sensitivity of the model slightly decreases in the experiments involving the second site. The sensitivity of the model is slightly more than 0.7 before involving the second data. This value is decreased to 0.59 and 0.54 in the first and the second experiments respectively. The specificity, however, increases in the third experiment from 0.75 to 0.86, which means that we can prevent the occurrence of false alarms significantly by using augmented data.

\section{Conclusion}
\label{S:6}

Loop detector sensors, despite their noisy data and lack of high spatial coverage, are mounted on countless roads. Therefore, developing models that can yield good incident detection rate by using these sensors helps governments, to a considerable extent, obtain valuable insights until new technologies start to outnumber loop detectors on roads. Using loop detector data, we illustrated that accidents can impact the traffic flow patterns in the upstream and downstream of the accident site. Capturing these patterns in the traffic flow and associating them to the occurrence of accidents is not an easy classification problem. In this paper, we investigated an LSTM-based deep representation of imbalanced spatio-temporal traffic flow data, obtained from loop detector sensors, to study the degree to which it can increase the incident/no-incident class separability. we compared this LSTM-based deep representation with other popular AI methods like CNN, PNN, AdaBoost, and GradientBoost, and showed that the LSTM-based method increases the class separability in the encoded feature space which resulted in higher detection rate and lower false alarm rate.

A key challenge was the rareness of traffic accident events, making the collision datasets highly imbalanced. We explored different resampling techniques to identify a suitable method for dealing with heterogeneous accident data. We also explored different settings for the LSTM network as the main component of feature encoding and representation learning. The main objective was to minimize the time-to-detection of the accidents from when they happen. The results indicated that an optimal setting for time series length and data aggregation interval are 6 and 3 respectively. We also showed that the LSTM-based framework outperforms other commonly used AI methods for detecting accidents and reduces the detection time to as low as 6 minutes after crash occurrences. It is expected that this framework works better in real-time scenarios since the time stamp of pre-recorded accident data are erratic. We have shown this fact by using a tunable DoF parameter that takes the early and delayed accident flow patterns into account. Also, our experiments testing the generalizability of the model shows that the sensitivity measure decreases slightly when the model is used on a site other that the one it is trained for. Potential future works can be to further decrease the detection time, apply this method to real-time data, improve the generalizability of the model and data augmentation, test the model on parts of the freeway with ramps and intersections, and exploit other methods to map the encoded feature space to crash/non-crash outputs.

\section*{Acknowledgement}
\label{S:Ack}

The loop detector data and accident dataset for this research is provided by Minnesota Department of Transportation, Office of Traffic Engineering.

\ifCLASSOPTIONcaptionsoff
  \newpage
\fi

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


A U.S. federal judge has denied a motion filed by Lance Armstrong's legal team seeking to negate any damages suffered by former team sponsor U.S. Postal Service, meaning the False Claims Act (FCA) lawsuit may now proceed to a jury trial.
The federal whistleblower lawsuit, first filed by former USPS teammate Floyd Landis in 2010 and joined by the U.S. government, on behalf of the USPS after Armstrong's doping admission in 2013, seeks damages on its $32.3 million sponsorship of the team between 2000 and 2004.
Because the False Claims Act, a civil statute, authorizes the United States — and whistleblowers suing on its behalf — to seek civil penalties plus treble damages for a violation of the FCA, Armstrong and former team management Tailwind Sports could be liable for nearly $100 million.
While the total cost of the Postal Service sponsorship from 1995 to 2004 was over $42 million, the ruling noted that the government's claims are based on invoices submitted within the FCA's statute of limitations.
Armstrong's attorneys have long argued that the U.S. Postal Service suffered no damages and received more in value from the sponsorship than the $32.3 million it paid, based on media impressions during his Tour de France reign.
However the government presented an expert declaration indicating that there were some 1.5 billion negative media impressions "connecting Mr. Armstrong's doping scandal to the USPS" between 2010 and 2014.
On Monday, U.S. District Judge Christopher Cooper denied Armstrong's request to dismiss the case with a summary judgment.
"Because the government has offered evidence that Armstrong withheld information about the team's doping and use of (performance-enhancing drugs) and that the anti-doping provisions of the sponsorship agreements were material to USPS's decision to continue the sponsorship and make payments under the agreements, the Court must deny Armstrong's motion for summary judgment on this issue," Cooper wrote.
Saying that both sides "missed the mark" in terms of damages, Cooper said the argument must be decided by a jury at trial.
Armstrong could not be reached for comment.
• The Court recognizes that calculating the government's damages in this case is fraught with considerations of fairness and culpability. As both the government and Landis point out, giving Armstrong "credit" for the benefits he delivered while using PEDs could be viewed as an unjust reward for having successfully concealed his doping for so long. It could also, in theory at least, encourage other government contractors to behave similarly. But by the same token, disregarding any benefits USPS received from the sponsorship could bestow the government with an undeserved windfall. The same could be said of Landis, whose role in this entire affair some would view as less than pure. In the end, the Court will avoid the types of moral and policy judgments that both sides invite, and instead apply the law as it interprets the D.C. Circuit to have instructed.
• The government here will be likewise entitled to present admissible evidence regarding the negative publicity the Postal Service received following the disclosure of Armstrong's PED use, just as Armstrong will be permitted to present admissible evidence of the sponsorship's positive benefits. Should the government prove liability, it will then be up to the jury to weigh the evidence on both sides of the scale and decide whether the government can prove it sustained actual damages and, if so, the corresponding amount. The Court will, therefore, deny Armstrong's motion for summary judgment on the issue of actual damages.
• Armstrong challenges the government's common-law fraud claim on the same grounds as he does its FCA fraud-in-the-inducement theory: That the government cannot prove as a matter of law that USPS relied upon Armstrong's misrepresentations or fraudulent behavior. As previously noted, however, the government has offered evidence raising a genuine dispute as to whether USPS officials relied on the defendants' doping denials when making decisions regarding the sponsorship. See supra Section III.B.2.a. This material dispute alone saves the government's common-law fraud claim at this stage of proceedings. The government does not have to show, as Armstrong argues, that it took additional steps to investigate the doping allegations or otherwise confirm the truth of Armstrong's denials. Therefore, the Court will deny Armstrong summary judgment on the government's common-law fraud claim.
• Armstrong raises multiple challenges to the government's unjust enrichment claim. He first maintains that he could not have been unjustly enriched because he was not a party to the sponsorship agreements and thus not obligated to USPS in the first place. He further contends that he properly earned his salary by training, riding for the team, and appearing at sponsored events. Finally, Armstrong argues that even if he were legally obligated to USPS, the remedy of restitution is unavailable because the government would need first to reimburse Armstrong the value of any benefits it gained from his services, which it cannot do. As a threshold matter, a number of cases in this district have held that a contractual relationship with the defendant is not necessary for a plaintiff to recover on a claim of unjust enrichment. See, e.g., United States v. Honeywell Int'l Inc., 798 F. Supp. 2d 12, 25–26 (D.D.C. 2011) (permitting an unjust enrichment claim against a company that supplied defective materials to a manufacturer when only the manufacturer had a contract with the government).
Armstrong, like the Honeywell supplier, was obligated to USPS because the sponsorship agreements placed restrictions on his behavior and provided the funds for the majority of his salary. Additionally, while USPS does not dispute that Armstrong rode and trained for the team, "[Armstrong] cannot immunize himself from an unjust enrichment claim by performing services that [USPS] never desired in the first place[,]" which includes riding and training while using banned substances. Lastly, as previously discussed, the amount USPS benefitted from the sponsorship agreements remains disputed and does not entitle Armstrong to summary judgment on his unjust enrichment claim.


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Sukiyaki Western Django (International Version)
This is the 98-minute "international" version of Sukiyaki Western Django, versus the near 2-hour one that was the initial release in Japan.
On the whole, my review of the longer version applies here, although I will say that the international cut is, for sure, the superior version. There are a couple of oddball beats that are cut out – one part of a sex scene; an unfortunate "masculinity" joke – and the sequences around those beats are much better for them. Otherwise, most of the snipping is small, and the movie shifts between its sections more fluidly with these changes. This does lessen some of the surreal vibe of the "original," which has its appeal, but definitely makes it a tighter, more enjoyable film as a result. The trade-off beyond that, I'd say, is that some of the relationships aren't afforded the extra glances or lines that gave them some context, which means the emotional impact of the movie is stronger, but limits some of the characters to figureheads moreso than better realized (on screen) roles. But again, as the overall experience ends up being better for it (including the addition of subtitles to assist with some difficult lines), this is the way to go, and it's worth noting that Miike willfully made these cuts after the initial reception of the movie, making the shorter one, in my mind, more akin to a director's cut.


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Romodanov AP, Sosulja JA, Laponogov OA, Trosch RM.
In 35 patients with deep-lying and medial gliomas (25 cases of glioblastoma multiforma, 10 cases of dedifferentiated astrocytoma), stereotactic cryodestructions were performed after preceding biopsies. Diagnosis was made in the usual manner, mainly by means of angiography. Sites of action were the tumour mass proper and the blood supply zones. In some cases, partial removal of the tumour was carried out in the usual way before or after cryodestruction. There are 3 kinds of further development after cryodestruction: regression, slow progression and rapid progression. Progression was found in 14 patients; 9 patients died within 2 weeks after the operation. In their cases, cryodestruction was by far insufficient. Dedifferentiated astrocytomas showed more a regressive behaviour than multiform glioblastomas. The possibility of local chemotherapy was also utilised.


Note: This article may contain spoilers. If you dont care about that, read on.
Norman Osborn is going down. There's no doubt about it. The "Dark Reign" looks to be collapsing and as casualties on both sides pile up in Seige, the dawn of the Heroic Age is just around the corner.
With the Heroic Age coming, some Marvel books will be coming to an end, most notably New Avengers. In the aptly named New Avengers: Finale, scribe Brian Michael Bendis and artist Bryan Hitch will tie up the loose ends of every New Avengers tale that has led to this point. With no cover image even leaking on to the internet yet, no one knows exactly what's to come from this book.
This will all come to a head in April, giving perfect timing for Marvel's latest set of promotional art entitled, "I'm an Avenger" and "I'm a Secret Avenger". With a "May 2010" tag, these pieces feature work from John Romita Jr. and show some of the current stars expected to be present in the Avengers team post-Siege. The most shocking has Hawkeye, not Ronin, pictured which could mean that Clint Barton may finally be returning to the good old purple and blue.
The five "secret" pieces show simple silhouettes of various members of the new Secret Avengers along with different quotes they'd use to describe themselves. Of the Secret Avengers thus far there are many theories going on about who might be on the team.
Take a look for yourself at them all below and let us know who you think may be screaming for their team to "Assemble!" come May.
In what could very well be the next step in comic book storytelling, Marvel entered the motion comics arena with Spider-Woman, based off of the recently launched series written by Brian Michael Bendis with art by Alex Maleev. Available on iTunes, each episode is $1.99 US each or $8.99 US for the entire series with a total runtime of 53:46. Are we witnessing evolution, or merely a fad? And how is the story itself? Pouring through all five episodes, the answers may surprise you.
Starting with the story, Jessica Drew, aka Spider-Woman, finds herself lost in a world she doesn't recognize. No, she's still on Earth, but she was replaced by Veranke the Skrull Queen during the Secret Invasion storyline and has only recently returned to the playing field. Making matters worse, at one point Jessica was an agent of practically anything you can be an agent of in the Marvel Universe – all simultaneously – so it's no wonder she's a little out of sorts these days.
When SWORD Agent Abigail Brand tracks her down and offers an outlet for that pent up frustration, Jessica feels that she may find herself and knock a few heads in the process, so she sets off to the island of Madripoor. As the story unfolds, Jessica winds up in a whirlwind of activity as local authorities, Skrulls, the Thunderbolts and the New Avengers try to insert themselves into her booked-solid dance card. Once the dust settles, it's the decisions going forth that has Jessica's full attention as she continues to find her rightful place in the world.
As we do each week (unless something goes horribly wrong) following is a list of all the great Marvel comics hitting store shelves this week. In case you don't know how this works, new comics come out on Wednesday of each week unless Monday is a holiday or something. Then, they come out on Thursday.
Fortunately, today isn't a holiday so Wednesday it is. So, be sure to head out to your local comic book store two days from now and pick up some cool new comics. Some of the Marvel titles we're interested in this week include the latest issue of Punisher Noir, a new Dark Avengers, the second issue of Spider-Woman and the latest Invincible Iron Man.
Be sure to check out the entire list, and some great art from some of these upcoming titles, after the jump.
For previous installments of Dave's Weekly Comic Book Recommendations, go here.
Slim pickings yet again this week. From DC Comics, we have the entire Blackest Night Tales from the Corps for $7.99, and the final issue of the exceptionally wonderful Wednesday Comics.
From Marvel we have the third issue of the exceptionally fun Immortal Weapons, and the first issue of Spider-Woman #1 comes out. If you haven't been watching the "motion comic," via the iTunes download, then get the series in this more traditional format. Its a lot better. Especially with the former Daredevil team Brian Michael Bendis and Alex Maleev at the helm.
From last week, the softcover of Jonathan Ames and Dean Haspiel's The Alcoholic came out from Vertigo. For those of you who caught the new HBO series Bored to Death and are interested in Jason Schwartzman's character's work in graphic novel form, you ought to pick that up.
And well, to be honest, there isn't anything else that interests me this week so that's about it. As always, we here at The Flickcast care about what you read, so leave us a comment and let us know what you liked/didn't like from this week's comics. For a more complete list of what comes out this week, check out the one at Midtown Comics.
We've been keeping pretty close track of Spider-Woman: Agent of S.W.O.R.D. and Astonishing X-Men: Gifted, the new motion comics (aka MOCO) coming from Marvel. Recently, we brought you the first news of them, a trailer and a sneak peek and now we've got the brand new trailer for episode three of Spider-Woman: Agent of S.W.O.R.D. for you today.
Marvel is proud to unveil new footage from the critically acclaimed Spider-Woman motion comic! The New York Times Best-Selling team of Brian Michael Bendis and Alex Maleev (Halo Uprising) continue Marvel's first ever original motion comic as Jessica Drew shows the world why Spider-Woman is force to be reckoned with!
Spider-Woman Episode 3 hits iTunes on September 16th and no True Believer can afford to miss it! Discover more at www.marvel.com/motioncomics!
Previously, we brought you news of this motion comic (MOCO) and told you that Episode One was one of the fastest selling videos on iTunes. In addition, we were able to bring you a trailer for another new Marvel MOCO yesterday that we're all pretty excited about: Astonishing X-Men: Gifted. Now, courtesy of the folks at Marvel, we've got a preview of Episode Two of Brian Bendis and Alex Maleev's Spider-Woman: Agent of S.W.O.R.D. to share with you this morning.
And, as is often the case with these types of Marvel goodies, they've got something to say about the video.
So far, this Spider-Woman MOCO is pretty awesome and we can't wait to see Episode 2 in its entirety. Until then, be sure to click through and check out this brand new trailer for it right here.
You think motion comics (aka MOCO) are just a fad? Maybe, but tell that to the thousands of people who downloaded Marvel's first MOCO, Spider-Woman, when it became available this week in iTunes. The story, written by Brian Bendis with art by Alex Maleev, was the number one episode on the Television/Animation chart and the number two episode on the chart of all TV episodes.
Want to know more? Fortunately, Marvel has provided us with a bunch more information, via an official press release, and we're going to share it with you right now. If you haven't checked out the MOCO in iTunes, its definitely worth a look.
Marvel is pleased to announce that the first episode of Spider-Woman Motion Comic has conquered iTunes, debuting as the #1 episode on the Television-Animation sales chart and as the #2 episode on the Top Television Episodes sales chart. The New York Times Best-Selling team of Brian Michael Bendis and Alex Maleev (Halo Uprising) present Marvel's first ever original motion comic as Spider-Woman embarks on a new mission that'll change the Marvel Universe forever!
Progress marches on and apparently its not enough to read good comics, now they have to move too. Oh well, can't stop progress I guess. Besides, as much as I might hate to say it as a fan of reading, this new Spider-Woman motion comic (aka MOCO) does look pretty darn cool. Don't understand what I'm talking about?
Well, fortunately, we've got all the info you could possibly need about this new offering from the folks at Marvel, via an official press release. Of course, we've also got some awesome screen shots of the MOCO after the jump, so be sure and check those out!
If the pages of Marvel Comics could move, what would they look like? Wonder no more! Marvel Comics announced the launch of its first ever original motion comic, Spider-Woman. The legendary, Eisner-award winning Brian Michael Bendis wrote the story of Spider-Woman, with art by Eisner Award winning artist Alex Maleev (Daredevil and Stephen King's N. Mobisodes). The first episode of Spider-Woman will be available on iTunes beginning August 19, 2009, with additional episodes released every two weeks.


Key Words: Sorghum bicolor, Anemia, Immature cell.
Int. J. Biosci. 8(4), 62-71, April 2016.
Sorghum bicolor is a medicinal plant used in Benin to treat anemia. This study aimed to evaluate its therapeutic efficacy in anemia treatment. An aqueous extraction of Sorghum bicolor leaves was carried out and analyzed. Five groups of five Wistar rats each were formed. The rats of four groups were rendered anemic by injection of phenylhydrazine (hemolysis) in the first two days D0 and D1. From the second to the fifteenth day (D2 to D15), anemic groups were gavaged either by the extract of Sorghum bicolor at 200 or 300 mg/kg body weight/day, or by vitafer, a reference drug against anemia. The last anemic group was not treated. The group of non-anemic rats served as a control. Blood samples were collected for all rats on days D0, D2, D7, D10 and D15 to assess blood count and osmotic resistance of red blood cells. The phytochemical analysis revealed the presence of catechol tannins, flavonoids, leucoanthocyanes, steroids, cardenolides, reducing compounds and coumarins. The extract like the vitafer corrected anemia in two weeks by increased stimulation of hemoglobin synthesis, production and the early release of immature red blood cells and its effect was dose dependent. Its action was quite specific and did not affect platelet lineage. Sorghum bicolor has an excellent therapeutic efficacy and may be considered for transformation into improved traditional medicines (ITM) after study of its biological tolerance and appropriate clinical trials.


Coffee as mere caffeine conveyance? That's so 1992. In Chicago and other big cities, people's interest in their cup of joe actually tasting good started with, yes, that big green monster called Starbucks in the early '90s. But about 10 years later the second wave of coffee innovation was upon us, and everybody (Intelligentsia and Metropolis, specifically) started roasting their own beans in small, carefully sourced batches. We were hooked.
Chicago is now deep in the third wave of coffee culture typified by new brewing techniques, such as the time-consuming pour-over method, that bring out nuanced flavors. Indie coffee shops are so serious about their beans and brewing they're as obsessive as Chicago's best bartenders. But the more obsessive they become about their techniques, the more obsessive we become about their coffee.
Here's our guide to Chicago coffee: where to drink it, Chicago restaurants making great coffee drinks, what to eat at coffee shops (hint: the best doughnuts in Chicago) and unexpected spots for a great cup.


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Q: laravel 4 install issue when using composer install When I run

composer.phar install

I get this error almost at the end of the installation, any ideas?
Loading composer repositories with package information
Installing dependencies (including require-dev) from lock file
Nothing to install or update
Generating autoload files
Warning: Unexpected character in input:  '\' (ASCII=92) state=1 in /home/belendez/public_html/laravel/artisan on line 46
Parse error: syntax error, unexpected T_STRING in /home/belendez/public_html/laravel/artisan on line 46
Script php artisan optimize handling the post-install-cmd event returned with an error

I look into the suggested link and it appears that somehow in the middle of the installation it changes from 5.3 to 5.2! If I try to start installation using 5.2 it returns right a way: class 'Phar' not found. Any ideas?

A: It seems that issuing only a php command will pick up another version of the interpreter. Try to change all the references to php in your composer.phar file to the one you want actually to be executed (in your case it seems /opt/php53/bin/php).
You can confirm that by simply launching a php -v directly from the command line to see what version your server will pick up

redpajama_set_name: RedPajamaStackExchange


Bronchiectasis moves in a viciouc cycle with the potential to cause increasing amounts of damage to airways. Untreated, this vicious cycle of infection, inflammation, and injury often results in progressive symptoms and loss of lung function, chronic lung infection, poor pulmonary function tests and decreased activity and quality of life.
Recurring lung infections from bronchiectasis may be caused by different pathogens or may be chronic due to mucus failing to be cleared from the lungs. Patients with bronchiectasis have been shown to harbor more bacteria and other pathogens in the lungs and have more inflammatory markers in their bodies. Exacerbations are made worse by and can be more frequent during infections, a complication indicative of increased inflammation and progressive tissue damage.


Higher bosses, wider tasks, and FinTech disrupting the financial services market. This is the face of the new Financial Services sector in Salt Lake City. The roster of innovators and leaders is impressive; talented strategists with global responsibilities are helping to shape our future growth.
Did you know Utah is one of the top states in the nation for Financial Services employment? Utah employs more than 75,000 people in the Financial Industry and has experienced employment growth at over 23% over the last 5 years. We are home to the highest number of industrial banks in the nation with 15 chartered industrial banks, eight of which are in the top ten industrial banks in the country by asset size.
"Salt Lake City is earning a new title: 'Wall Street of the West' – Natalie Gochnour, Ph.D., associate dean of David Eccles School of Business and director of University of Utah's Kem C. Gardner Policy Institute.
Why Salt Lake City? A large part of the growth is due in large part to quality — workforce, life, and business environment. The world is taking notice as financial and business services are choosing to relocate to lower cost markets in the U.S. instead of moving them abroad. Within the last 10 years, Utah has eliminated or modified nearly 400 business regulations to ease the burdens placed on businesses. Over 100 languages are spoken fluently in Salt Lake Metropolitan area which is a benefit to international finance and business services. Many employers are discovering what we already know that Salt Lake City provides a lower cost of living and better quality of life. What used to be over an hour commute one way for some, is down to 15 minutes or less for many of Salt Lake City's financial services transplants.
The diversity of the financial services workforce —its youth and above-market salaries have contributed to downtown's revitalization and embraced our commitment to public transportation and lowering our carbon footprint. In fact it's one of the reasons newcomer Elefant chose downtown, and Fidelity, Zions Bank, and Goldman Sachs (among others) have invested so heavily in the annual UCair Clean Air Challenge. You could call it a higher minded community that wants to see Salt Lake City thrive and grow responsibly.
Speaking of community, the finance sector is what many local economic developers call a targeted industry cluster. We focus on it because tends to provide higher wages – now and in the future. As the industry grows it reaches a something of a critical mass — we tend to see a synergy and community building phenomenon, must like what we're seeing now with Silicon Slopes. Employers support one another, more opportunities are created, and we retain workforce in the long term. This, in turn, helps the local economy and all employees.
Reach out to Salt Lake City's Department of Economic Development for more information about our Financial Services industry. We're here to help!


Bite Copenhagen 2018: Setting Tomorrow's Table!
How does tomorrow taste? On the 30.-31. of august you can find out for yourself, when the international food exhibition Bite Copenhagen, presents tasty solutions to current and future challenges.
Bite Copenhagen is a culinary mecca that offers debate, inspiration and new know-how/knowledge. You can experience a magnitude of international exhibitors and speakers, as they serve their creative ideas within artificial intelligence, gastronomy, social responsibility, organic food, sustainability, alternative protein sources, insects, superfoods, technology and much more.
Bite Copenhagen will be packed with professionals from the industry; but we have reserved tickets for excited enthusiasts, who wishes to explore the culinary horizon and please their taste buds.Order your ticket now and stay updated on www.bitecopenhagen.dk, where the program, news and information is updated on a regular basis.


TheBestIRS Wins Inavero's 2014 Best of Staffing® Client Award!
TheBestIRS Wins Inavero's 2014 Best of Staffing® Client Award!
Two years in a row! We couldn't be more excited to announce that TheBestIRS has once again won Inavero's 2014 Best of Staffing® Client Award (presented by CareerBuilder. This award not only highlights how our continuous improvements have made a difference for our clients but it also solidifies us as a staffing industry leader atop our competitors. Here's what YOU should know with regards to the staffing industry's most prestigious award.
What is Inavero's Best of Staffing® Award?
This award provides the only statistically valid and objective service quality benchmark in the industry. It reveals which staffing agencies are delivering the highest satisfaction levels to clients and to the permanent and temporary employees for whom they find jobs. The award outcome naturally highlights a growing divide between the industry's leaders and laggards, and identifies TheBestIRS as one of the best staffing agencies for companies and job candidates alike.
How does a staffing company win the Best of Staffing® Client Award?
In order to achieve this high honor, TheBestIRS received satisfaction ratings of 9 or 10 out of 10 from 66.7 percent of clients, compared to just 35 percent for the average staffing agency. TheBestIRS score almost doubled that of the industry average!
Why does the Best of Staffing® Award matter for our clients?
Less than two percent of all staffing agencies in North America receive the Best of Staffing® Award for ser vice excellence. Clients in the market of sourcing and securing a reputable staffing company to assist them with their hiring needs can rest assured they've made the right choice when they select a Best of Staffing® winner.
What does The Best of Staffing® Award mean to TheBestIRS?
Our first win was an exceptional and remarkable accomplishment. Being the ONLY insurance staffing company to win this award two years in a row is an even bigger achievement. Saying we're pleased is an understatement, but we've spent the past two years dedicating our efforts to continuously improving for our clients. So, this award not only benefits TheBestIRS, but also our clients. When they're in need of a staffing solution, they know exactly who they can count on.
How is client satisfaction measured?
Promoters – respondents who answer with a 9 or 10 are most likely to promote your staffing services to others.
Passives – respondents who answer a 7 or 8 are indifferent to your staffing company.
Detractors – respondents who answer with a 6 or lower are unlikely to continue working with your staffing company.
Inavero calculates the Net Promoter Score by taking the percentage of promoters minus the percentage of detractors. Winning The Best of Staffing® Award accelerates our milestones for 2014 and we're excited for our clients to see where TheBestIRS is headed!
Next → Next post: TheBestIRS Wins Inavero's 2014 Best of Staffing® Talent Award!


Have you ever had the occassion to see or listen to a particular thing once, having one impression and then, on a later second ocassion, have a completely different impression? That was almost exactly my experience with False Coda and their debut album Closer to the Edge.
False Coda: another happy metal band.
On the first listen I thought I heard some power metal with some progressive metal nuances and lots of neo-classical guitar. Upon the second listen, the music struck me more as straight progressive metal, though dark, sharp, and piercing. Yes, there's some fine guitar leads, neo-classical and otherwise, but this is not strictly progressive power metal. Maybe I wasn't paying attention the first time, but my second impression is probably the more accurate of the two.
Unfortunately, this conclusion also led me waver between ambivalence and dislike of Closer to the Edge. The songs are not without some merit. But from the start, I got the feeling that False Coda came to the studio with a thousand musical ideas and then put them together in a formless creation much like assembling Tinker Toys while blindfolded. The result was a song like Code of Kindness or Picture becoming nearly tedious as they bumble along in an attempt to be both meticulous and innovative. Also, after some time, my ears tired of the heavy piercingly sharp riffage that riddles nearly every song. I also tired of the vocals, either because it seemed Manos Xanthakis was getting beat up by the music, and so I felt sorry for him, or I simply didn't like his singing. What was interesting, however, is how guitarist Vasilis Milios could weave a tapestry of styles from rock to metal to nearly some jazz fusion. You might catch the latter in Simple Rules and Respect. In the end, there's definitely some talent with False Coda, and I suspect they will only improve in the future.
With Closer to the Edge, upstart Greek band False Coda offers progressive metal, though heavy, dark, and piercing, with mixed results.