text:
> Transportation
> Air Transport
> Airport Market Trends
Airport Information Systems Market: Global Industry Trends, Share, Size, Growth, Opportunity and Forecast 2022-2027
IMARC Group
The global airport information systems market size reached US$ 3.3 Billion in 2021. Looking forward, the analyst expects the market to reach US$ 4.3 Billion by 2027, exhibiting a growth rate (CAGR) of 4.52% during 2022-2027. Keeping in mind the uncertainties of COVID-19, we are continuously tracking and evaluating the direct as well as the indirect influence of the pandemic. These insights are included in the report as a major market contributor.
An airport information system (AIS) refers to a collection of various technological solutions that are majorly used for displaying flight information to passengers at the airports. It uses electronic or mechanical television screens that are located around the terminal, while the virtual versions of the systems are displayed on the website of the airports. AIS includes the airport operation control center (AOCC) that is responsible for cargo handling, flight dispatch, maintenance management and crew management, along with the departure control system (DCS) that is responsible for passenger check-in and boarding, weather monitoring and flight information display.
Significant growth in the aviation industry is one of the key factors driving the growth of the market. Furthermore, the integration of AIS with various advanced technologies, such as near-field communications, video analytics, geolocation, thermal sensors and 3D face recognition, at airport terminals across the globe, is also providing a boost to the market growth. Authorities are employing these solutions to streamline operations and enhance the overall experience of the passengers. Additionally, the widespread adoption of baggage handling systems is acting as another growth-inducing factor. For instance, various airlines are introducing innovative baggage tag devices that are attached to the luggage for assisting in tracking and handling operations at the airport. Other factors, including the rising demand for self-service and automated processes, along with increasing investments to improve the existing infrastructure at the airports, are projected to drive the market further.
Key Market Segmentation:
the analyst provides an analysis of the key trends in each sub-segment of the global airport information systems market report, along with forecasts at the global, regional and country level from 2022-2027. Our report has categorized the market based on type, system, airport, end-use, cost and application.
Breakup by Type:
Breakup by System:
Airport Operation Control Center
Departure Control System
Breakup by Airport:
Class A Airports
Class B Airports
Class C Airports
Breakup by End-Use:
Passenger Systems
Non-Passenger Systems
Breakup by Cost:
Operating Cost
Procurement Cost
Integration Cost
Breakup by Application:
Breakup by Region:
The competitive landscape of the industry has also been examined with some of the key players being Advantech Co., Ltd., Airport Information Systems Limited, Amadeus IT Group, S.A., CGI, Inc., Damarel Systems International Ltd., Harris Corporation, International Business Machines (IBM) Corporation, Ikusi Redes de Telecommunications, S.L., Indra Sistemas S.A., INFORM Institute fur Operations Research and Management GmbH, Lockheed Martin Corporation, Northrop Grumman Corporation, RESA, S.A.S., Collins Aerospace, Siemens AG, SITA, TAV Technologies, Thales Group, Ultra Electronics Holdings plc., etc.
How has the global airport information systems market performed so far and how will it perform in the coming years?
What are the key regional markets in the global airport information systems industry?
What has been the impact of COVID-19 on the global airport information systems industry?
What is the breakup of the market based on the type?
What is the breakup of the market based on the system?
What is the breakup of the market based on the airport?
What is the breakup of the market based on the end-use?
What is the breakup of the market based on the cost?
What is the breakup of the market based on the application?
What are the various stages in the value chain of the industry?
What are the key driving factors and challenges in the industry?
What is the structure of the global airport information systems market and who are the key players?
What is the degree of competition in the industry?
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Smart Airport Market Research Report by Airside, Lanside, Terminal Side, Application, Region - Cumulative Impact of COVID-19, Russia Ukraine Conflict, and High Inflation - Global Forecast 2023-2030
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text:
De Lievensberg is een 127 hectare groot bosrijk landgoed ten oosten van de A58 bij Bergen op Zoom.

Het gebied bestaat uit loof- en naaldbos, enkele vastgelegde stuifduintjes en een ca 25 ha groot heideveld in het van het gebied. Het ligt vlak bij de stad en wordt druk bezocht door wandelaars. Het is met name te bereiken vanaf de Balsedreef. Die omgeving is fraai parkachtige aangelegd met zware beukenlanen, picknickplaatsen, een heidetuin en een vijver. In de heidetuin vindt men 30 soorten heide.

Het gebied is in eigendom en beheer bij de gemeente Bergen op Zoom, maar een deel is in gebruik als waterwingebied van Brabant Water, dat een productiebedrijf heeft nabij Nieuw-Borgvliet aan de overzijde van de A58, Mondaf genaamd.

Ligging
Het gebied ligt ten oosten van de kom van Bergen op Zoom, daarvan gescheiden door Rijksweg 58. Via een tunnel komt men op de Balse Dreef, vanwaar ook een wandeling is uitgezet. Ten noorden van de Lievensberg vindt men landgoed Zoomland, ten zuiden ligt landgoed Groot Molenbeek en ten zuidoosten vindt men het tot Zurenhoek behorende landgoed Boslust.

Natuurgebied in Noord-Brabant
Heide in Nederland
Bergen op Zoom

redpajama_set_name: RedPajamaWikipedia


text:
CROW credits storm doors for saving animal hospital from Ian's storm surge
By Meagan Miller
SANIBEL, Fla. — When Alison Hussey returned to Sanibel for the first time since Hurricane Ian, she cried tears of joy to see what was left. She's the Executive Director for the Clinic for Rehabilitation of Wildlife (CROW). She showed ABC7 what saved their hospital building from the storm surge that ravaged the island.
"It could have been so much worse, it really could have. The whole island was underwater at one point," said Hussey.
They still have what's estimated to be hundreds of thousands of dollars in damage.
"A lot of our structures, our rehabilitation enclosures are older so the large flight enclosure went down, the pelican enclosure behind the ambassadors went down," Hussey said.
The winds were powerful and CROW's whole property got more than five feet of water. Most of their buildings are elevated on stilts, but not the hospital. For many homes and businesses on the island, that meant destruction.
"The older homes on Sanibel, the ones that were ground level, those are the ones that we saw that had all the damage and that was tough, because they lost everything," Hussey said.
Literally everything. Some people on Lee County's barrier islands couldn't even find their homes after Ian. Others had to gut everything that was left. But that wasn't the case for CROW, and they credit one of their storm prep tools for saving it — their storm doors. They go right over the regular doors.
"You put them up and then bolt it in, and there's a foam seal that kind of suction cup seals it to the wall to keep surge out," Hussey said.
Seeing how water burst into buildings all over Southwest Florida, Hussey wasn't sure if their storm doors had held — but they did, and she said they made all the difference.
"If the storm doors were not there or if the storm doors had not held, without a question, we would have lost industrial washers and dryers. We would have lost more of the appliances and drywall. It would have ended up being a complete rebuild in there," she said.
They will have to rebuild those older enclosures that got destroyed and replace a lot of equipment.
"But you look and see what we had recently built and all to code. They're still standing without any issues and these buildings were actually built after Hurricane Charley. That was like, okay, hurricane ratings work. So I think now our board is definitely having conversations about future-proofing," Hussey said.
Sometime in the near future, CROW plans to be back in action on the island. For now, they're caring for animals off-island, in some space the Animal Refuge Center is letting them use while cleaning up the damage they have.
"We'll be exploring to make sure the new enclosures look a lot like the new enclosures we just built. All the cabinets have to be ripped out, but I'd rather rip out cabinets than rip out everything and start from scratch," Hussey said.
So many people do have to start from scratch and even if you don't have much damage, FEMA officials said we should all learn from Ian. Right now, they're offering free advice on how to build back stronger and safer, prevent damage in the future, and deal with existing problems.
"So we are providing information on securing your metal, metal roofs, very important techniques on protecting your property from flood damage, of course," said Adolph Hernandez, a FEMA hazard mitigation and community outreach specialist.
Right now, FEMA is set up at the Home Depot in Bonita Springs on Bonita Beach Road. They'll be there until December 11 from 7 a.m. – 6 p.m. You can also call them at 800-621-3362 or use their app.
They've also got sites where you can get help, specifically, with applying for assistance.
Categories: Cover Story, Local, News
Tags: CROWFEMAHurricane IanSanibelstorm doors
By Meteorologist Sami Squires January 28, 2023
By Katie Fogarty January 28, 2023
Last day to vote for LCSO's new robot dog's name
By ABC7 News January 28, 2023

redpajama_set_name: RedPajamaCommonCrawl


Come ashore on an uncharted island where we'll learn that Jesus carries us through life's storms. Mt. Hope's Shipwrecked VBS is for kids from pre-K through 5th grade. Participants will go through five activity-packed mornings full of Bible lessons, games, snacks, music, crafts, and more.
You can register online, pick up a registration form from the church office or call Mt. Hope Church.


text:
är en ortsteil i kommunen Brodersby-Goltoft i Kreis Schleswig-Flensburg i förbundslandet Schleswig-Holstein i Tyskland.  var en kommun fram till 1 mars 2018 när den uppgick i Brodersby-Goltoft.  hade  invånare 2017.

Referenser

Orter i Schleswig-Holstein


Any type of machine to produce rolled sugar cigars, cigarettes, fan-shaped wafers of various shapes.
These machines produce single wafers or wafer ribbon. Downstream devices, either manual or semi-automatic or fully automatic roll and shape the wafer forming cigars, triangular fan shaped wafer etc. In our catalogue "Cigarettes, fan-shaped wafers…" are shown some of the most common types but any other design could be made. Just ask our technical office. Heating can be by electricity (any voltage), natural gas or liquid gas. The control of baking temperatures is automatic. The machines are supplied with all operative instructions including the recipe to make the cones.
To start to make cones, cigars, baskets/shells etc.. these convenient professionals electrical machines are just the best solution. The produced wafer made using the most different recipe (a basic recipe is supplied by our company) can be shaped, using the suitable accessories, in the many forms: rolled sugar cones, cigars or cigarettes, baskets, etc.
Automatic machine to produce flat wafer or rolled sugar cones and/or cigars and bowls/baskets/shells. Electric heating.


Software will amplify not hide any shortcomings in your talent strategy or process. The vendors typically fail at really helping you at this step and is why I am such a proponent of companies using implementation specialist (companies like Talent Function, OneSource Virtual , Appiro do this for the Enterprise level) to help you through this the majority of the time. I love Star Wars and have brought my kids up with a love for Star Wars as well.
The first four themes are fairly predictable – and they all have some impact on talent strategies and HR functions – but the fifth, Diversity, might be a surprise to you. Talent Acquisition and Management. But look at what is in second place: Talent Acquisition and Management!


On Shimmering Strings (2009), Sage demonstrates the versatility of her instrument with selections that range from 16th century Spanish dances to modern classical works. On the CD, Romantic showpieces, Bach transcriptions, early lute pieces, and contemporary compositions combine to form a truly unique collection of harp music.
Sample tracks may be heard on Sage's Soundcloud page.
Sage's most recent recordings, such as this Scarlatti sonata, can be found online on her YouTube channel.


Manager of PAOK Igor Tudor is willing to use Bulgarian striker Dimitar Berbatov at the starting lineup on Sunday's away derby against Olympiakos.
The new signing of the club, central defender Dimitris Chatziisaias, is also expected to be a starter along with Vellidis, Kitsiou, Vitor, Tzavellas, Tziolis, Cimirot, Pelkas, Mak and Athanasiadis.
Dragic: "Khimki-Olympiakos at the Final Four!"


text:
\section{Introduction}

It is critical to perform  low-latency, high-fidelity, online loop closure detection (or place recognition) for real-time visual SLAM  in order to enable bounded localization errors.
This is a challenging problem, because the visual appearance of one location at different times can change dramatically
due to varying viewpoints, illumination, weather, and dynamic objects (see Fig.~\ref{fig:gp_example}).
Numerous algorithms have recently been developed to address these issues~\cite{Lowry2016TRO}.
Although these methods can perform well, in particular, by  incorporating
temporal information~\cite{SeqSLAM,NaseerEtAl,GalvezTRO12,PepperellEtAl, MilfordEtAl, Bampis2017},
they may not be fast or robust enough for real-time performance in  challenging environments.

Convolutional neural networks (ConvNets)~\cite{LeCun1989} have recently become the state of the art for many vision-based classification tasks~\cite{dl-book}.
While off-the-shelf ConvNets are proven as useful feature embeddings for place recognition~\cite{Chen2014, sunderhauf_performance_2015, sunderhauf_place_2015, Razavian2014, Hou2017},
specialized networks have also been constructed and trained to further improve performance~\cite{Arandjelovic16, LOPEZANTEQUERA2017, Chen2017, gao_unsupervised_2017}.
However, most of these ConvNet-based approaches suffer from either slow feature extraction \cite{gao_unsupervised_2017, Hou2017}, slow querying \cite{sunderhauf_performance_2015, sunderhauf_place_2015}, or the need for a large amount of labeled data for training \cite{Arandjelovic16, LOPEZANTEQUERA2017, Chen2017}.

\begin{figure}
        \subfloat{
          \centering
          \includegraphics[width=.235\textwidth]{GPDayEx}\hfill

        }
        \subfloat{
          \centering
          \includegraphics[width=.235\textwidth]{GPNightEx}\hfill

        } \hfill\quad
        \subfloat{
          \centering
          \includegraphics[width=.235\textwidth]{GP_Day_relu3}\hfill
        }
        \subfloat{
          \centering
          \includegraphics[width=.235\textwidth]{GP_Night_relu3}\hfill

        } \hfill
  	\caption{An example image match from the {\em Gardens Point} dataset,
which demonstrates large differences in viewpoint, dynamic objects, and illumination, as well as occlusions.
Nevertheless, with the right image as the query, our proposed method correctly retrieves the left during our experiments (see Section~\ref{sec:experiments}), while all of the tested state-of-the-art methods retrieve incorrect images.
	Below each image, the first face of the descriptor layer, before flattening, is shown.
	Evidently, these visually dissimilar images are transformed into very similar activation maps.
 }
	\label{fig:gp_example}
\end{figure}

To address the aforementioned issues, in this paper, we construct a novel autoencoder-based ConvNet for loop closure that requires very few parameters,
and train it using public data in an {\em unsupervised} manner.
In particular, when building our autoencoder network, we exploit the advantages of classical geometric vision techniques --
the histogram of oriented gradients (HOG)~\cite{HOG} that offers a convenient way to compress images while preserving salient features,
and the projective transformation (homography)~\cite{Hartley2004} that relates images with differing viewpoints.
In contrast, we also incorporate the modern stacked convolutional autoencoder into the network to be data-driven.
Consequently, the features extracted from our network are not only robust to extreme variations in appearance,
but also lightweight and compact enough for real-time loop closing -- even for resource-constrained systems.

The main contributions of this paper are the following:
\begin{itemize}

\item We design an {\em unsupervised}, convolutional autoencoder network architecture, tailored for loop closure,
and amenable for efficient, robust place recognition.

\item We perform extensive comparison studies of the proposed deep loop-closure model
against the state-of-the-art methods on different datasets.
To benefit the research community,
we {\em open source} our code and pre-trained model used in this work along with a new dataset\footnote{The code, dataset, as well as the pre-trained model from this work are available online: \url{https://github.com/rpng/calc}.}
that captures extreme variations in viewpoint, weather, illumination, and dynamic objects in a single dataset.

\end{itemize}

The rest of the paper is structured as follows:
After reviewing the related work in the next section, in Section \ref{sec:methodology} we present in detail the proposed deep loop closure network,
including the network architecture, training scheme, and online usage.
The proposed approach is tested extensively in Section \ref{sec:experiments} -- both against state-of-the-art algorithms and in a real-time loop-closure setting.
Finally, we conclude the paper in Section \ref{sec:conclusions}.

\section{Related Work} \label{sec:rel_work}

Due to its importance, loop closure, or place recognition, has attracted significant attention in recent years.
Many different algorithms have been introduced (see~\cite{Lowry2016TRO,Latif2017RAS} and references therein),
with variant performance characteristics in terms of complexity, robustness and efficiency.

The approaches based on bag of words (BoW), such as FAB-MAP \cite{fabmap} and DBoW2 \cite{GalvezTRO12},
are among the most popular for real-time visual SLAM systems (e.g., \cite{murORB2, murTRO2015, lsd_slam}).
These methods build vocabulary trees based on point features of different descriptors~\cite{Bay2008,ORB,BRIEF}, typically amenable for fast querying of matches;
yet, they may fail when there are large variations in appearance between images.
For this reason,
SeqSLAM~\cite{SeqSLAM} was introduced to  utilize the information provided by image sequences to construct a better hypothesis of loop closure.
However, this method directly compares pixel values of down-sampled images and can fail under large variations in viewpoint.
In contrast, different hand-crafted features are used in \cite{Latif2014RSS,Latif2017RAS,Zhang2016RSS},
where the loop closure is formulated as sparse optimization problems.

Recently, ConvNet-based approaches have risen in popularity.
\citet{Chen2014} first introduced the concept of using features produced by the off-the-shelf Overfeat network~\cite{overfeat}
as a holistic image descriptor -- shown to outperform state-of-the-art place recognition systems.
However, the descriptors extracted from such deep networks are too large to be used for real-time loop closure without approximating their similarity scores, which hinders their widespread deployment.
Since then, many similar deep learning approaches have been introduced.
For example,
\citet{sunderhauf_place_2015} employed ConvNet features to match subregions corresponding to landmarks, improving upon the performance of the holistic image descriptors, but the authors pointed out that it was {\em nowhere} near fast enough to be used in real time.
\citet{KENSHIMOV2017124} proposed a method to omit parts of the activation maps from the neural networks in order to improve cross-seasonal place recognition.
\citet{Hou2017} combined ConvNet features with a bag of words scheme to speed up querying,
while \citet{BAI2018} combined ConvNet features with sequence searching to increase reliability.
All of these methods rely on features extracted from generic neural networks that are not trained specifically for loop closure.

Others have trained their own networks for place recognition.
For instance,
\citet{Chen2017} compiled a large place recognition-specific dataset to train classification networks for the sole purpose of feature embedding.
NetVLAD \cite{Arandjelovic16} is an architecture that relies on geotags from Google Street View to label training images for a triplet loss scheme,
where a triplet consists of two matching images and one non-matching image.
\citet{LOPEZANTEQUERA2017} proposed a similar method using manually-labeled triplets, which reduces images into a single 128-dimensional vector.
Their descriptor is shown to be useful for place recognition,
and far more compact than that from any previous methods (e.g., \cite{Arandjelovic16}).
However,
all of these methods rely on {\em supervised} learning --
requiring an immense amount of (human) effort to label images.

To address this issue, \citet{gao_unsupervised_2017} recently introduced a stacked denoising autoencoder architecture~\cite{Vincent2008} to solve the place recognition problem.
Their method is shown to perform comparably to FAB-MAP 2.0~\cite{fabmap2}, but suffers from slow feature extraction.
The model employed by \citet{gao_unsupervised_2017} learns to reconstruct an image that has had random pixel values altered, but, if it is to be used for place recognition, it then has to be invariant to variations in viewpoint.
Intuitively, it would be more useful to train an unsupervised model to reconstruct an image that has been altered to mimic the viewpoint variations that it will encounter in reality.
With this observation,  in this work,
we build upon the autoencoder concept, utilizing the multi-view geometry of homographies and the invariance of HOG, to design a novel unsupervised architecture that is both more lightweight than the previously mentioned ConvNets, and trained to compensate for the specific types of visual appearance changes that are often encountered in loop closure scenarios.

\section{Unsupervised Deep Loop Closure} \label{sec:methodology}

\begin{figure*} [!t]
	\centering
	\includegraphics[width=\textwidth]{train_diagram}
	\caption{The training pipeline for our deep model. In this architecture,

	 the projective transformations and HOG descriptors are computed only once for the entire training dataset, and the results are then written to a database to use in training.
	 Upon deployment, the batch size $N$ is set to 1, and only the layers in the boxed area are used.
	}
	\label{fig:train_diagram}
\end{figure*}

In this section,
we present in detail our method to construct, train, and utilize a novel autoencoder network for the loop closure task.
Our model is designed to map high-dimensional raw images into a low-dimensional descriptor space,
which is invariant to appearance differences.
The proposed network and training scheme creates a compact robust feature embedding, while eliminating the need of image labeling.

\subsection{Design Motivation} \label{sec:design_motivation}

The standard denoising autoencoder network randomly drops input values during training to mitigate the effect of noise from actual signals during testing~\cite{Vincent2008}.
Clearly, such networks do not learn the variations in images that a loop closure system will encounter, such as changes in viewpoint, illumination and so on.
Thus, the direct deployment of such autoencoders for place recognition may not be optimal.

Inspired by~\citet{sunderhauf_place_2015}, where synthetic viewpoint alterations, in the form of simple translations, were used to {\em test} their place recognition system,
we employ more generalized viewpoint alterations (projective transformations) to {\em train} our deep loop-closure model.
Specifically, we inflict ``noise'' on the training inputs while modeling the natural variations due to robot motion,
thus improving the performance of the autoencoder specifically for the place recognition task.
However, these raw image pairs are not enough.
In experimentation with different network architectures,
 we constructed an autoencoder that shares the same encoding layers as the proposed model,
 but utilizes deconvolution and unpooling layers to attempt to reconstruct the other raw image from the pair.
Without any extra optimization constraints, the network learned zero vectors, which suggests that the model needs more information to map one image from the pair to the other.

HOG, by design, provides geometric information about an image.
\citet{Li2015} showed that HOG description over segmented image patches can successfully be used to match images with vastly differing appearances in a place recognition setting.
Furthermore, since HOG descriptors are fixed-length vectors for images of the same size, and can naturally be compared by the Euclidean distance, they are easily integrated into a neural network with $\ell_2$ loss.
However, since HOG relies on gradient orientation, it is not very robust to alterations in viewpoint, but, on the other hand, the image gradients are robust to illumination to some degree.
Therefore, HOG provides the prior geometric knowledge needed by our network with the added benefit of learning illumination invariance, while the random projective transformations still create the added noise required to obtain a more useful feature embedding than just HOG can provide.
Finally, it should be noted that we do not randomly place dynamic objects in the training pairs, even though our model is shown to be invariant to them (see Section \ref{sec:experiments}).
While doing so could potentially improve robustness to such occlusions, well-trained ConvNets are naturally invariant to such noise as~\citet{sunderhauf_performance_2015} observed.

\subsection{Network Architecture} \label{architecture}

Fig.~\ref{fig:train_diagram} provides a visualization of the data flow from raw images to the loss layer.
Before training begins, every image in the set of training images $\mathcal{I}$ is converted to grayscale, resized to $120 \times 160$, and used to create an image pair (see Fig. \ref{fig:im_warp_ex} and Algorithm \ref{alg:training_data_alg}).
The HOG descriptor is computed for a randomly chosen image from each pair.
We stack all the HOG descriptors from each batch of training images, denoted by $\mathbf{X}_2$ of dimension $N\times D$, where $N$ is the batch size and $D$ is the dimension of each HOG descriptor.
The other image from the pair remains in raw form, and is stacked along with the other $N-1$ images in that training batch.
The resulting tensor denoted by $\mathbf{X}_1$ has the dimension of $N \times 120 \times 160$.

The training network aims to reconstruct $\mathbf{X}_2$ given $\mathbf{X}_1$ using only two convolution and pooling paired layers,
one pure convolution layer, and three fully-connected layers.
Note that every layer has an activation after it.
We use the rectified linear unit (ReLU) activation for the convolutional layers,
while the sigmoid activation is chosen for the fully connected layers in order to better reconstruct the HOG descriptor (as it normalizes the data into $[0,1]$).
Additionally,
since the Euclidean distance is naturally a good distance metric for HOG descriptors,
we employ an $\ell_2$ loss function to compare $\mathbf{X}_2$ with its reconstruction $\hat{\mathbf{X}}_2$.
Upon deployment, we drop all layers but $\mathbf{X}_1$ and the three convolution layers.
Our model is extremely lightweight compared to the state-of-the-art models for place recognition~\cite{LOPEZANTEQUERA2017, Arandjelovic16, sunderhauf_place_2015}, taking up only 139 MB of GPU memory,
allowing plenty of space for other processes -- even on resource-constrained low-cost platforms.

\subsection{Network Training} \label{sec:training_data}

As previously mentioned, the proposed model does not require the training images to be labeled or contain any specific information -- that is,
any image from any scene can be used in the training set to improve our model.
To illustrate this,
we have trained our model on the {\em Places} dataset~\cite{zhou2017places}, which has over 8 million images  originally designed for scene recognition.
Figs. \ref{fig:train_diagram} and \ref{fig:im_warp_ex} contain a few examples of images from this dataset.
While the majority of the images in the {\em Places} dataset are unrelated to any scene that a loop closure algorithm may encounter, the sheer number of images leads to improved performance over training on smaller datasets.
Algorithm~\ref{alg:training_data_alg} outlines the main steps of utilizing such a dataset to create $\mathbf{T}_1$ and $\mathbf{T}_2$, the large tensors from which $\mathbf{X}_1$ and $\mathbf{X}_2$ are sampled during every iteration of stochastic gradient descent.

\begin{algorithm} [!t]
	\caption{Generating Training Data}\label{alg:training_data_alg}
	\textbf{input:} $\mathcal{I}$: A set of grayscale training images, resized to $\textit{H
} \times \textit{W}$

	\textbf{output:} $ \mathbf{T}_1 \in \mathbb{R}^{\textit{M} \times \textit{H} \times \textit{W}} $ and $ \mathbf{T}_2 \in \mathbb{R}^{\textit{M}\times\textit{D}}$

	\textbf{define:} $ \textit{rand}(\mathcal{A}) $ as a map from set $\mathcal{A}$ to one of its elements, chosen at random

	\begin{algorithmic}[1]
		\State $ \textit{W, H, D, M} \gets 160, 120, 3648, |\mathcal{I}| $
		\State $ \mathbf{T}_1 \gets \mathbf{0}_{\textit{M} \times \textit{H} \times \textit{W}} $
		\State $ \mathbf{T}_2 \gets \mathbf{0}_{\textit{M} \times \textit{D}}
		\State $ \mathbf{P}_c \gets ((0,0),(0,\textit{H}),(\textit{W},0),(\textit{W,H})) $
		\For{$ \textit{i} \in \mathbb{N} \cap [1,\textit{M}] $}
			\State $ \mathbf{I} \gets \textit{rand}(\mathcal{I}) $
			\State $ \mathbf{P}_r \gets \textit{randFourPts}(\textit{W,H})
			\State $ \mathbf{H}_p \gets \textit{estimateHomography}(\mathbf{P}_r, \mathbf{P}_c)  $
			\State $ \mathbf{I}_w \gets \textit{transform}(\mathbf{I}, \mathbf{H}_p) $
			\If{$\textit{rand}(\{0,1\})$}
				\State $ \textit{swap}(\mathbf{I}, \mathbf{I}_w) $
			\EndIf
			\State $ \mathbf{T}_1^{(\textit{i})} \gets \mathbf{I} $
			\State $ \mathbf{T}_2^{(\textit{i})} \gets \textit{calcHOG}(\mathbf{I}_w) $
		\EndFor
	\end{algorithmic}
\end{algorithm}

Given an image $\mathbf{I} \in \mathcal{I}$, we would like to automatically generate $\mathbf{I}_w$, which is of the same scene as $\mathbf{I}$ from a different viewpoint; this effect is achieved by applying a randomized 2D projective transformation matrix, $\mathbf{H}_p \in \mathbb{R}^{3 \times 3}$, to every pixel location in $\mathbf{I}$.
To obtain this matrix, four points are randomly selected within the bounding boxes along the corners in the image $\mathbf I$ (see Fig.~\ref{fig:im_warp_ex}); $\mathbf{H}_p$ is then calculated to warp $\mathbf{I}$ such that those four points become the four corners of $\mathbf{I}_w$.
We choose each bounding box of the point selection to be $H/4 \times W/4$ in order to avoid excessive distortion of $\mathbf{I}_w$, while still warping it enough to emulate a new perspective of the scene.
Since every $\mathbf{I}_w$ appears zoomed in compared to $\mathbf{I}$, we randomly choose which of the images out of every pair to place into $\mathbf{T}_1$, avoiding unnecessary training biases.

We employ a HOG descriptor with large strides and a small window size to reduce the dimension of one of the images in each training pair --
mapping an image $\mathbf I \in \mathbb{R}^{120 \times 160}$ of 19,200 pixels to $\mathbb{R}^{3,648}$.
While this particular HOG descriptor may not be very informative for place recognition because of its aggressive data compression,
it helps the autoencoder model to learn a good image encoding as mentioned in Section~\ref{sec:design_motivation}.
To construct and train our model, we utilize the {Caffe Deep Learning Library}~\cite{jia2014caffe} due to its efficiency.
We train our model for roughly 42 epochs with a fixed learning rate of $9\times10^{-4}$.
Based on~\citet{AlexNet}, we choose a momentum of 0.9, and weight decay of $5\times10^{-4}$.

\begin{figure}[t]
	\centering
	\includegraphics[width=\columnwidth]{ImWarpEx}
 	\caption{An example of a possible training image pair.
		The four bounding boxes shown on the raw image (left) highlight the possible locations of each randomly selected point.
		Once the point correspondences are generated, a 2D projective transformation is calculated such that each one of those points becomes a corner of the warped image (right) after applying it.
		The randomly selected points are shown on the left, connected to their corresponding locations in the warped image shown on the right.
	 }
	\label{fig:im_warp_ex}
\end{figure}

\subsection{Online Use} \label{sec:online_use}

Once our model is trained, upon its deployment for online use,
we create a database of the descriptors extracted by our model and later query it to find loop closure candidates.
While K-D trees \cite{Bentley1975} are a popular means to create such databases for nearest-neighbors searches, there is no speed up over a linear search for 1,064-dimensional vectors -- even when the search is approximated \cite{flann_pami_2014}.
For this reason, we use the simple linear search method.
Additionally, as the descriptors are compact enough, their similarity can be calculated directly with no dimensionality reduction.

We seek to emphasize that our method of creating and querying the database with the descriptors extracted from our model is simple but effective;
albeit, we are able to achieve faster-than-real-time querying speed with minimal memory usage (see Section~\ref{sec:runtime-eval}).
Furthermore, since many new ConvNet-based place recognition systems~\cite{sunderhauf_place_2015, KENSHIMOV2017124, Hou2017, BAI2018} rely on features from bulky off-the-shelf networks, our light-weight model can potentially be utilized in many of these systems to achieve speedups with competative accuracy (see Section \ref{sec:improving_performance}).

\section{Experimental Results} \label{sec:experiments}

To validate the proposed unsupervised deep loop closure model,
we have performed extensive comparison studies on various datasets with the state-of-the-art approaches as well as other benchmarks where applicable.
While runtime is used as the criterion for evaluating efficiency,
we utilize the precision-recall curve, a standard method to evaluate binary classification, to quantify effectiveness.
While there are many ways to interpret a precision-recall curve, we  primarily use:
(i) the area under curve (AUC), where a higher AUC is desired;
and (ii) the maximum recall rate with 100\% precision,  denoted by $r$, where again a higher value is desired.
This can be observed visually in any precision-recall curve, as it will be the recall rate where the precision first dips down from 1.0.
By observing both of these values, we obtain a comprehensive picture about how well the considered algorithms can generalize; however, the $r$ value is slightly more desirable in practice, since one binary classifier can have non-perfect precision for all recall rates despite a high AUC.

For the results presented below,  we compare the proposed approach with the following:
(i) {\bf Autoencoder}: A traditional denoising convolutional autoencoder.
 This model has the same encoding layers as our proposed model upon deployment, but, instead of reconstructing HOG descriptors of warped images, it utilizes deconvolution and unpooling layers to reconstruct the original image and is subject to random dropout during training.
(ii) {\bf LA}: The model from \citet{LOPEZANTEQUERA2017}, which has comparable efficiency as our {\em unsupervised} model while requiring labeled data for training (i.e. {\em supervised}), making any retraining difficult.
(iii) {\bf DBoW2}: We use the DBoW2 vocabulary tree from the state-of-the-art ORB-SLAM~\cite{murTRO2015, murORB2}.
(iv) {\bf AlexNet}: \citet{sunderhauf_performance_2015} found AlexNet \texttt{conv3} to be the most robust layer for place recognition; however, it was also noted that the 64,896-dimensional vector produced was too large to perform real-time database queries.
Therefore, we apply Gaussian random projection (GRP)~\cite{Dasgupta2000, Bingham2001} as in~\cite{sunderhauf_place_2015} to compress the \texttt{conv3} layer to the same size as the descriptors from the proposed model.
In our tests, we use the AlexNet trained by BVLC.
(v) {\bf HOG}: Although the 3,648-dimensional HOG descriptor is used to train our model,
we include this comparison merely to show that our model is able to learn a better feature than the original reduced HOG,
rather than to show the ability of HOG as descriptors for place recognition.
Note that for all of these methods, we use a single nearest-neighbor linear search in order to purely compare the ability of each descriptor to match places.
At last,
it should be pointed out that
in all the following experiments,
our approach uses the {\em same} model  trained on a completely {\em different} dataset from the testing datasets,
showing that the proposed deep loop closure network does not require environment-specific training.

\begin{figure} [!t]
	\centering
	\subfloat{
	  \centering
	  \includegraphics[width=.475\columnwidth]{AlderleyNightEx}
	}
	\subfloat{
	  \centering
	  \includegraphics[width=.475\columnwidth]{AlderleyDayEx}
	}
	\caption{An example image pair from the Alderley dataset. Note that these frames are extremely difficult to match, even for a human.}
	\label{fig:alderley_ex}
\end{figure}

\begin{figure}[!t]
	\centering
	\includegraphics[width=\columnwidth]{AlderleyNightVDay}
	\caption{Our method outperforms the state-of-the-art algorithms on the {Alderley dataset}, with the highest AUC and $r$ value.}
	\label{fig:alderley_night_v_day}
\end{figure}

\subsection{The Alderley Dataset}

The {Alderley dataset}
was first introduced in SeqSLAM~\cite{SeqSLAM}
and is composed of two image sequences, extracted from videos taken during a rainy night and a sunny day.
Fig. \ref{fig:alderley_ex} shows an example image match from this dataset; it is very difficult even for human to realize these images are of the same place.
Frame correspondences are included in the dataset,
providing ground truth for place recognition, with an added tolerance for multiple sequential frames of the same location.
We test on the last 200 frames of each sequence.
The comparison results are shown in Fig. \ref{fig:alderley_night_v_day}.
Clearly, our method is the most robust in this case, taking the highest AUC and $r$ value by large margins.
Interestingly, the regular autoencoder performs well here.
Note that the model from \cite{LOPEZANTEQUERA2017} was trained on a different subset of the Alderley dataset than used here, giving their model an advantage over the others that have not been trained on any of the Alderley dataset.
Nevertheless, our model is still more robust in this experiment,
while the other methods are failing due to the significant differences in appearance in this dataset.

\subsection{The Gardens Point Dataset}

The {Gardens Point dataset}
consists of three traversals through the QUT campus in Brisbane, Australia.
In this dataset, there are two day-time traversals --
one  tends to contain images of the left side of the walking path, and the other contains the right side.
Additionally, there is one night-time traversal, which tends to the right side of the path as well.
Unlike in the Alderley dataset, image $i$ from one sequence matches image $i$ from any of the other two.
We utilize this, as well as an added tolerance for multiple sequential images of the same location, to define the ground truth for this experiment in addition to the remaining precision-recall experiments in this work, since the rest of the datasets follow the same format.

An example of this dataset is shown in Fig.~\ref{fig:gp_example},
while Fig. \ref{fig:gp_day_left_v_right} shows the comparative results.
Fig. \ref{fig:gp_day_left_v_right} (top) is for the {\em day left and day right sequences}.
Our method, AlexNet, and LA perform comparably in this dataset, and even the autoencoder is not far behind; however, we will see that this trend of comparable performance does not carry throughout the experiments.
DBoW2 is competitive in this experiment, but falls short of our method, AlexNet, and LA.
The HOG descriptor we used to train our model is clearly not nearly as robust as our final descriptor, even in this daytime dataset -- one of the easier datasets used in experimentation.
To further challenge these methods,
we use the {\em night-time sequence} from the Gardens Point dataset --
the results of which are shown in Fig. \ref{fig:gp_day_left_v_right} (bottom).
In this case, our method takes the highest $r$ value, and the second-highest AUC.
DBoW2, HOG, and the autoencoder completely fail in this test.
Although the performance of DBoW2 could most likely could be improved by training the ORB vocabulary tree on the night-time images,
we want to test all of these methods void of any environment-specific training for the purpose of generalization.

\begin{figure}[!t]
	\centering
	\includegraphics[width=\columnwidth]{GardensPointDayLeftVDayRight}
	\includegraphics[width=\columnwidth]{GardensPointDayLeftVNightRight}
	\caption{The comparison results on the Gardens Point dataset.
	(top) Our method performs comparably with \cite{LOPEZANTEQUERA2017} (which, however, is a supervised learning approach) in the {\em  day-time sequence}, while (bottom) our method  outperforms its competitors  in the {\em night-time sequence}.
	}
	\label{fig:gp_day_left_v_right}
\end{figure}

\subsection{The Nordland Dataset}

The {Nordland dataset},
one of the most challenging place recognition datasets to date~\cite{KENSHIMOV2017124},
consists of four time-synchronized videos of train journeys through Norway.
Each of the four 9-hour long sequences corresponds to a different season, creating a difficult challenge for cross-seasonal place recognition.
In addition to seasonal variation, the images also contain extreme blurring from the fast speed of the train.
Fig. \ref{fig:nord_ex} shows an example of an image pair from this dataset.
We test our method on one of the most difficult sequence pairs, {\em Winter versus Spring}.
Specifically, we extracted 5,357 frames from these two videos.
This experiment was performed using frames 29 to 200, as this was the first sequence we found where the train was constantly in motion and outside of tunnels.
 Note that images from inside the tunnels are completely black, and therefore useless for experimentation; additionally, if the train was stopped at a station, there were too many sequential images of the same location, causing large biases in the precision-recall curves.

The results of this experiment are shown in Fig.~\ref{fig:nord_spring_v_winter}.
It should be noted that \citet{LOPEZANTEQUERA2017} used all but the last hour of each Nordland sequence in training their model; this implies that their model has seen this testing data in the training phase.
However, even with this incredible disadvantage, our model outperforms theirs, along with other methods in this experiment.

\begin{figure}[!t]
	\centering
	\subfloat{
	  \centering
	  \includegraphics[width=.46\columnwidth]{NordlandSpring}
	}
	\subfloat{
	  \centering
	  \includegraphics[width=.46\columnwidth]{NordlandWinter}
	}
	\caption{An example image pair from the Nordland dataset. The left image is from the spring sequence while the right one is from the winter.}
	\label{fig:nord_ex}
\end{figure}
\begin{figure} [!t]
	\centering
	\includegraphics[width=\columnwidth]{NordlandWinterVSpring}
	\caption{Comparison results on the Nordland dataset.
	Our method is observed to be more robust to the seasonal changes provided by this subset of the winter and spring sequences.
	}
	\label{fig:nord_spring_v_winter}
\end{figure}

\subsection{Our Campus Loop Dataset}

The Nordland dataset provides extreme weather variations, the Gardens Point dataset provides extreme brightness and viewpoint variations, as well as many dynamic objects,  while the Alderley dataset provides all but large viewpoint variations.
However, we found that no dataset can provide all of these challenges.
Therefore, we collect our own dataset, termed as the {\em Campus Loop dataset}.
The dataset consists of two sequences of 100 images each.
The sequences are a mix of indoor and outdoor images in a campus environment.
The first sequence was taken on a snowy day, when it was very cloudy, while the second was taken nine days later, when most of the snow had melted and the sun was out.
The indoor images obviously do not vary as much with this weather change.
Additionally, each image match contains varying perspectives and many dynamic objects, making this one of the most challenging publicly-available place recognition datasets.
Fig. \ref{fig:campus_loop_ex} shows an example of an image pair from this dataset.

The results of experimentation with this dataset are shown in Fig. \ref{fig:campus_loop}.
As expected, across the board the performance is worse than any other dataset thus far.
Nevertheless, comparatively,
our method is the most robust to the challenges presented in this new dataset.
The model from  \citet{LOPEZANTEQUERA2017} falls flat in this experiment,
performing significantly worse than the other three deep-learning methods, and falling short of even DBoW2 in AUC.

\begin{figure}[!t]
	\centering
	\subfloat{
	  \centering
	  \includegraphics[width=.475\columnwidth]{CampusLoop1}
	  \label{fig:campus_loop_ex:subfig1}
	}
	\subfloat{
	  \centering
	  \includegraphics[width=.475\columnwidth]{CampusLoop2}
	  \label{fig:campus_loop_ex:subfig2}
	}
 	\caption{An image pair example from our Campus Loop dataset, which has extreme variations in viewpoint, weather, and dynamic objects.}
	\label{fig:campus_loop_ex}
\end{figure}

\begin{figure}[!t]
	\centering
	\includegraphics[width=\columnwidth]{CampusLoop}
	\caption{Our approach outperforms the other benchmark methods on our own Campus Loop dataset,
	with the highest $r$ value while tying with AlexNet \texttt{conv3} for the highest AUC.}
	\label{fig:campus_loop}
\end{figure}

\subsection{Runtime Evaluation}
\label{sec:runtime-eval}

To validate the efficiency of our approach,
we perform runtime evaluations of both descriptor computing time, and database querying time for a single-nearest neighbor search.
These tests are conducted on affordable hardware to allow for better reproducibility --
specifically, an i7-6700HQ CPU, and a GeForce GTX 960M GPU.
Note that in this test we only compare against DBoW2 and AlexNet due to the following:
(i) \citet{LOPEZANTEQUERA2017} do not provide an open-sourced library for performing image matches,
so it would not necessarily be fair to time their models in our code.
They report 1.8 millisecond descriptor computing time using a GPU, which is slower than ours.
However, their descriptor is smaller than ours, so it should be cheaper to query, while ours is shown to be competative (if not better) in accuracy, and more convenient to fine-tune or retrain.
(ii) The reduced HOG is presented in the preceding tests only to show that our model learns a better version of it, rendering its runtime irrelevant.

We choose DBoW2 with ORB features as one benchmark, since it is one of the fastest place recognition libraries used in many state-of-the-art SLAM systems (e.g., \cite{murORB2, murTRO2015}).
Additionally, we choose AlexNet \texttt{conv3} features both with GRP, compressed to 1,064 dimensions, and in original form, since it is a popular choice for ConvNet-based place recognition (i.e. in \cite{sunderhauf_place_2015, Hou2017, BAI2018, KENSHIMOV2017124}).
Note that AlexNet has been modified to only contain up to the \texttt{conv3} layer here for fair testing.
The {\em KITTI Visual Odometry} dataset sequence 00 \cite{Geiger2012CVPR} is used as testing data for the first experiment.
We utilize the 4,541 $376 \times 1241$ stereo pairs from this sequence to construct two subsets, placing all of the left images in the database, and using the right images for querying.
Table \ref{table:exec_time} shows the results of this experiment,
where feature extraction time refers to the time between starting with a raw image and ending with having that image's representation inserted into the respective database.
The query times do not include any descriptor calculation times.
Note that DBoW2 has no GPU implementation, and AlexNet with GRP produces features of the same size as ours, so the query times will be the same.
From Table \ref{table:exec_time}, it is clear that our method is faster than the others for feature extraction when a GPU is used to make forward passes through the net, and is still reasonably fast when using a CPU.
Additionally, our method for querying, though it is simple, outperforms DBoW2 in terms of speed in this experiment.

\begin{table}[!t]
	\caption{Times (in milliseconds) to extract features and query a database of 4,541 images on the KITTI dataset.}
	\centering
	\resizebox{0.5\textwidth}{!}{
	\begin{tabular}{@{\extracolsep{4pt}}lllllll}
		\toprule
		 Method  & \multicolumn{2}{l}{Extract (GPU)} & \multicolumn{2}{l}{Extract (CPU)} & \multicolumn{2}{l}{Query} \\
		         & $\mu$ & $\sigma$                          & $\mu$ & $\sigma$                          & $\mu$ & $\sigma$ \\
		\midrule
		 Ours    & \bf{0.862} & 0.025                    & 44.0  & 2.98                              & \bf{1.47}  & 0.031 \\
		 DBoW2   & N/A        & N/A                          & 15.8  & 3.08                              & 4.25  & 0.547 \\
		 AlexNet (no GRP)& 2.13  & 0.038                             & 405.0   & 17.4                              & 80.8  & 0.708 \\
        AlexNet & 16.6  & 0.658                             & 418.0   & 17.8                              & N/A & N/A \\
		\bottomrule
	\end{tabular}
	\label{table:exec_time}
	}
\end{table}

\begin{figure}[!t]
	\centering
	\includegraphics[width=\columnwidth]{QueryTimeTest}
	\caption{The proposed method performs queries faster than DBoW2 with varying database size.}
	\label{fig:query_time}
\end{figure}

We also test the query speed for a variable-sized database, comparing only to DBoW2 -- the most competative candidate from Table \ref{table:exec_time}.
We use the large {\em St. Lucia} dataset~\cite{Warren2010}, which, similar to KITTI, is a sequence of stereo pairs.
However, this sequence contains over 30,000 stereo pairs, making it very useful for testing a variable database size.
The left images are used for the database, and a subset of the right images is used for querying.
Fig.~\ref{fig:query_time} shows the results of this experiment,
from which it is evident that our querying method is inexpensive, even for very large databases
 -- larger than that created by a typical SLAM system.

\subsection{Online Loop Closure} \label{sec:online}

\begin{figure}[!t]

	\centering
	\subfloat{
	  \centering
	  \includegraphics[width=0.75\columnwidth]{kitti_00_loops}
	  \label{fig:kitti_loops:00}
	} \quad
	\subfloat{
	  \centering
	  \includegraphics[width=0.75\columnwidth]{kitti_05_loops}
	  \label{fig:kitti_loops:05}
	}
	\caption{
		The results of online loop closure using KITTI 00 and 05, respectively.
		The 2D location of the trajectory is represented on the $x$-$y$ plane, and the $z$-axis is the current keyframe number.
	}
	\label{fig:kitti_loops}

\end{figure}

Precision-recall curves are a good metric for binary classification, but they do not fully prove that our method is capable of accurately closing loops in practice.
Therefore, we perform real-time loop closure using an extremely simple application of our model on KITTI~\cite{Geiger2012CVPR} sequences 00 and 05.
In this experiment, we simulate keyframe selection by using every seventh frame for loop detection.
A loop closure hypothesis is proposed if a database query score is above an a-priori threshold $\tau$, and a loop is determined closed if three consecutive queries retrieve descriptors within six frames of the first query.
We exclude the most recent images from the search space, and do not start loop detection until the database is of sufficient size.
We choose $\tau$ from the precisions and recalls shown in Fig.~\ref{fig:gp_day_left_v_right} (bottom) such that it maximizes the recall rate with perfect precision.
Fig.~\ref{fig:kitti_loops} shows the results of this experiment.
Clearly our method is able to consistently close loops on a practical SLAM dataset using a threshold from completely unrelated ground truth data, which shows that it is ready to use in a real-time SLAM system.
Additionally, this application of our model for online loop closure is extremely simple, and can be improved upon easily by looking at the $k$-nearest neighbors instead of the single-nearest neighbor, adding extra false positive rejection methods (i.e. a geometric check), or utilizing any of the methods described in the next section.

\subsection{Integration into ConvNet-Based Place Recognition Systems} \label{sec:improving_performance}

As stated in Section \ref{sec:online_use}, our model can easily be integrated into off-the-shelf ConvNet-based place recognition systems~\cite{sunderhauf_place_2015, KENSHIMOV2017124, Hou2017, BAI2018} for faster feature extraction.
These methods build upon the use of holistic image descriptors, improving performance in different cases.
They treat the ConvNet as a black box for image description -- throwing out image classifications from classification networks.
Many of these methods are forced to reduce the dimension of the ConvNet features to minimize runtime, while
our model already produces a small enough descriptor for real-time use, and is smaller and faster than the typical classification network used by these methods.

To prove this, we reproduce the state-of-the-art landmark-based place recognition system presented in~\cite{sunderhauf_place_2015}, replacing Edge Boxes~\cite{Zitnick2014EdgeBoxes} with BING~\cite{Cheng2014BING} as \citet{Hou2017} did to reduce runtime, and replacing the dimension-reduced AlexNet \texttt{conv3} landmark descriptor with that from our model.
\citet{sunderhauf_place_2015} proposed reducing the AlexNet \texttt{conv3} layer to 1,024 dimensions, while ours is naturally 1,064, so we do not need to reduce it further -- avoiding the cost of the $ 1024 \times 64896 $ by $ 64896 \times m $ matrix multiplication required to project $m$ landmark descriptors (using AlexNet) into 1,024 dimensions, which must be done every time a new image is added to the database.
The results of this experiment can be seen in Fig.~\ref{fig:landmark_method}.
The landmark-based method offers an enormous improvement over the holistic image descriptor -- approaching perfect performance on the Gardens Point daytime dataset.
Our model is seamlessly integrated into this system, which suggests that it can easily replace bloated classification networks in other such ConvNet-based place recognition systems.

\begin{figure}[!t]
	\includegraphics[width=\columnwidth]{landmark_method}
	\caption{The landmark-based method shows a vast improvement over the holistic approach -- nearing perfect performance.}
	\label{fig:landmark_method}

\end{figure}

\section{Conclusions} \label{sec:conclusions}

We have presented a novel unsupervised deep neural network for fast and robust loop closure, applicable in visual SLAM.
Built upon the denoising autoencoder architecture, we apply randomized projective transformations to images
in order to capture extreme variations in viewpoints due to robot motion,
while employing the fixed-length HOG descriptor to help our network better learn the geometry of scenes.
The proposed model allows for vast amounts of data to be used in training, since none of it needs to be labeled or contain any special information.
Furthermore, although our pre-trained model generalizes well in its current state, it is easy to fine-tune or retrain due to our unsupervised design -- increasing the likelihood of improvement as more data becomes available.

We have performed thorough comparison studies on different datasets against the state-of-the-art image description methods for place recognition,
where the extensive experimental results have shown that the proposed deep loop closure method generally outperforms the benchmarks in terms of both effectiveness (precision-recall) and efficiency (runtime).
Our model is compact, robust, and fast -- making it a promising candidate to replace larger, slower classification networks in ConvNet-based place recognition systems, as we have shown by reproducing \cite{sunderhauf_place_2015}.
Due to its lightweight yet robust design, our model is suitable for use in real-time SLAM systems -- in particular, direct algorithms~\cite{dso, lsd_slam, dtam, direct_vins} where no intermediate image representation is needed.
We aim to provide an out-of-the-box solution for loop closure, and, more generally, place recognition.
We are currently working to integrate our model into various SLAM systems, applicable for autonomous navigation in challenging environments.

\bibliographystyle{IEEEtranN}

redpajama_set_name: RedPajamaArXiv


CHICO FLORES claims his row with Swansea team-mate Garry Monk was a "silly argument" which he has attributed to tension over the club's recent form.
Police were called to the club's training ground on Friday when the two centre-halves were involved in a heated exchange as they prepared for their home match against Tottenham on Sunday, which they lost 3-1.
Reports suggested Flores had brandished a brick, but this was denied by the club.
And Flores said on Twitter: "It was just an argument between team-mates. I did not threaten anybody – and even less with a brick.
"My relationship with Garry Monk has always been fantastic and still is – to a point that we spent the afternoon together yesterday.


Minnesota Wild star Zach Parise suffers injury - can the Wild cope?
The Minnesota Wild have to play at least two games without their superstar sniper Zach Parise. The 31-year-old sustained a lower-body injury against the Nashville Predators on November 5.
Parise was hit by Predator's forward James Neal along the boards during his second shift of the game. Neal flew full speed into the hit and his knee made contact with Parise's legs. Parise flew head first into the boards and crumpled onto the ice.
The veteran sniper limped off the ice and onto the bench where he was tended by the Wild's medical staff. Parise played five more shifts during the first period, but he did not return after the first intermission.
The Minnesota Wild will have plenty of line juggling to do in order to make up for Parise's absence. The Wild have moved Charlie Coyle to the first line and moved Jason Pominville to the left wing, in Parise's regular spot. However, the Wild's most productive line of Jason Zucker, Mikko Koivu, and Nino Niederreiter remains the same.
The Wild will also have to rely on the third and fourth lines to step up their production to make up for the lost firepower of Parise. This means that players like Thomas Vanek, Chris Porter, Erik Haula, and Ryan Carter will have to play more aggressively until Parise returns.
Zach Parise was drafted by the New Jersey Devils in 2003 and he has been producing over 56 points per season for his entire NHL career. The talented winger was traded to the Minnesota Wild during the summer of 2012, and his impact was instantaneous.
Although the veteran is not likely to produce over 80 points like he did during his prime in the 2008-2009 and the 2009-2010 seasons, Parise will definitely average around 60 points this year. It will be a devastating blow to any team to lose a player like Parise's caliber.
Hopefully Parise can make a fast recovery, but the Wild will have to try to maintain or improve their record in his absence. The Minnesota Wild will play the Winnipeg Jets on November 10.


China-based TCL is promoting TVs hand over fist, taking market share from Vizio and different massive TV manufacturers within the US. It has grown 60 p.c in every of the final two years, based on market analysis agency NPD, and its Roku TVs proceed to dominate Amazon's best-seller lists.
At Proinertech our favourite TCL is the 6 sequence, with the perfect image high quality for the cash of any TV we have examined. However cheaper fashions, particularly the TCL three and four sequence reviewed right here, are those that stay the preferred.
Let's get one factor out of the way in which first: Their image high quality is mediocre. They can not compete with dearer units for black degree efficiency, distinction or pop. If you would like a house theater-worthy picture in a funds set, begin with the Vizio E sequence — simply remember the fact that it isn't accessible in sizes underneath 43 inches.
Then again, mediocre is likely to be ok for you, particularly if you happen to're shopping for a smaller set for secondary viewing otherwise you simply need the most cost effective sensible TV you will get. So long as you do not anticipate an excessive amount of, you is likely to be completely happy with a TCL three or four sequence, particularly for the value. And if nothing else, I predict you may like its built-in Roku.
There's a variety of completely different fashions in these sequence so earlier than we get into it, here is how they break down.
We have reviewed the S305 and S405 in 2017 and for this evaluation we in contrast them to 2 new 2019 evaluation samples, the 43-inch 43S325 and the 50-inch 50S425. Sure, the cosmetics are barely completely different, with the newer units having black stand legs as a substitute of silver, and barely completely different frames across the image. We additionally noticed some minor variations in picture high quality (see under for particulars). However total not a lot has modified in two years, and the Proinertech rankings are the identical for all of them.
In different phrases, you are advantageous shopping for the 2017 (S305 and S405) variations for so long as they continue to be available on the market. TCL's consultant stated they'd be slowly phased out and changed by the newer fashions (S325 and S425) this 12 months.
This is the place I point out that the S305 and S325 fashions have 720p within the 32-inch dimension, and 1080p decision (aka full HD) within the 40- and 43-inch sizes, and so they cannot do excessive dynamic vary (HDR). In the meantime the S405 and S425 fashions have 4K decision and HDR functionality.
As you may see on the chart, for many sizes there is not any overlap: The 32- and 40-inch sizes are HD solely, whereas the 50-, 55- and 65-inch sizes are 4K HDR solely. Most individuals select a TV dimension first, then fear about all the pieces else, so there's not a lot of a alternative in these sizes.
The place sizes overlap (43- and 49-inch) there's sometimes $30 to $70 distinction. For many consumers on this worth vary, I do not assume it is price paying that distinction. You are higher off saving the cash and getting the 1080p, HD, non-HDR variations as a substitute of the 4K HDR variations. Sure, you could possibly see some enchancment in picture high quality with some 4K HDR materials, however it is going to be minor at finest. See the picture high quality part under for extra.
The Roku TV distant is super-simple.
The perfect factor in regards to the three and four sequence TVs is built-in Roku. It offers you dead-simple entry to simply about each streaming app accessible, together with Netflix, YouTube, Amazon Prime Video, Hulu, Sling TV, Pluto TV and extra.
For the reason that apps are built-in, you will get to them sooner and simpler than through an exterior streamer, which requires switching inputs and doubtless juggling a second distant. In fact you may join different gear (like sport consoles or Blu-ray gamers) to those Roku TVs too, and so they have some cool options for individuals who use an over-the-air antenna to get free TV.
Roku TV's most important competitor is Amazon's Fireplace TV Version units by Toshiba and Insignia. Amazon has its benefits, particularly in the case of voice management with Alexa. However I nonetheless like Roku higher total as a result of its menu system is extra impartial — it does not force-feed you Amazon Prime TV reveals and films.


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This is a refurbished RC car. This RC car comes with a 45 day true warranty. We have our highly trained technical staff repair and test our refurbished products to make sure they are in perfect working order. If you're a fan of Toyota's then you need to do yourself a favor and get the Licensed Toyota FJ Cruiser Electric RC Car. This fantastic RC is officially licensed and features an impressive amount of detail. The high gloss finish, the rubber tires, and the chrome wheels are only some of the things that really make this RC stand out. The full function radio control will let you steer the car in any direction, so you'll be zipping around corners in no time. If you're looking for an authentic and realistic RC then the Licensed Toyota FJ Cruiser Electric RC Car is a must have. Get yours today and tear up the streets!
Sorry the Refurbished Licensed Toyota FJ Cruiser 1:24 Electric RTR RC Car is currently out of stock.


Phoenix Wright and co. are making the leap to an animated TV series. Here's the debut trailer.
Well, what do you think? The anime will debut April 2 in Japan.
In case you missed it, here is Kotaku's review of the Ace Attorney feature film.


This homeowner not only wanted to give back to his local environment and boost the area's biodiversity, but create a better view from his house. There was only one way to do that on a flat roof - install a beautiful green roof!
We supplied the ANS GrufeKit Green Roof System to this enthusiastic homeowner. The simple self-install system meant that it was a perfect choice as creating a great looking flat roof did not require in this case a costly installation team.


Required days of instruction and required subjects for homeschools in Puerto Rico are essentially the same as for Puerto Rico's public schools. Homeschool teachers in Puerto Rico are not required to be certified, licensed or otherwise hold any specific degrees to qualify to teach in a homeschool. As non-governmental schools, homeschools in Puerto Rico are not required to administer any form of standardized testing to students.
As "non-government schools," home schools in Puerto Rico are not recognized or regulated by law. However, a "primary or secondary academic" school which "declares, promises, announces or expresses the intention of granting certificates, diplomas, degrees or licenses" must be licensed (18 L.P.R.A. Section 2101-2103).
As "non-government schools," homeschools in Puerto Rico do not grant high school diplomas but do issue transcripts demonstrating that the student has completed relevant educational studies.


Please note that the $40 deposit is due for this Walk as soon as possible after both the Candidate and Mentor applications are sent (the remaining $100 can be paid at any point before or on the evening of the Walk). Checks/money orders should be made payable to SWVA Emmaus. In the event you must cancel, please notify the Registrar as soon as possible (using the contact info below) so that you may be rescheduled. In the event of cancellation, the deposit will be refunded and you will be notified by phone, mail, or email prior to the Walk date.


As the built environment evolves, the impact of water will always be a vital consideration. We work with our clients to assess, model and design ways to de-risk water and expedite projects.
Waterco help clients understand, mitigate and reduce the impact of a development on the water cycle, the environment and new and existing water and drainage infrastructure. This is achieved through Assessment, Modelling and Detailed Design.
Waterco have a reputation for quality work, delivered by proactive, innovative and friendly people. We have the latest technology and a wide range of expertise in our team, including geo-environmental experts. Discover how we have helped our clients achieve their objectives using our services.
At Waterco our company vision is to improve the world with smart and sustainable environmental and water management.
Water Consulting Services throughout Wales, North Wales, North West, Manchester, Chester and London.


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Q: Ограничение по дате в оконной функции PostgreSQL Есть таблица с данными за весь 2021 и 2022 год. Нужно посчитать кумулятивную сумму поля по дате за предыдущий год. То есть нужно, например, чтобы у строки 2022-03-01 было значение кумулятивной суммы за 2021-03-01
пока пробую данную оконную функцию:
SUM(fact_mln) OVER(PARTITION BY date - INTERVAL '1 year' ORDER BY date)
Но метод - INTERVAl '1 year' не работает
Как можно преобразовать данную оконную функцию или может есть какие-либо решения ещё?

redpajama_set_name: RedPajamaStackExchange


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Q: Android Background threads: Difference between launching from an Activity and from a Service Is there any difference in running background threads from an Activity and from a Service that is started by the Activity? Added: The background threads do not interact with the UI.
I currently have some background threads that are launched from the Activity. Most are via AsyncTask and one is through my own ExecutorService. I would like to know if there is a significant benefit to refactoring the code to move these to a Service or IntentService.

A: You seem to be confused about the definition of Activities and Services.  To make it clear:

*

*Activities are things that run according to the Activity lifecycle state machine.  The code in the respective handlers interacts with an event loop attached to a UI.

*Services are things that run according to the Service lifecycle state machine.  The code in the respective lifecycle handlers performs operations to handle things like Intents, etc..., but does not interact with the user via a UI.

Both of these, however, run on the "main thread" of the application.  By itself, an Activity or a Service (or Broadcast Receiver, Content provider, etc...) is not a thread.  Look at the documentation, and you will see that the Activity and Service classes do not, in fact, form a thread.  Instead, they are hooks that will run inside the Android framework, and the framework will at the appropriate time call them on the "main" thread of the app.
You can create separate threads for the app, or use an AsyncTask to do work and publish it to the UI thread easily (something not so easily achieved with a Service).

A: Threads that are bound to Activities have the same lifecycle. So if you restart/kill the Activity, the thread will also be restarted/killed. This is a problem if you don't manage the lifecycle of an Activity. A service is good in this case. You can have the activity destroyed and still a worker thread running in the background (in the service). But be advised that, if the Android system need resources (memory for example) it will kill the services first (and then restart them according to their Sticky flag). In my opinion, there are no actual benefit in changing threads from the Activity to a Service, since you control the workflow of your activity. If the threads are heavy (and brake the UI for moments) consider putting them in a service on a separate process (in AndroidManifest put the process name of the service).
In Android documentation:

Caution: Another problem you might encounter when using a worker
  thread is unexpected restarts in your activity due to a runtime
  configuration change (such as when the user changes the screen
  orientation), which may destroy your worker thread. To see how you can
  persist your task during one of these restarts and how to properly
  cancel the task when the activity is destroyed, see the source code
  for the Shelves sample application.


Much of the politically attuned public's attention during the 2014 election season has focused on the battle for majority control of the U.S. Senate. But there are also important elections this year in the states. Thirty-six states will be voting for Governor on November 4, with eleven of these races considered Toss Ups by the Cook Political Report. On the same day 6,049 of the nation's 7,383 state legislative seats will be on the ballot, according to the National Conference of State Legislatures. Governing Magazine sees 18 of the country's 98 partisan legislative chambers as being vulnerable to a change in majority control.
With this degree of competition, it is worth asking where the money will come from. Some clues can be found in the elections just past. Today the Campaign Finance Institute is releasing its analysis of the sources of campaign funding in the 2012 elections for state office, based on data supplied by the National Institute on Money in State Politics (NIMSP). This continues a series that CFI began with the elections of 2006.
MAJOR DONORS AND PACS: With so small a number of people giving, the bulk of the candidates' funds came from non-party organizations (generally PACs) and from individuals who gave $1,000 or more. In the median states (North Carolina and New Hampshire) these sources accounted for 65% of the candidates' total fundraising from private sources, excluding self-financing (see Table 2). In five states the figure was above 80% (Georgia, Missouri, Utah, Texas and Nevada).
SMALL DONORS: Connecticut and Minnesota led the states in their candidates' reliance on small donors. Five other states made up a strong second tier.
Connecticut's candidates received 90% of their private funds from donors who gave $250 or less. The state offers voluntary full public funding to candidates; most legislative candidates in 2012 participated. Candidates who participate have to adhere to a $100 contribution limit when raising the money that qualifies them for the public grant. This explains the state's high percentage from small donors.
Minnesota's candidates received 66% of their privately raised money from donors who gave $250 or less. The state has a partial public financing system with voluntary spending limits. Most Democratic and Republican candidates participated. Minnesota also offers donors up to a $50 annual rebate (suspended 2009-13) for contributing to candidates who accept public funds or to political parties.
In five other states, candidates received 40% or more of their funds from donors who gave $250 or less. The states were Wisconsin (49%), Massachusetts (43%), Rhode Island (42%), Maine (42%), Arizona (42%), and Montana (41%).
Washington, Kansas and Kentucky were the median states, with 16% of candidates' funds contributed by donors who gave $250-or-less. Twelve states were below 10%.
NOTE ON METHODOLOGY: To derive the percentage of a state's voting age population (VAP) that gives to candidates, CFI began by counting the number of unique donors to candidates in each of the relevant elections. The records were obtained from the National Institute on Money in State Politics, which collects, standardizes and produces electronic files for each of the states. Itemized donors who gave to more than one candidate (or more than once to a single candidate) were counted only once. Unitemized donors had to be estimated. CFI assumed that each unitemized donor gave half of the state's disclosure threshold. Thus in the median state, with a disclosure threshold of $50, CFI assumed an average of $25. CFI then divided the number of unique donors in each state by the state's VAP in the election year as reported by the U.S. Bureau of the Census, including non-citizens. The results are reported as the percentage of VAP who were unique donors in each state.


Former Seattle School District administrator Dan Riley and his wife suffered life-threatening injuries in a car accident Thursday and eventually died at Harborview Medical Center. According to reports, the couple was driving in their station wagon in Wallingford Thursday when Mr. Riley lost control and crashed into a tree.
Both were taken to Harborview Medical Center to be treated for their injuries, where they later died within hours of each other. Mr. Riley was 91 and his wife, Rose, was 93 when they passed away.
They leave behind six surviving children and ten grandchildren. The Davis Law Group sends our sincerest condolences to the family and loved ones of Mr. and Mrs. Riley.


HPE Foundation Care 24x7 Service — offers 24x7 service, including on Hewlett Packard Enterprise holidays, with a four-hour on-site response time for hardware and a two-hour response time for software.
HPE Foundation Care 24×7 Extended Service Agreement with 5 years – on-site allows access to electronic support information and services and escalation management.
HPE Foundation Care 24×7 service provides electronic access to related product and support information, enabling any member of your IT staff to locate this commercially available essential information.


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Duane A. Gamble is a retired United States Army major general who, as a lieutenant general, served as the deputy chief of staff for logistics of the United States Army from September 2019 to April 2022. Previously, he was the commanding general of the United States Army Sustainment Command from July 2017 to August 2019.

Suspension
Gamble was suspended from his position as the deputy chief of staff for logistics on February 16, 2022 "pending the final outcome of a Department of the Army inspector general investigation into allegations of creating or fostering a counterproductive leadership environment". Once the investigation substantiated that Gamble engaged in counterproductive leadership, he was officially relieved of duty and reassigned as a special assistant to the director of the Army Staff in April 2022, reverting to his permanent rank of major general.

References

Lieutenant generals
Living people
Place of birth missing (living people)
Recipients of the Distinguished Service Medal (US Army)
Recipients of the Legion of Merit
United States Army generals
United States Army personnel of the Gulf War
United States Army personnel of the Iraq War
United States Army personnel of the War in Afghanistan (2001–2021)
Year of birth missing (living people)


sprint we hope to have up to date working 32 and 64 bit versions.
Stackless, you would be more than welcome in joining us to help out.
the sprint, taking time away from the PyPy sprint.
to reply, whether here, to this email address or to richard at ccpgames.com.


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Bellevue Historic District ist der Name folgender im NRHP gelisteten Objekte:

 Bellevue Historic District (Idaho), NRHP-Nr. 82002506
 Bellevue Historic District (South Carolina), NRHP-Nr. 97001206


Anyone thought about what's happening outside the artbox (artboard)?
Every project has some hidden dirty secrets. Let's reveal all of them!
This is a tribute to all forgotten shapes, cut forms, etc. placed outside the canvas.
Every project has some hidden dirty secrets.


We look at the ownership model at AC Milan, and the rumours about the wealth of owner, Li Yonghong.
We take a look at the ownership of AC Milan. Once owned by the infamous Italian, ex-politician Silvio Berlusconi, the club changed hands in 2017 to Chinese businessman Li Yonghong. Since then, various news outlets have alleged that Yonghong's wealth isn't what he has claimed.
Chris Weir recalls Demetrio Albertini's glittering career and examines the person behind the player.
We look at Shakhtar Donetsk owner Rinat Akhmetov and chart his rise to power.


WikiMediation Partners. (2009, May 30). WikiMediation, . Retrieved 20:10, April 22, 2019 from http://en.wikimediation.org/index.php?title=WikiMediation_Partners&oldid=1261.
"WikiMediation Partners." WikiMediation, . 30 May 2009, 15:58 UTC. 22 Apr 2019, 20:10 <http://en.wikimediation.org/index.php?title=WikiMediation_Partners&oldid=1261>.
WikiMediation contributors, "WikiMediation Partners," WikiMediation, , http://en.wikimediation.org/index.php?title=WikiMediation_Partners&oldid=1261 (accessed April 22, 2019).
WikiMediation contributors. WikiMediation Partners [Internet]. WikiMediation, ; 2009 May 30, 15:58 UTC [cited 2019 Apr 22]. Available from: http://en.wikimediation.org/index.php?title=WikiMediation_Partners&oldid=1261.
WikiMediation Partners, http://en.wikimediation.org/index.php?title=WikiMediation_Partners&oldid=1261 (last visited April 22, 2019).


☝ Serigrafia SIGN FutureTEXTIL 2018 will be held in São Paulo.The exhibition will bring the latest information and technology in the advertising and digital printing industry. It is the most comprehensive exhibition. During the four-day exhibition, it is expected to have nearly 36,000 visitors.
develop the market in South America ,and make a good visual experience for customers,we customize the booth for the first time in overseas exhibitions.
☝ As a high-tech enterprise with more than 30 years experience,we pay attention to both domestic and overseas market.For now,there are customers from more than 80 countries. We committed ourselves to offering higher quality products and serving the customers better all the time,and will do the same in the future.


Reference 1524 — Very nice villa offering 4 bedrooms and nice furnished independant studio. All confort, nice features, stunning view. Swimming pool and teraces!
Services : Sliding windows, Fireplace, Double glazing, Irrigation sprinkler, Barbecue, Swimming pool.


Meet The Team - Why Remax?
Asking "Why RE/MAX?" It could be the start of a life-changing conversation.
Behind the RE/MAX offering is a skilled and accessible team committed to helping you realise your career objectives. You will benefit from the experience and knowledge of highly specialised professionals at hand who will assist you in building your brand or business.
Copyright © 2019 Why Remax?. All rights reserved.


Greenbelt, Maryland – U.S. District Judge Theodore D. Chuang sentenced Monica Kendrick, age 47, of Walkersville, Maryland, today to 27 months in federal prison, followed by three years of supervised release, for wire fraud arising from a scheme in which she embezzled money from her employer. Judge Chuang also ordered Kendrick pay restitution of $271,921.06, the total amount of loss resulting from the scheme.
The sentence was announced by United States Attorney for the District of Maryland Rod J. Rosenstein; Special Agent in Charge Brian Ebert of the United States Secret Service - Washington Field Office; and Chief J. Thomas Manger of the Montgomery County Police Department.
According to her plea agreement, In May 2014, Kendrick was hired as the office and finance administrator for a non-profit organization located in Silver Spring, Maryland, which acted as a trade association for the herbal products industry. Almost immediately after being hired, Kendrick began stealing money from the company. From May 2014 to November 2015, Kendrick wrote over 80 unauthorized checks from the company and deposited the fraudulent checks into a bank account. She then withdrew the illicit proceeds from the account.
United States Attorney Rod J. Rosenstein commended the U.S. Secret Service and Montgomery County Police Department for their work in the investigation. Mr. Rosenstein thanked Assistant U.S. Attorney Leah Jo Bressack, who prosecuted the case.


Welcome to the 18th Annual IAM Conference which will be hosted by the National University of Ireland, Galway (NUI Galway) from 3-4 September 2015. The conference will be preceded by the Doctoral Colloquium 2 September 2015. The IAM conference is a blind peer-reviewed international conference.
The Doctor Colloquium takes place 2nd September 2015 from 8.15am – 5.00pm. The conference takes place 3rd and 4th September 2015. The full schedule of speakers, symposia, parallel tracks and presentations is available here.
To improve business networking, the 2015 Irish Academy of Management Conference is pleased to adopt the fuseami networking app. fuseami is a revolutionary new mobile app to help you to business network smarter. Discover and connect with those other professionals that are most relevant to you at this conference.
Simply – download, login with your LinkedIn account, choose the 2015 Irish Academy of Management Conference and start networking with other delegates.
Since its establishment in 1845, NUI Galway has developed a strong tradition of research and scholarship within an academic environment that emphasises the student experience. From an initial intake of 63 students in 1849, NUI Galway is now a thriving community of 17,000 students, of which over 2,000 are international students, representing some 92 countries across the globe. NUI Galway is a leading international, research-intensive university, ranked among the top 250 universities in the world. Many of NUI Galway's alumni and academics have become leaders in their fields in Ireland and far beyond, including physicist, George Johnstone Stoney, who discovered and named the electron; Alice Perry, the first woman engineer in the world; many leaders in political and public life, including President of Ireland, Michael D. Higgins; writers and actors, John McGahern, Thomas Kilroy and the founders of the renowned Druid Theatre Company.
The J.E. Cairnes School of Business and Economics at NUI Galway has grown rapidly in recent years to over 2,100 undergraduate and postgraduate students and a teaching staff of over 60 spread across the Disciplines of Accountancy and Finance, Business Information Systems, Economics, Management and Marketing. The School has built a reputation for teaching and research excellence, confirmed by external quality evaluations and accreditations (including AMBA, EPAS and CIPD), as well as wide-ranging exemptions for major accounting bodies. The School offers a wide range of programmes, from undergraduate degrees to executive education, tailored to deliver the specific professional skills required to succeed in a globalised and competitive environment. The School's outstanding faculty are recognised as experts in their field by practitioners and by other academics, and its ambitious and motivated students come from over 40 countries. The School's graduates are highly employable and many of its 14,000+ commerce, business and management alumni have become leaders in their fields.
The IAM Conference also provides an excellent opportunity for delegates to visit Galway city and its hinterland. Galway is located on the West Coast of Ireland, which sits on the River Corrib between Lough Corrib and Galway Bay. Galway is a vibrant, cosmopolitan city steeped in culture, rich in folklore and famous for its cobble-stoned streets, quaint shops, charming restaurants, and is known as Ireland's Cultural Heart and is renowned for its vibrant lifestyle and numerous festivals, celebrations and events.
Home to the world-renowned Druid Theatre Company, Macnas Street Theatre Company and An Taibhdhearc, there is much to do in this wonderful city.
From the prehistoric fort of Dun Aengus on the Arann Islands, the most westerly point in Europe, to Connemara and the Burren, Galway boasts some of the most beautiful scenery in the world.
This video provides a brief snapshot of some of what Galway city and its hinterland region has to offer. Further information on Ireland's idyllic West Coast region is available here and the Wild Atlantic Way is a world-famous coastal route that spans seven of Ireland's counties, with some breath-taking scenery along the way from Donegal in the north to Cork in the south and taking in Counties Sligo, Galway, Clare, Limerick and Kerry, the Wild Atlantic Way is a journey of discovery.
See the Travel and Accommodation webpage for more information on Galway and its hinterland.
This video provides a brief snapshot of some of what the city and region has to offer. Further information on Ireland's idyllic West Coast region is available here.
We look forward to welcoming you to Galway.