I am currently stuck in a place where I look like a child and often get treated as such but I'm actually (unfortunately) an adult.
I'm 27 – teetering on the edge of mid to late 20s (some say late, I like to think mid) and heading towards the slippery slope that will hurtle me towards 30 and actual, real, can't escape it any longer – adulthood. HOW and when did this happen?
Sometimes I feel like I blinked and became this age, without ever actually learning how to be this age. I really don't know how to adult! But, that doesn't mean I don't want to be treated like one.
Just the other day I was at the dentist and telling her I had moved in with my boyfriend, to which she chuckled and replied "bit young for that aren't you…" By this time she had her hands in my mouth so I never actually found out if she really thought 27 was too young, or if after I left she whispered to her assistant 'at this rate she will be a teenage mum by the time she is 18'.
older and therefore be able to do more. It is only when you get to that age, that you wish you could slow it down and, although I would still like to look (slightly) older than I do, you start to wish you could get back some of those years you once wished away.
A boy (who looked half my age, although I could be doing the same thing others do to me) came to the door of MY flat to try and sign me up to donate to charity and asked if my parents were in. When I told him it was my flat and how I old I was, he literally laughed in my face and looked as if he definitely did not believe me.
This happens regularly, despite showing my ID I always seem to get a look of pity, like I'm obviously not old enough but I have done such a good job getting hold of fake ID that they will let me in the bar/buy the alcohol anyway.
On one night out my best friend, who is in fact 2 days younger than me, got asked if she was my mum. Not sure who was more insulted/found it funnier, me or her.
I, like many tweenagers, wore make up, clothes and shoes to make myself look older than I was. Now I end up looking like some kind of frumpy teenager still appearing to be dressing up to look older. Alongside this people think you are an innocent goody two-shoes because of your child-like face, "Oh, you don't look like the sort to *drink your weight in vodka*?
You can't win. Get dressed up – get ID'd. Don't get dressed up – get ID'd Leaving the house without said ID, not an option. Unless you want to be that person looking desperate as you try to persuade the bouncer you really have been legally able to drink for 10 years! And you will definitely still experience the fear of trying to buy something with an age restriction even when you do have ID in hand.
However, looking young definitely doesn't mean you feel young. Whilst always getting ID'd to go into the club, I get the hangovers of someone 10 years older, if not more. Wine is not my friend – but there are some things we just never learn how ever old we get. You start to realise that you have taken your youthful appearance for granted and suddenly it becomes apparent you are in fact aging, although perhaps not facially, without having realised it.
You get told the same old thing, "you'll be grateful when your older", however I have always joked I will get to 30 and suddenly age dramatically, getting mistaken for being in my 40s or 50s. Only then will I wish people mistook me for being years younger than my age.
Sometimes I think I should just accept it, this could work to my advantage – child's ticket on the train? But would it work on those occasions, probably not!
I think where we are at in our lives can determine how old we feel, not a number. For me I only began to feel like an adult when I moved out of home. But, that being said, that little number does, of course, mean quite a lot.
10 more years and I might finally look my age, 27.


C-Trak has just finished a system installed into a large recycling plant. It pays to be green these days why not take a look at our video footage showing waste being conveyed at the plant and emptied into large containers.
C-Trak will be closing down for the Easter break from 4.30pm Thursday the 5th April until 9am on the 10th April.
All calls will be diverted and picked up by one of our sales engineers so no need to worry.
Any emergencies applications can be dealt with. C-Trak is always here to help support our customer in any way we can.
We would like to wish all of our customers a very Happy Easter and hope you all recharge your batteries in the long bank holiday weekend.
Just to let all the followers of our new blog know we have now changed the blog so it is easy to follow.
We want to keep our customers upto date with the latest technology on the market for their material handling using our bespoke conveyors. Our blog has been running since 2009 and we would like to welcome you to the new look.
Please contact us if you would like to add your new conveyor products to our news blog.
Our client often specify what type of conveyor belting they want on their conveyors. One that comes up time and time again is Intralox Belting as they are well known for the quality of their belts.
If your looking for a competent conveyor company to supply you with a fruit, salad or vegetable conveying systems then contact C-Trak Ltd on 01525 850316.
Expert advice and problem solving for your food handling process.
New design for cleaning belts and removal has just been launched for total cleanliness of your conveyors.
Our Fruit Conveyors will handle your products with care making sure no bruising occurs.
This was a project that C-Trak took on for a reversible conveyor application for more information just follow C-Trak this will show you more photo's and video's.
Having problems finding a conveyor company that can provide you with a belt conveyor that can withstand high temperatures. Well you need look no further.
C-Trak have new heat resistant belting that can withstand temperatures up-to 300 degrees, we have just finished a particular job that needed to transport liquid plastic to go through a spray booth. We provided a cost effective solution for this company which has saved them time which of course mean money savings on their production line.
C-Trak has just had an enquiry for extensive work for a modular magnetic conveyor system. Which includes 2 plastic modular belts plus extension of magnetic modular belt conveyor with rework.
This is a typical type of conveyor application that C-Trak undertakes.
Have you got a Conveyor Problem?
Let us help you with your material handling we have very experience sales engineers that can come out to your premises and and help with a solution for your products. We have been manufacturing Conveyor Systems for over 30 years and your problems are our business.
What is a modular belt conveyor. Well it is a bit like lego made up of plastic parts clicking together to form a woven belt of plastic links.
C-Trak Conveyors can provide you with the ultimate Conveyor System that will last for years.


One of the early arrivals for 2012, Mass Effect 3 is set to complete the journey of Commander Shepherd, putting him in one last final battle against the Reapers as they launch a massive attack against humanity. Don't know who the Reapers are? Check out our coverage of Mass Effect 2, which we still reckon is one of the best RPGs of recent years.
Bioware hasn't been timid with its plans for Mass Effect 3, controversially introducing multiplayer components into the game, then linking progress between the single and multiplayer aspects on top of that. As if that weren't enough, our hands-on time with the game indicates it's leaning heavily towards the violent side of the Action RPG genre, with the stats further streamline and more explosions than ever before.
While we're quietly cautious about how Mass Effect 3 will ultimately turn out, we can't deny that we're looking forward to the scale of the finale though. We've literally waited years to see the ultimate fate of Shepherd and his comrades - so fingers crossed that Bioware don't disappoint!
Ubisoft seems to have scaled down the Far Cry 3 hype-machine since we last saw the game in Germany last year, which is a little surprising given the boldness with which the game was announced. The footage which initially seeped out suggested a bombastic tone with a linear story; an ironic far cry from the immediately previous title.
Since we've not had a chance to go hands on with Far Cry 3 yet ourselves, it's hard to know how Ubisoft is shaping the game at this point. The last we saw of it there was talk of RPG systems being finally woven in alongside the action, which takes place on a tropical island apparently full of psycho-mercenaries. How far the stats will extend though, we can't say.
That said, what we have seen of Far Cry 3 presents a potentially very exciting future for the series - as well as showing that Ubisoft isn't afraid to tinker with each and every aspect of the series in pursuit of perfection.
Summing up what we know about BioShock: Infinite isn't easy, as the immediate assumption which everyone wants to make is that it's 'just the original BioShock but on a blimp' - and combating that takes a lot of talking. Believe it or not though, 2K Games would seem to have grander plans for Infinite than the naysayers might suggest.
Set on a floating city spread across a number of track-connected blimps, Infinite is essentially a rescue story. Disgraced detective Booker DeWitt is tasked with liberating a young woman from the skyborne fortress, but quickly finds out that there's more at stake than one woman's fate. Tears in reality, a genetically altered populace and a raging class war are just some of the issues that Booker will have to contend with.
Parallels to the original BioShock games are admittedly obvious; huge robotic enemies and plasmids show up, albeit under different names, and there's no getting away from the aesthetic similarities either. Still, even if Infinite does turn out to be nothing more than BioShock in the sky, we don't think that's at all a bad thing.


text:
import {Component} from '@angular/core';

@Component({
    templateUrl: 'app/calfWelfare/calfWelfare.component.html'
})
export class CalfWelfareComponent{

}

redpajama_set_name: RedPajamaGithub


If you're involved in a claim it's important you understand how the Weathertight Homes Tribunal works. To help with this we run seminars that explain the process in detail.
If you can't attend a seminar, we can send you an information pack or make an appointment for you to meet with a case manager.


text:
\section{Introduction}
Interestingness recognition is a crucial component of autonomous mobile robot exploration. Robots should perceive the surroundings by processing visual information quickly and estimate the interestingness of the scenes to make decisions for next actions and plans. Take a robot that explores underground tunnel as an example (first row of \fref{intro-figure}). While an uninteresting scene like \fref{intro-figure} (a) may not change the course of the robot actions, interesting scenes that contain the wooden door as in \fref{intro-figure} (b) or a dark entrance hole as in \fref{intro-figure} (c) would affect the next actions of the robots, such as moving towards the door or hole.

There have been various approaches to modeling visual interestingness using the concepts of saliency \cite{borji-et-al, learning-saliency-based-attention}, memorability \cite{photo-memorable}, and novelty detection \cite{novelty-detection}. However, these previous algorithms were not apt for robotic applications, because they were unable to adapt to unknown environments quickly and perform real-time responses to the rapidly changing scenes. Recently, Wang \textit{et al.}~ proposed an unsupervised learning approach that detects visually interesting scenes in an \textit{online} manner \cite{eccv, wang2021unsupervised}. The core of this algorithm is the use of a visual memory module that adapts to the surrounding environment quickly and remembers input scenes efficiently, so that the robot can recall the past and detect novel, unusual scenes as interesting in real-time \cite{wang2021unsupervised}. Another notable feature is that, even if the robot once detects a specific scene as interesting, it loses interest over following similar scenes gradually if they appear repetitively, just like humans lose interest when exposed with a series of similar scenes.

\begin{figure}[!t]
    \centering
    \includegraphics[width=1.0\linewidth]{Figures/fig1.png}
    \caption{Exemplary images from robots' exploration in the DARPA Subterranean Challenge. Some scenes, {e.g.},~ (a) are uninteresting, while there are interesting scenes {e.g.},~ (b), (c), (d), (e), and (f).}
    \label{intro-figure}
\end{figure}

However, one limitation of this algorithm is that it does not have the capability to adapt to human-informed interesting objects. Take this scenario as an example: an unmanned aerial vehicle (UAV) is exploring an underground tunnel and encounters scenes that look interesting and contain different objects as shown in \fref{intro-figure} (d), (e), and (f). The previous unsupervised online learning algorithm \cite{eccv} enables the UAV to detect these scenes as interesting, but it can't specify which objects within the scenes make them actually interesting. Depending on the missions and human operator's interests, the targeted interesting objects might differ with respect to the context. To solve this problem, we aim to take human operators' feedback into account for better recognition of interesting scenes and objects.

\begin{figure*}[!t]
    \centering
    \includegraphics[width=1.0\textwidth]{Figures/diagram.pdf}
    \caption{Diagram of AirInteraction. The robot explorer perceives continuous streams of images, which first go through the unsupervised visual interestingness module. The interesting scenes detected by the visual memory are sent to the base station, where the human annotator can decide if the image is interesting or not. Uninteresting images are sent back to the robot and written into the memory, which prevents the false judgement of similar scenes afterwards. The interesting objects in the real interesting images are annotated by the human (drawing tool is given). Then the last layer of the few-shot object detector is fine-tuned on the novel object using the provided annotations. The updated weights of the final layer are copied to the classifier onboard periodically, so that the robot can detect novel interesting objects in real-time during the mission.}
    \label{fig:diagram}
\end{figure*}

The challenge is that, before a mission starts, \textit{both} the robot and human operator may not have information about what the interesting objects would be. During the exploration, the robot may discover a completely novel object, which is possibly uncommon for existing large scale training set \cite{MSCOCO} and even could belong to an unknown category to the human operator. However, it could still be interesting to the operator, and there is a need for the robot to learn about the object quickly \textit{during the mission} and detect similar objects in the later phase of the exploration. Hence, traditional object detection approaches \cite{faster-rcnn,yolo,ssd} trained for fixed and predefined categories on existing large scale datasets \cite{MSCOCO} are inapplicable to this situation.

We suppose that human's few online inputs on the object-specificity, ({e.g.},~ certain objects or specific image parts that make the scene interesting), can be of great assistance to robot exploration.
Specifically, when a robot detects an interesting scene (image) online, it sends the potential interesting image to a human operator at the base station.
When the human operator thinks an image is interesting, the operator is prompted to draw a bounding box around the interesting object/part within the image.
This image and bounding box annotation are fed into a few-shot object detector \cite{TFA}, which can quickly learn an object detection model only using a very few labeled examples. Since human annotations can be expensive, we restrict the model to learn novel, unseen objects using just one, two, or three examples per category.

In summary, our contributions are:
\begin{itemize}
\item To solve the problem of existing algorithm that can only detect predefined object categories, we propose a human-interactive framework, AirInteraction, for autonomous robots to detect visually interesting and human-informed objects during online exploration.
\item To reduce human efforts and communication bandwidth, we design three components: (1) an online unsupervised algorithm running on the exploration robot to detect visually interesting scenes, (2) a human-robot interaction interface running on a base-station to process operator's feedback for interesting objects, and (3) a few-shot object detector running across the robot and the base-station to enable fast training of human-informed objects during the robotic mission.
\item We test the effectiveness of AirInteraction on various challenging datasets and perform ablation studies to verify the necessity of each component.
\end{itemize}

In \sref{sec:background}, we review prior works on robotic interestingness and few-shot object detection. Our new method, human-in-the-loop online visual interestingness with few-shot object detection is presented in \sref{sec:method}.
The experimental setup, results of our method and baselines, and the ablation study are shown in \sref{sec:experiment}, followed by the limitation of our framework and possible solutions in \sref{sec:limitation}.

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

\subsection{Visual Interestingness}

Although prior works \cite{what-is-interesting}\cite{interestingness-survey} have called the extent to which an image/video captivates or holds one's attention as visual interestingness, the strict definition and quantification of visual interestingness hasn't been globally agreed yet. Since the notion of interestingness is inherently subjective and non-trivial \cite{interestingness-survey}, there have been many different approaches to model visual interestingness using various concepts. Isola \textit{et al.}~ \cite{photo-memorable} and Bylinskii \textit{et al.}~ \cite{bylinskii} focused on the concept of  memorability, investigating the intrinsic and extrinsic factors that make images remembered by humans' memory. Borji \textit{et al.}~ \cite{borji-et-al} and Zhao \textit{et al.}~ \cite{learning-saliency-based-attention} focused on the concept of saliency, investigating features of subparts, objects, or regions within an image that stand out from neighboring parts, and reviewed saliency-based visual attention that drives the shift and focus of human gaze. Gygli \textit{et al.}~ \cite{Gygli2013} proposed computational evidence for different visual cues that constitute interestingness of images, especially focusing on aesthetics, unusualness, and preferences. Hsieh \textit{et al.}~ \cite{social-interesting} examined the interaction between visual and social interestingness, focusing on virality, popularity, and aesthetics features. However, these aforementioned works have focused on \textit{static} properties of images, which is not suitable for situations that require adaptation to the changing distributions.
Previous works \cite{eccv,wang2021unsupervised} proposed a new concept of visual interestingness for robot exploration with online adaptation to changing distributions.
It can report the potentially interesting frames, but fail to detect user-informed objects in real-time. In this paper, we will solve this problem by incorporating a few-shot object detection model.

\subsection{Few-Shot Object Detection}
Object detection aims to estimate the locations and predefined categories of objects in an image. With the emergence of deep learning-based methods, there have been rapid progress of object detection algorithms, mainly divided into two branches of algorithms: (1) two-stage approaches \cite{fast-rcnn,faster-rcnn,R-FCN,mask-rcnn} that separate region proposal generation and proposal classification/regression, (2) one-stage approaches \cite{yolo,ssd} that design an end-to-end neural network to tackle the problem. However, the limitation of the general object detection is the requirement of the large scale annotated training data. Moreover, the object categories are fixed and predefined after training.

Along with the recent progress in few-shot learning \cite{vinyals2016,maml} that learns to generalize with only a few novel data, there have emerged various approaches to tackling few-shot object detection task. A few-shot detector aims to detect novel object classes using merely a few labeled examples. One paradigm for few-shot object detection is transfer learning \cite{TFA, LSTD, mpsr}, which pretrains a base model on base classes using abundant data, and adapts the model via fine-tuning on few labeled examples. Another paradigm is meta-learning \cite{metarcnn, feature-reweighting, ONCE, meta-detect-rare,airdet}, which aims to extract meta-level knowledge during base training that enables efficient learning on novel classes with a few examples.

\section{Methodology} \label{sec:method}

\subsection{Overview}

As shown in \fref{fig:diagram}, AirInteraction is composed of three components, including (1) an online unsupervised learning algorithm running on the robot that detects interesting scenes during exploration in real-time, (2) a human-robot interactive interface at the base station, and (3) a few-shot object detector that learns human-informed objects online.

The workflow of AirInteraction is also simple but efficient. Due to the uncertainty of working environments, we cannot guarantee the network condition, thus we reduce the communication bandwidth requirement by putting most of the computation on the robot and only send necessary information to the base-station.
Specifically, the robot detects potentially interesting scenes by employing the online interestingness detection algorithm and only sends the images with highest probability to be interesting to the base-station.

If the network is interrupted, the interesting scenes will be stored in a stack and cover those images with lowest probability. The images with higher probability will be sent in higher priority once the network is established again.
If the human operator with the base-station thinks an image is interesting, the operator will be prompted to select the region of interests, such as a novel object. The selected regions (bounding boxes) and the object labels are then taken as training samples to fine-tune the few-shot object detector.

The few-shot detector is fine-tuned on the base-station and a mirror copy model runs on the robot.
The human operator's annotations are dynamically added to the training samples' pool to update the model parameters, which are sent to the robot periodically to detect similar objects.
We next explain more details about the three modules, respectively.

\begin{figure}[t]
    \centering
    \includegraphics[width=1\linewidth]{Figures/fig3-online.png}
    \caption{The structure of the unsupervised online learning model. It detects the potentially interesting scenes based on a visual memory module. The images that are not well recalled by the online updated memory are taken as interesting, which will be sent to base-station.}
    \label{fig:online-learning}
\end{figure}

\subsection{Online Unsupervised Interestingness Detection}

To reduce the communication requirement, we leverage the unsupervised online learning model \cite{eccv,wang2021unsupervised} to select potentially interesting frames.
This approach is composed of a three-stage learning architecture, i.e., (1) the \textit{long-term} learning, which acquires general world-level knowledge on large scale training data, (2) the \textit{short-term} learning, that learns prior task-dependent knowledge via a visual memory module from hundreds of uninteresting images in several minutes, and (3) the online learning, which processes image stream and updates the memory module in real-time to adapt to the environments.

The key of this architecture is the visual memory, which saves the knowledge about the uninteresting scenes during the short-term learning, and outputs lower confidence when novel scene appears suddenly during the online mission execution. One important feature of this system is a losing of interests over repetitive scenes, {i.e.},~ when a robot sees an interesting image, it is quickly written into the memory, so that the interestingness level of similar following scenes decrease gradually. However, the interestingness recognition system \cite{eccv,wang2021unsupervised} has no interaction with the operators and it cannot detect specific human-informed objects.

When the exploration robot perceives the surrounding environment, it processes continuous streams of image. Each image vector $\textbf{x}(t)\in \mathbb{R}^{C\times W \times H} $ goes through the visual memory module $\textbf{M} \in \mathbb{R}^{N \times C \times W \times H}$ of the interestingness algorithm, where $C,W,H$ are number of channels, width, and height of the image, and $N$ is the number of cubes in the visual memory. When the robot sees a new scene $\textbf{x}(t)$ at time $t$, the visual memory module $\textbf{M}$ is updated as follows:
\begin{equation}
    \textbf{M}_i(t) = (1-w_i)\cdot \textbf{M}_i(t-1) + w_i\cdot \textbf{x}(t),
\end{equation}
where $w_i$ is the $i$-th element of the vector $\textbf{w}\in\mathbb{R}^{N}$
\begin{equation}
\textbf{w} = \sigma\Big(\gamma_w \cdot \tan\big(\frac{\pi}{2} \cdot \mathcal{D}\big(\textbf{x}(t),\textbf{M}(t-1)\big)\big)\Big),
\end{equation}
where $\sigma$ is the {\tt softmax} function, $\gamma_w$ is a learning speed control parameter, and $\mathcal{D}$ is the cosine similarity function. This \textit{writing} process \cite{eccv, wang2021unsupervised} enables the visual memory module to incrementally learn new scenes without forgetting old scenes.

Concurrently, when the robot sees a scene $\textbf{x}(t)$, it needs to recall the memory and compare the new scene with the old scenes within the memory. Within this memory $reading$ process \cite{eccv, wang2021unsupervised}, the reading score is defined as
\begin{equation}
 f(t) = \sum_{i=1}^{N} v_i \cdot \textbf{M}_i(t),
\end{equation}
where $v_i$ is the $i$-th element of the vector $\mathbf{v}\in\mathbb{R}^{N}$
\begin{equation}
    \mathbf{v} = \sigma\Big(\gamma_v \cdot \tan\big(\frac{\pi}{2} \cdot \mathcal{S}\big(\textbf{x}(t), \textbf{M}(t)\big)\big)\Big),
\end{equation}
where $\gamma_v$ is also a learning speed control parameter for reading, and $\mathcal{S}$ is maximum cosine similarity operator between $\textbf{x}(t)$ and all possible translations of $\textbf{M}(t)$, which is calculated from element-wise multiplication of their Fourier transforms.

Intuitively, when the memory reading confidence score is low, the scene is more likely to be interesting since the scene has never been visited before, and vice versa. In practice, we take the cosine similarity between $f(t)$ and $x(t)$ as the reading confidence.
All the scenes that were previously seen are written into the memory; this prevents robots over-detecting similar scenes as interesting. Even if a scene is interesting at first glance, robot still loses interests over the scene, if the scene is repetitively visited.

We choose the visual memory learning scheme \cite{eccv, wang2021unsupervised} as the first component of AirInteraction, not only due to the high efficiency of the algorithm, which enables real-time online processing of image streams, but also because it can make prior judgments so that only a few potentially interesting scenes will be sent to the base station. Using the visual memory, the robot can respond to novel scenes only, ignore the repetitive or similar scenes, and avoid the redundant request for human feedback.

\begin{figure}[!t]
    \centering
    \includegraphics[width=1.0\linewidth]{Figures/interface-revised2.png}
    \caption{Human-robot interface for annotation and surveillance.}
    \label{fig:interface}
\end{figure}

\subsection{Human-Robot Interactive Interface}
We design the interface for human-robot interaction between the robot explorer and human operator based on OpenCV \cite{culjak2012brief} and ROS \cite{quigley2009ros}.
A screenshot of the visualization and annotation tool is shown in the \fref{fig:interface}.
When the interesting scenes detected by the robot are sent to the base station, the scenes are queued and being shown to the human operator one by one. If the human operator presses the key {\tt N}, which denotes \textit{not interesting}, the image is sent back to the robot and written into the memory, which prevents similar following scenes from being detected as interesting by the robot.

If the human operator presses the key {\tt I}, which means the human operator thinks the scene is \textit{interesting}, the pop-up screen of the image appears where the operator can draw bounding box of the interesting object in the image. The annotation information of the image and the location of the bounding box are sent to the few-shot object detector running on the base station, which will be explained next subsection.

Our interface is designed to make the human operator's interaction with robot as easy and simple as possible.
Using this visualization tool, any inexperienced human operator can give feedback to the robot with minimal efforts and actions by just clicking the image screen and using a mouse to draw the bounding box.

\subsection{Few-shot Object Detector}
The human-informed object annotations and the image pairs serve as few labeled example inputs for few-shot object detector, which is equipped in the base station. We further explain the details about the choice of the few-shot object detection model and techniques to improve its performance.
\subsubsection{Two-stage Fine-tuning Approach}
In this work, we employ one of the transfer-learning based few-shot object detection model, two-stage fine-tuning approach (TFA) \cite{TFA} as a baseline, which is known to have state-of-the-art performance on benchmarks. The two-stage refers to (1) the base training stage which jointly trains feature extractor and box predictor for base classes, and (2) the fine-tuning stage which trains box predictor for novel classes. For the base model, we follow the same architectures proposed by \cite{TFA}, where Faster R-CNN \cite{faster-rcnn} is adopted as a base detector with Resnet-101 \cite{deep-residual} backbone.

During the fine-tuning stage, the feature extractor of the model is fixed, and only the final layer of the box predictor is fine-tuned. While training for base classes requires abundant data, training for novel classes via fine-tuning the final layer only requires using few labeled examples. We use the human-informed image-annotation as the labeled examples and feed them into the trainer to fine-tune the final layer of the model.

\subsubsection{Weighted Mixture of Batch for Fine-tuning}
\label{sec:weighted}

When fine-tuning the last layer of the model, previous work \cite{TFA} used the balanced mixture of base class images and novel class images as a minibatch for training. However, our experiments show that this often cannot produce satisfactory results for novel classes. Instead, AirInteraction employs a weighted mixture of batch for fine-tuning. Let's denote the number of base classes and novel classes as  $B$ and $N$, and we use $K$ instances per category during fine-tuning. The random mixture of $BK$ base images and $NK$ novel images are used. If the minibatch size is $m$, each minibatch is a random combination of base and novel images, and there are $\nCr{(B+N)K}{m}$ combinations. In this work, we use a variant of this scheme by increasing the frequency of using novel (human-informed) images when generating a minibatch, rather than using random uniform mixture. Let's denote the ratio of novel images compared to base images as $r~(r\ge 1)$. Then there can be $\nCr{(B+rN)K}{m}$ combinations of minibatches to be used for fine-tuning. Note that we are not changing the number of instances per novel category, but just simply reusing the existing instances more frequently to generate minibatches.
\begin{figure}[!t]
    \centering
    \includegraphics[width=0.8\linewidth]{Figures/fine-tuning.png}
    \caption{The combination of base images and novel image shots compose the batch for the fine-tuning training. We parameterize the frequency of using novel image shots compared to base images, and use the novel shots up to 3 times more frequently than base images.}
    \label{fig:fine-tuning}
\end{figure}

\subsubsection{Parameter synchronization between robot and base station}
We have two identical few-shot object detectors in the system, one in the base station, and the other in the robot. Since training the object detection model is computationally heavy for the robot, we perform the fine-tuning process in the base station. Note that the parameters of the pretrained features are fixed, both in the robot and the base station, and only the parameters of the final layer are updated. Therefore, it suffices to synchronize the final layer parameters of the object detector in the base station and the one in the robot. The dimension of the final layer is comparatively small, and can be sent easily to the robot for synchronization.

\section{Experiments \& Results}\label{sec:experiment}

\begin{figure}[!t]
    \centering
    \includegraphics[width=0.85\linewidth]{Figures/dataset.png}
    \caption{Representative images from each dataset. From 1st to 4th row: SubT Tunnel, SubT Urban, Scott Reef 25, Drone Filming}
    \label{fig:dataset-images}
\end{figure}

\subsection{Dataset}
The experiments are conducted on four authoritative challenging datasets, as shown in \fref{fig:dataset-images}, to verify the effectiveness of our system.

\subsubsection{SubT Tunnel and SubT Urban Datasets}
The two datasets are recorded during the DARPA Subterranean (SubT) Challenge, which requires competitors to build autonomous robotic systems to search and explore the subterranean environments like tunnel and urban underground. In this challenge, the autonomous robots are prompted to search for different objects. We use the datasets recorded by two autonomous UGVs from the Team Explorer (Carnegie Mellon University and Oregon State University).
For SubT Tunnel circuit, we chose door, hole, surveillance camera, and fire extinguisher as interesting objects, and for SubT Urban dataset, we chose helmet, backpack, rope, drill, vent, and human survivor as interesting objects.

\subsubsection{Scott Reef 25 Dataset} The Scott Reef 25 dataset is a sequence of top-down view images recorded by autonomous underwater vehicle (AUV) at Scott Reef in Western Australia.
There are five labeled objects in this dataset, in which we take sand as uninteresting and the other objects including finger coral, mushroom coral, weed, and debris as interesting.

\subsubsection{Drone Filming Dataset} It is an outdoor dataset recorded by quadcopters that follow moving targets during real-time aerial filming. In this work, we select three different object categories (van, truck, bicycler) as interesting.

\subsection{Baseline, Metrics, and Hardware}

Since AirInteraction is the first human-informed robotic interestingness detection system, we lack off-the-shelf systems to compare.
Therefore, we will take the few-shot object detection method TFA \cite{TFA} as a baseline. For fairness, we adopted the default parameters for the TFA. To test the performance of few-shot object detector trained on the inputs from the human operator, we use the traditional evaluation metric, Average Precision (AP) score \cite{MSCOCO} for each class of objects and across the classes. For each dataset, we measure and average the performances over the object classes. For fine-tuning, we used 1 Nvidia GeForce RTX 2060 GPU.

\begin{table}[t]
	\centering
	\caption{Performance of AirInteraction and TFA on all datasets. Bold values denote the best performance in each number of shot.}
	\label{tab:performance}
	\begin{tabular}{c|c|c|c|c}
		\toprul
		Dataset & Model & 1 Shot & 2 Shots & 3 Shots\\
		\midrule
		SubT Tunnel & Ours ($r=1$) &  5.21 & 10.55 & 13.65 \\
		& Ours ($r=2$) & 12.39  & 14.78 & 14.29 \\
		& Ours ($r=3$) & \textbf{13.38} & \textbf{17.56} & \textbf{17.81} \\
		& TFA \cite{TFA} & 7.52 & 13.85 & 17.20\\
		\midrule
		SubT Urban & Ours ($r=1$) &  4.35 & 8.27 & 9.52 \\
		& Ours ($r=2$) & 8.32  & 10.20 & 11.51 \\
		& Ours ($r=3$) & \textbf{12.53} & \textbf{13.68} & \textbf{14.95} \\
		& TFA \cite{TFA} & 8.54 & 10.56 & 10.82\\
		\midrule
		Scott Reef 25 & Ours ($r=1$) & 8.37 & 10.02 & 34.72 \\
		& Ours ($r=2$) & \textbf{35.28} & 31.77 & \textbf{42.49} \\
		& Ours ($r=3$) & 32.48 & \textbf{34.62} & 37.74 \\
		& TFA \cite{TFA} & 28.25 & 30.92 & 36.97 \\
		\midrule
		Drone Filming & Ours ($r=1$) & 12.60 & 63.52 & 51.82 \\
		& Ours ($r=2$) & 40.03 & 83.41 & 83.25 \\
		& Ours ($r=3$) & \textbf{62.68} & \textbf{89.36} & \textbf{84.38} \\
		& TFA \cite{TFA} & 35.26 & 70.59 & 80.95\\
 		\bottomrul
	\end{tabular}
\end{table}%

\subsection{Performance with Fixed Number of Shots}
\label{sec:performance-shots}

Since the online annotation is costly, we assume that human operator can give annotation feedback for up to 3 scenes per class. Therefore, we take very challenging settings to demonstrate the effectiveness of our method, i.e., only $k = 1,~2,~3$ shots are provided in the experiment. In addition, we also report the performance using the weighted mixture of batch for fine-tuning as described in \sref{sec:weighted}, i.e., the novel-to-base image ratio $r=1,~2,~3$ are used.

In the \tref{tab:performance}, we reported the overall performance of AirInteraction, which is fine-tuned for 5 minutes to meet the quick deployment requirements.
The results show that AirInteraction with $r=2$ and $3$ achieve better performances than the TFA baseline in all three datasets, while AirInteraction with $r=1$ achieves lower performance than TFA.

In average, the AirInteraction with $r=2$ and $r=3$ show \textbf{10.8\%} and \textbf{29.7\%} higher performance than the TFA baseline across all datasets. AirInteraction with $r=1$ shows 34.4\% lower performance than the TFA on average. This highlights the effect of tuning the ratio $r$ when employing AirInteraction; reusing the novel shots provided by the human operator in the minibatches facilitates the improved performance to the original model. We also note that the performance of AirInteraction with $r=1$ is relatively lower than the TFA baseline, because the dynamic addition of human-informed shots during the training is not as stable as starting the training with novel shots in the first place. Increasing the ratio $r$ can be thought of as a method to make up this shortcoming by using novel shots more frequently for training.

In \tref{tab:class-performance}, we additionally reported the best performance of the AirInteraction model by object classes in the dataset. We observe that the AirInteraction is achieving comparatively good performance in detecting objects in Drone Filming and Scott Reef 25 datasets than SubT tunnel and SubT Urban datasets.

\subsection{Performance with a Fixed Training Time}

In \sref{sec:performance-shots}, we report the model performance with fixed fine-tuning time.
However, during autonomous exploration, the robots should be able to learn the novel object categories within a limited amount of time for quick deployment.
In this section, we further report the model performance in \fref{fig:time} with a fine-tuning time of 1, 2, 3, 4, and 5 minutes.
This means that the few-shot object detector should learn novel object categories within five minutes for exploration, which is acceptable for most tasks. In general, the performance of the object detector improved over time. In the case of $r=2$ and $3$, the performance of the object detector started to saturate starting 3 minutes, while in the case of $r=1$, the performance of object detector was low until 5 minutes. On average, across the datasets, the model performance of AirInteraction with $r=2$ at time $t=3$ and $4$ minutes was $80.8\%$ and $97.5\%$ of its performance at $t=5$ minutes, respectively, and the performance of AirInteraction $r=3$ at time $t=3$ and $4$ minutes was $85.6\%$ and $94.8\%$ of its performance at $t=5$ minutes.

\begin{table}[t]
  \centering
  \caption{Best Object detection performance of AirInteraction for each class of object in each dataset.}
  \label{tab:class-performance}
    \begin{tabular}{c|cc}
    \toprul
    Dataset & Class & Performance\\
    \midrule
    SubT Tunnel & Door &  19.28\\
    & Hole &  10.25\\
    & Cctv &  14.55\\
    & Fire Extinguisher &  18.50 \\
    \midrule
    SubT Urban & Backpack &  14.38 \\
    & Helmet &  18.25 \\
    & Rope  & 9.44 \\
    & Drill & 11.69 \\
    & Vent  &  16.28 \\
    & Survivor & 13.74 \\
    \midrule
    Scott Reef 25 & Weed & 32.54 \\
    & Finger Coral & 53.15 \\
    & Mushroom Coral & 43.16 \\
    \midrule
    Drone Filming & Van & 98.52\\
    & Truck & 83.41\\
    & Bicyler & 88.11\\
    \bottomrul
    \end{tabular}%
  \label{tab:subt_cls}%
\end{table}

\subsection{Online Responsivity}

Real-time performance is crucial for robotic systems, thus we next measure the online responsivity of the interestingness detection system.
Specifically, we employ the metric Area Under Curve of Online Precision (AUC-OP) \cite{eccv}, which jointly considers online response, precision, and recall rate.
The baseline is the online visual interestingness algorithm \cite{eccv} without the object detector component, which detects the binary interestingness of the scenes only using a visual memory module. Basically, all the scenes that contain interesting objects are taken as interesting, and the others are not interesting.

In the \tref{tab:auc-op}, we reported the AUC-OP scores of the baseline visual interestingness algorithm and AirInteraction in the different datasets. In the SubT Tunnel, SubT Urban, Scott Reef 25, and Drone Filming datasets, the AirInteraction showed better online response scores than the baseline.
Note that we often accept several false positives to improve the recall rate and the metric of AUC-OP provides a variable $\delta~(\delta\ge1)$ to reflect this. Basically, higher $\delta$ means more false positives are allowed and $1$ means no false positive is allowed ($\delta=2$ is recommended for most applications).

\begin{table}[h]
  \centering
  \caption{The performance on AUC-OP score.}
    \begin{tabular}{c|c|ccc}
    \toprul
    Dataset & Method & $\delta=1$ & $\delta=2$ & $\delta=4$\\
    \midrule
    SubT Tunnel & Baseline \cite{eccv} &  0.32 & 0.51 & 0.72\\
    & Ours & 0.36 & 0.53 & 0.81 \\
    \midrule
    SubT Urban & Baseline \cite{eccv}&  0.29 & 0.45 & 0.66 \\
    & Ours & 0.35 & 0.44 & 0.69 \\
    \midrule
    Scott Reef 25 & Baseline \cite{eccv} &  0.44 & 0.71 & 0.89\\
    & Ours & 0.58 & 0.75 & 0.85 \\
    \midrule
    Drone Filming & Baseline \cite{eccv} &  0.56 & 0.68 & 0.74\\
    & Ours & 0.59 & 0.70 & 0.88\\
    \bottomrul
    \end{tabular}%
  \label{tab:auc-op}%
\end{table}

\begin{figure*}[!t]
    \centering
    \includegraphics[width=1.0\linewidth]{Figures/time.png}
    \caption{Performance of few-shot object detector within five minutes. Blue, red, yellow graph each denotes AirInteraction $r=1,2,3$}
    \label{fig:time}
\end{figure*}

\subsection{Reducing Communication Bandwidth}

The purpose of online unsupervised learning module is to select a few interesting scenes and send those scenes to the base station, thereby reducing communication bandwidth between the robot system and the human operator. In this section, we measure the reduction of communication bandwidth by comparing the ratio of the number of selected interesting scenes sent to the base station and the number of total scenes. As can be seen in \tref{tab:communication_bandwidth}, our system reduce the communication bandwidth 80 \% up to 91\% across datasets, which indicates its effectiveness.

\begin{table}[h]
  \centering
  \caption{Percentage of images being sent}
    \begin{tabular}{c|cccc}
    \toprul
     & SubT-Tunnel & SubT-Urban & SR25 & DF\\
    \midrule
    Ratio & 0.15 & 0.11 & 0.20 & 0.09 \\
    \bottomrul
    \end{tabular}%
  \label{tab:communication_bandwidth}%
\end{table}

\section{Limitation} \label{sec:limitation}
One of the limitations of AirInteraction comes from the fact that it is using a fine-tuning based few-shot object detector. Under our specific choice of fine-tuning-based object detection model \cite{TFA}, the maximum number of novel object classes should be fixed; we set the capacity of new object categories as 10, but if the robot faces more objects during the mission, the capacity should be enlarged. Moreover, fine-tuning the model requires some amount of time to reach a good performance; although we limited the training time to five minutes, and showed that AirInteraction achieves good performance within this time constraint, it is better to shorten the amount of time learning a new category. To this end, deploying a few-shot object detection model without fine-tuning should be considered. Recent works on dynamic few-shot object detection without fine-tuning \cite{airdet} or incremental meta-learning based few-shot object detection \cite{ONCE} can be our future alternatives.

\section{Conclusion}
We proposed the human-robot interactive framework, AirInteraction, that enables interesting scene recognition via human-informed few-shot object detection. Composed of three components, (1) online unsupervised learning module, (2) human-robot interaction interface, and (3) few-shot object detector, AirInteraction aids mobile unmanned exploration robots to learn novel interesting objects during the mission only relying on minimum amount of human feedback. Our additional techniques, weighted mixture minibatches and parameter synchronization, improve the performance of the few-shot object detector in the online learning setting that requires quick responses. Our experiment results show that AirInteraction achieves good object detection performance in diverse object detection datasets, and impressive performance within less than five minutes.

\section*{Acknowledgment}
This work was partially sponsored by the ONR grant \#N0014-19-1-2266 and ARL DCIST CRA award W911NF-17-2-0181.
The authors would like to thank all members of the Team Explorer for providing data collected from the
DARPA Subterranean Challenge.

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


text:
Joshua Francis speaks to DNO on reasons for bowing out of political life again
Dominica News Online - Tuesday, February 4th, 2020 at 7:15 PM
Former Parliamentary Representative for the Roseau South Constituency, Attorney Joshua Francis has stepped out of the political arena once again.
This is not the first time Francis has left politics as there were rumours of his departure as early as 2016 when he was ousted as deputy leader of the UWP due to his involvement in a court matter. He also quit outright in 2017 only to return in May of 2018.
He told Dominica News Online (DNO) during an interview on Tuesday that he decided to take a break and move out "cleanly".
Francis, who was a member of the Opposition United Workers Party (UWP) lost his seat to newcomer Chekira Lockhart-Hypolite in the 2019 General Election by about 299 votes.
"I stepped out to give myself an opportunity to walk into a new season of my life. I needed a break, I needed to get out of the noise to go into a season of peace and quiet for self-restoration, self-repair, and self-development," he stated.
Francis stated, however, should it be God's will sometime in the future he may return to give political service to the people, "but not in the immediate future."
"I have no intention to be a candidate in the next general election. I do not know what the future holds but for now I needed some time and space for self-development and I wish everybody well," he said.
Meantime, Francis said he will continue to have a close relationship with the Roseau South Constituency.
"It is part of my extended family and I have not turned my back and I will not turn my back on the people," he indicated.
He went on to say that one of the things he will be focusing on is his law practice.
"My law practice is one of the things that I will be focused on moving forward," he stated.
Francis expressed his gratitude to all those who have contributed to him in one way or the other.
He also thanked all of the UWP, the Roseau South Constituency, and Dominica by extension.
"I hope the best for every one of us, I hope the best for a better Dominica, a better Roseau South and an improvement in life for every single Dominican, especially the barefoot man," Francis said.
His decision was officially made known in a letter to the President of the UWP.
The UWP suffered a major defeat to the incumbent Dominica Labour Party, who won 18 of the 21 seats in the last general elections.
Joshua Francis as political leader is not in the works says APP
Francis says help is on the way for Roseau South
Skerrit calls on Francis to resign as Roseau South MP
UPDATE: Joshua Francis resigns from "very turbulent" political journey
Lockhart-Hypolite vows to deliver 'progress not commess'
Lin clown
Me,what lost? after you and Linton embarrassed the man in Mahaut? HYPOCRITE.
Hot debate. What do you think? 6 15
iprotest
I remember when my schoolmate Ian resigned as parl rep for soufriere…..my padna was sobbing in full public view on de podium.
Talk about embarrassing your own in full public view!!!
I'm not a supporter of any party……. certainly not DLP…. not after de embarrassments of Rosie and Pierro…..I felt hurt when I saw de end of my friend political career … sobbing in full view of the public.
What happened to de case nuh?
Barbara Saunders
I don't find the name Joshua looks anything like Ian so why is Ian the topic? any embarassment associated with Ian pales in comparison with that of Joshua. Feb 07 debacle; …………………..pathetic appearance and treatment at the UWP Mahaut party gathering etc.
You people continue to deflect the focus from yourselves to other people, trying to take out the speck in other people's eyes while the beam is blinding your own.
You all just seem never to learn.
Eagle-Eyed
What ever happened to the allegations of inappropriate behaviour with this underaged girl? Does it mean that he can now face prosecution for his part in this action? The other guy from the Labor that was involved got sacked by Skerrit but Linton went on to appoint Joshua as deputy leader of the UWP.
Well-loved. Like or Dislike: 11 6
EIFILE
It is always sad to see hard work gone to waste. Now the so called barefoot man is having a good laugh. It is a heavy price to pay when you don't follow your office code of conduct of practice. We all learn from our mistakes. You fell off your horse
you remount and learn to ride better. No we are boys business, your family is your wife and children, relatives should take care of themselves. Good luck for the future. God Speed
Gouvelma
I applaud you for doing what you think is in your best interest. I wish you ALL the best in whatever direction you head out. I however think that the decision is premature. I am the eternal optimist. The courts are going to rule in favor of the UWP in the elections petition. You arevtge established candidate. It would be against a new candidate in the by elections should you hold firm on your decision. I hope that you will return to the ground as soon as the court's decision is given and you can win your seat and give true representation to the constituency and service to the country.
Hot debate. What do you think? 10 27
Govelma
Boy you people are a sample. Is he the same person who attended the UWP gathering in Mahaut after the case against him? are you the same supporters of the same party?
Thank God Joshua Francis is a young man with a likeable personality and he seems to have a big heart of forgiveness, but the rest of us are still here and we still remember the atrocious treatment which he received.
You people really have a nerve, but that is typical of ALL of you. Hope Joshua stops hopping around and sticks to his position this time.
Thanks 🙏
@gouvelma typical of you lunatics. The man gave strong and solid reasons for wanting to step out of the limelight, respect the man's decision! The reason is self perseverance! Now Mr. know it all you don't think his decision is good enough for you RIGHT! Because of your level of education, I thought you had a different analysis on matters, apparently no! Are you one of the devious ones from another PLANET 🌏?
Man bites dogs
@Gouvelma, You must be a bloody clown 🤡 or …..to think that the court will rule in Linton and his comedian party no wonder you lots keeps writing so much nonsense on this forum!!!
@Gouvelma, are you familiar with the term 'Cloud Coo-Coo Land'? Well I suspect that's where you are currently living on. Joshua decided to get a real life and move on, maybe you should follow his example and do likewise.
The Truth Be Told
But Gouvelma really? Really? Optimist? Eternal? Eternity ? What kind of advise is this? Why still mislead? i am so amazed at some people. Move on in your mind. Emancipate yourself from fake news slavery! It is done, it has been done. Threat and mayhay did not frighten the electorates. Move on.. Use a new and better strategy!
KID ON THE BLOCK
Mr just agree he can not be led by a JACK … for another 5 years so he trying something.
Did DLP win 18 or 17 seats?
Big up Joshua!
Every man had his path in life. Your political career as park rep for Roseau south was a blessing to the constituents who will continue to hold you close to their hearts.
Politics is a cost to the human spirit…bow out with Grace my boy. Your family and personal life is as important to the welfare of the constituency.
You bowing out with clear vision.
I live around Roseau south although temporarily ….I watched all de hatred and division coming from your opponent side … that attitude will not bring de constituency together.
You served the nation with pride… By de way I personally think that de Judas fella wanted to be on the executive.. maybe in your position.
He didn't satisfy his ego to be on the executive so he maliciously displayed a hatred for the polical leader.
Up till today de Roseau Central people who voted for him have no idea what is in the letter to the political leader.
Joshua you are a Man among Men!
Hot debate. What do you think? 6 5
Do you guys take time out to think deeply when you write? I believe deep down in my heart that Joshua is a good person but he has a few defects which makes him unfit to represent at that level. Josh talks a good talk but the blemish on his character placed there by the alleged incident with ………..along with his alleged treatment of his wife and her eventually flight for safety, doesn't help him. All you of this happened while he was parl rep so I am not sure what you mean when you talk about serving with pride and dignity.
Your opinion alone can't sway de reality
Chekira can't unite people.
She failed long time ago.
What's your point
Ou tou —. What does the two have to do with each other. Would you advise you child to go commit a crime because his or her peer committed one? I am tired of asking you all to forget political affiliation when you comment. It will go a long way in preventing you from sounding like an empty head.
T R FULLBROOK
If it is not going Joshua Francis's way……He walks away, again. If he had worked harder to regain his seat in parliament he would have won.
Learning more and more each day
U did well for Dominica and the people of Roseau South.thanks for your efforts
GOUVELMA,UWP CANNOT WIN If another election were to be called UWP would LOSE.You ,UWP people do not understand.In 7 elections the MAJORITY of DOMINICAN VOTERS HAS NEVER VOTED UWP.You guys betrayed JOSHUA,remember Mahaut?CROOKED.
Linclown
TELEMAQUE.After the election of 2019,MATT PELTIER said on Q95,that LENNOX LINTON was no longer the leader of the opposition.A few days later LENNOX LINTON,went on Q95 and said he was still leader of the opposition,MATT stays MOOMOO.Section 66 (4)a on page 64 of the DOMINICA CONSTITUTION,tells Linton,He is no longer leader of the opposition.
And this is a loss to Dominica… a guy who is smart, in touch with the people and loved… too bad … look who we got instead… shame..

redpajama_set_name: RedPajamaCommonCrawl


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Joined in the practice in 1989 by his daughter Celene and in later years by David Costello and his son in law Niall Mulligan. Also on the Sherry FitzGerald Royal team are Catherine Keegan, Caitríona Bailey and our two front of house Administrators Audrey Harlin and Geraldine Caulfield, providing decades of land, residential and commercial property sales and lettings expertise. The expertise of our staff is the key to the successful, hassle-free experience our customers receive when selling or renting with us.
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text:
Imprints: Dark Matters
Amirali is pushing boundaries and galaxies with his new black hole inspired label.
by Julia Martineau
July 7, 2015, 2:50pm
Imprints brings you weekly profiles of the most exciting independent record labels the world over, written by movers and shakers from within their own electronic music communities.
Since releasing his breakout track, "Beautiful World" on Crosstown Rebels in 2012, Iranian/Canadian producer Amirali Shahrestani has captivated audiences worldwide with his live vocals and keys. Thanks to the success of his critically acclaimed In Time LP, Amirali has played some of the world's most reputable venues, and was invited to perform at the first video broadcast of the coveted BBC Radio 1 Essential Mix in 2013.
Two years later, while working to complete his second album, Amirali is extending his musical palette with the launch of an abstract new label. Aiming to encourage the unusual, Dark Matters will showcase original material from live acts and Amirali's own productions. THUMP caught up with the London-based artist to discuss his musical inspirations, his creative process, and the metaphysical concept behind his new collective.
Name: Dark Matters
Vibe: Dark abstract psychedelic pop
THUMP: What was the inspiration behind the label name?
Amirali: I've always been interested in astronomy and fascinated by dark matter. Black holes are the mysteries of our universe. You can't see them, but you're drawn to them and want to learn more. I see a similarity with music. Music is something we can't see or touch, but we're always drawn to it.
What type of sound are you trying to cultivate?
My main goal for Dark Matters is to discover and develop fresh young talent. The label will also provide me with a platform to showcase my own work. We are mainly focusing on live acts but intend to represent a broad range of music—everything from techno to abstract pop to ambient. We're not following any trends or chasing a specific sound. Our aim for Dark Matters is to push boundaries and to put out forward thinking, cutting edge music.
What's the label's next release?
Our first release is my new single "Fearful Stay" which is coming out with two amazing remixes from Prins Thomas and Aboutface. It will be out on vinyl July 6 and digitally on August 3.
What's the dance music scene like in London?
London's dance music scene has quite a long history. There used to be so many great venues to choose from that it was difficult picking a destination. Unfortunately, there have been many closures and this has definitely reduced the variety. But thanks to London's ingenuity, we still have amazing events and festivals. Some promoters are scouting for unique spaces such as old lofts or warehouses that can be converted into incredible venues. No matter the city politics, you can always find a fantastic place to go out in London.
How would you describe your production style?
This is a tough one. I never like to repeat myself when it comes to production. Every time I make music, I think about the stuff that I haven't done in the past. I think people can recognize my sound in some ways, especially when they hear my voice.
How do you choose which artists to work with?
I'm looking for artists that have something special to say with their music, artists that think beyond limitations and boundaries. I think that's when your true self comes out and you are the most creative.
What challenges do you face running the label?
There are obviously many challenges, but it's those obstacles that make it exciting. A lot of hard work goes into running a label and luckily I have an amazing team who are making this experience as smooth as possible.
Do you have any advice for artists who are trying to make it?
I think every artist needs to find their own voice and stay true to themselves otherwise they will easily fade away. This industry has become like a very fast paced machine. In order to survive, you need to stay on top of everything and evolve all the time.
What can we expect from you for the rest of 2015?
I have a lot of new material coming out soon. I've also finished my second studio album, which I'll be going into details about later on. Stay tuned.
Dark Matters is on Twitter // SoundCloud // Facebook Amirali is on Twitter // SoundCloud // Facebook
Tagged:ThumpLondonthump exclusive streamDark Mattersaboutfaceamiralifearful stayprin thomas


text:
Smith named department administrator for pediatrics at Marshall Health
HUNTINGTON, W.Va. – Marshall Health has named Brandan L. Smith as department administrator for Marshall Pediatrics.
Smith has more than 15 years of experience in health care administration, including work as a clinic manager and specialty billing coordinator at Huntington Internal Medicine Group. For the last 10 years, he has worked as a managed care analyst for Cabell Huntington Hospital. Smith is a two-time graduate of Marshall University with a B.B.A. in management and an M.S. in Health Care Administration. He also serves as a board member for Lawrence County Developmental Disabilities in Coal Grove, Ohio.
In his new role, Smith oversees the day-to-day operations, administrative and human resources functions of Marshall Pediatrics, which includes more than 50 physicians and six clinic locations in Huntington, Barboursville and Teays Valley.


Brand new lifesize (4'-8" tall) standup of TYRION LANISTER (PETER DINKLAGE) from GAME OF THRONES. Can be displayed as a giant poster or as free standing lifesize standee. Exclusively offering JAMES BOND, The Beatles, The Man from UNCLE, Emma Peel, Lady Gaga, Madonna, Brigitte Bardot, Bonanza characters, Clint Eastwood, Psy (fully licensed, not available anywhere else). More to come! SHIPPING Comes folded, direct from a central warehouse so POSTED DELIVERY TIMES ARE ESTIMATES; therefore.... *** PLEASE EMAIL FIRST IF YOU HAVE A SPECIFIC NEED DATE *** S&H WITHIN THE CONTINENTAL USA: Ground UPS/FedEx in oversized package (no PO Boxes; AK, HI, PR, GU extra, - please email first for rate). Includes insurance. DELIVERY TIME: Posted delivery time frame is an ESTIMATE only. Generally, please allow up to 11 business days for delivery (in many cases faster). Approximately 1-2 weeks longer for international shipments. Expedited air shipping available for additional cost. INTERNATIONAL SHIPPING: we do ship internationally but please email first prior to bidding (Canadian buyers please provide your postal code). MULTIPLE PURCHASES: $7 extra per each additional piece within the Continental USA; variable elsewhere. TERMS: ment to be received within 5 days after date of seller's invoice. No returns/exchanges. However, if item is damaged or lost in transit, automatic insurance will cover replacement or refund (US and Canada only).


text:
Matla River  forms a wide estuary in and around the Sundarbans in South 24 Parganas district in the Indian state of West Bengal.

The main stream of the Matla River is divided into two arms near Purandar. One passes through Kultali-Garanbose and then passes through the Sundarbans. The other passes through Basanti, Pathankhali, Surjyaberia, Masjidbati and then meets Bidyadhari River.

Lateral connections of the Matla system on the west are Belladonna River, Kultala River, the Piyali-Nabipukur River, the Bainchapi Khal, the Kaikalmari River, the Suia River, the Dulibhasani Gang, and the Gokhaltali Gang, On the eastern side the main connections with the Gosaba and Raimangal systems are more intricate and many of the channels are yet to be named. The important channels in this belt are Rupkhali Khal, Pathankhali Nadi, Pirkhali Nadi, Gazikhali Khal, Panchmukhani Khal, Mayanadi Khal, Mayadip River, Bhangaduni River, Kalindi River, Kalgachia River, Raimangal River, Jhilla River, Gonna River, and the Harinbhanga River.

An abortive attempt was made to establish Port Canning on the Matla in 1860s.

About 300,000 pilgrims take a dip at the confluence of the Adi Ganga and Matla rivers at South Bishnupur village on Makar Sankranti day.

During the monsoons the Matla becomes so turbulent that it becomes impossible for boats to ferry people. At the same time, the Matla has become so silted that during the dry season boats cannot come near the jetties and have to be stationed  from the jetty. Long stretches of the Matla have embankments to protect villages in the surrounding areas from flooding.

A  road bridge across the Matla, was inaugurated by Buddhadeb Bhattacharjee, former Chief Minister of West Bengal, in January 2011. It links Canning with Basanti. The bridge is located at .

References

Gallery

External links

Rivers of West Bengal
Geography of South 24 Parganas district
Sundarbans
Rivers of India

redpajama_set_name: RedPajamaWikipedia


While technology has reshaped how most organisations do business, the real estate industry has for long remained a stranger to technology. With digital disruption approaching all kinds of industries, the real estate as a business today has changed too – and it isn't just for the buyer's side.
Investors too can benefit from the digital tools and strategies on offer and make their lives easier and better. So, what do you do if you are looking to digitally transform your real estate business? What are the technologies that you need, and how can you go about it? This guide talks about the REI (real estate investor) digital transformation, and how you can make a successful one easily.
Making the Right Moves as a Real Estate Investor?
Listed below are some technologies that have helped real estate investors make fantastic progress. Also, note when I say Real Estate Investors I also mean me. To provide full transparency, I have active memberships with each of the companies mentioned below. My intent as an affiliate member is to: 1) Ensure the Technology is maintained and iterative enhancements released; 2) Each Company has a healthy subscriber list, to ensure the company's longevity; 3) Subsidise my own Membership costs, and 4) Only ever recommend Technologies I use myself. Enough about me, let's see how we can digitise you!
Traditionally, real estate investors only made such decisions looking at deals that seemed hot at that time and the current economic situation. But the fact is that several markets had shown a surge during the worst of times and some markets had even gone down the spiral, also when the economy was booming.
No need to rely on your network and real estate agents while investing in a real estate property for flipping or leasing or any such activity. Big Data Analytics is changing the way we do practically everything. We don't have to stress ourselves anymore about finding a property dealer who won't make a fool out of us.
Housing Alerts is one such tool which I have personally used. It provides projections on average cash on cash return of the neighbourhood, it's rental income, occupancy etc.
Big Data has essentially brought a lot of transparency to the real estate industry. Be it owners, lenders, brokers or investors – everyone is benefitting by partnering with Big Data.
Investors can now make better-informed decisions by crunching numbers available from public and private data sources. Without the use of Big Data, they were only reliant on unprocessed data, which never gave utterly accurate results.
Housing Alerts offers real estate trend forecasts.
Big Data applications from Housing Alerts have brought about a turn by processing such data obtained from geographical information, census outputs, and the catalogues of property sales, as well as added transparency to this business by providing insights into the buying patterns of customers. The data obtained can be utilised by real estate investors to decide which property to buy, and the ones to avoid. That's not all – the data is also helpful in designing homes.
Identifying trends is critical when it comes to online real estate investments. An investor must be aware of future risks and future values of properties. It has served as a guide to the real estate investor who can decide how to manage their property most effectively. They can make calculations as to what factors will help them increase returns because of the real-time forecast of trends in the industry. It does save a lot of costs and improves the efficiency of operations.
When investors can predict trends using predictive analytics they can buy the best properties that hold the most value for the future and are also able to predict what would be the best design to take forward. It also helps them conclude what kind of tenants they should be targeting and whether the tenants will be able to maintain positive cash flow.
Housing Alerts offers real estate investors like me, a detailed picture of what the market is going to look like. This gives them complete and accurate data which they can filter to suit their requirements and gain a competitive advantage in the market which they want to target. It can help them devise an optimal strategy for making investments based on a particular location. As well as added ease to investments although real estate is still a very volatile arena and highly complicated.
Considering the volatile nature of real estate markets, investors often end up in situations where they buy a property only to find that in a short period, its value has depreciated.
Another challenge that real estate investors face is deciding the pricing. They must tag a price that brings them good returns and at the same time should not be so high that it drives buyers away. The same applies when they are purchasing a property. Pricing and Analytics tools from rehab valuators are the answer to every online real estate investor's pricing woes.
To be honest, nobody has the luxury of time on their side today – unless you've left it all and gone to the countryside. In the business of real estate investments, timing is everything – you need to know how to play it right.
When there are good deals on the market, they sell like hotcakes – and it's only if you can spot them quick that you can tell your customers about it. Real Estate Investors cannot wait around for an agent or someone else to say to them about a good deal and why exactly it is good.
If you wait around calculating and weighing all the data, you will probably have lost precious time, and that will have cost you the deal. Rehab Valuator has given investors like me tools that have made it possible for me to find, evaluate, analyse and make an offer on a lucrative deal.
With the tools that Rehab Valuator has empowered me with, not only have I been able to save time, I have also been able to keep a lot of money by not overpaying and have also been able to get better returns. Whenever I want to make a deal, I know exactly what kind of profit this investment will bring me because the tool provides investors with all costs involved in financing, partner splits, and exit strategies, easily.
When you want to flip your property, it gives you the right marketing tools to use, which you can make into a point and click presentations for marketing within seconds. Investors can now sell their deals faster and for more money. The tools also help us find lenders and raise money for your real estate projects with their flexible financing options.
The tools provide you with some great data which gives you a complete picture of the market. They are also easy to use as they are all automated and come with great mobility. You can access all the features from any device, anytime. You do not have to be confined to your office anymore and never miss out on any deal just because you were not sitting in front of your office computer.
Stay updated with all price drops, seller types, etc. Make quick assessments on properties and make your investment decision with these tools.
With the world rapidly going digital, everything happens online. If your business is not online, your business cannot exist. Motivated sellers are quickly on the lookout for someone who will buy their property, and they are doing it online. This is the place where you generate leads – leads without which, your online real estate business isn't going anywhere.
Inbound Marketing is one of the most effective techniques for generating leads for your online real estate business. Inbound Marketing helps you attract potential buyers and sellers and essentially directs them to reach out to you. It is a passive technique of drawing business. These are techniques that help you develop clear strategies using SEO tools that will help optimise your site with targeted keywords and helps take your site to page 1 of Google's search results. Not only that, but it also improves your site gain visibility and drives traffic to your website.
One tool that has been a life saver for me is Investor Carrot. Several Online Real Estate Investors like me have benefitted from the leads generated for their real estate business by Investor Carrot and have witnessed tremendous growth in the leads they were able to produce, converting their investments into solid returns. They have built an industry-leading Conversion Methodology which they integrate into every website they create. They also keep testing their systems and upgrade them regularly so that the performance of your lead generation website will keep improving.
These tools help your site rank higher and market in smarter ways. The reason why On Carrot has been able to figuratively control page 1 rankings on Google is their strategy which relies on inbound marketing tools that generate leads and the exclusive training they provide to their members for marketing strategies. Whenever I have had a question, I have had them in my corner, and they have always had my back.
With a fast-progressive world where everyone is always on the move and yet wants to stay updated with everything happening around them, Investor Carrot creates websites that are friendly for every kind of portable device. They create sleek interfaces that look, as well as operate, with great efficiency on mobile devices and do not waste any load time. The team uses very secure infrastructure for their enterprise applications. They take care that there is no downtime and put a lot of work into maintenance, hosting and recovering.
The websites are equipped with integrated tools like instant text, email and message notifications about leads which make everything possible on the go.
Investor Carrot provides all kind of marketing strategies to take your website to the top, no matter if you are a new entry in the business or have been here for a while. It has made the lives of so many investors so much easier. Considering that it may sound all technology and marketing to someone coming from a real estate background, it actually provides a hassle-free experience.
If you ever find yourself struggling with the competition, Investor Carrot will help you find more deals with their marketing strategies. Find motivated sellers and buyers off-market with the tools that Investor Carrot has to offer you.
Investor Carrot provides a simple property Listing Engine that helps you sell your properties faster.
You can also build a buyer list with a more powerful cash property. If you are into renting, you can find tenants and fill your rented spaces faster. All of this is done by adding more credibility to your brand which is achieved by strategies created from extensive research. Could I have done that kind of extensive research on my own? I don't think I would have the time or that level of deep understanding of what I should be doing, or in which direction I should be headed; so basically, Investor Carrot served to me on a silver platter that helped me grow my online real estate Business and get the right bang for my buck.
Investor Carrot has empowered me with a powerful lead engine page, a website that is fully customizable, responsive and has all the features even on a mobile. It also has capabilities for social media and blogging. And I can't say enough about the training and support that they provide along the way. Amazing.
When it comes to investments, a significant focus area for the millennials is the real estate market. They represent 68 per cent of today's first-time buyers. When it comes to the generation of millennials, they are the nomads of the digital land and want instant access to everything. Even when it comes to real estate, they want information on the go. That said, it is also a fact that the real estate market has been a little slow in embracing technology.
Until a few years ago, social media did not even exist for real estate agents. But with the entry of millennials, that is no longer an option. The millennials expect everything to have a social media presence. If a business is not on social media, then it practically does not exist for them, neither do they find it credible.
Surveys also show that the significant chunk of the home buyers is the people who are the most active Facebook users. This calls for real estate professionals to give social media some serious thought. Luckily, it isn't rocket science.
There are several social media websites out there. Not everything can work for every kind of business. For example, if your business is in retail fashion, then you probably have no business being on LinkedIn. Connected Investor is a platform that helps you make the right connections in your industry. Needless to say, it is how vital connections are made in the world of real estate.
Without connections, it is like being stranded on a secluded island. Relationships are necessary for your deals to happen. If you want to improve your net worth as a real estate agent, you need to have a robust network. The advantage with Connected Investors is that they provide you with a real estate investing community for each and every city so that you will be able to connect with investors and professionals of your choice.
I have been able to grow my real estate business with Connected Investors who helped me expand my network far beyond what I initially thought was possible. I made connections inside the community and gradually my profits started increasing.
Connected Investors have a crowdfunding portal where you can find investment properties in bulk packages. You can also choose from several local investment properties, making your searches much easier. Just select the ones you need, and you can be done in minutes – no need to search for the right properties for hours – you've got it all in front of you.
We have a complete app savvy generation today that demands portability. They want access to things on the go, and they want super-fast apps to do the work for them. Connected Investors also offers investment apps that are suitable for mobile as well as desktop use. They are simple to use and help solve many of the problems that a real estate investor encounters.
These apps help them make optimal use of resources. By simplifying their tasks and streamlining their activities, they have now become central to their productivity. The best part is the freedom and the ease of access they bring.
There are several good ones out there – so you may just want to head off to the app store to see which ones you would like – it's an excellent way to know if there is any new property up for sale in your neighbourhood.
I have no clue how many times I have sat back at my home and let the app do the number crunching for me, find me hot deals, helped me generate leads, raise capital and so much more.
With my life becoming so much easier, I now have the luxury to solve my day-to-day challenges and make money with ease.
It is, in fact, the first app that was built for real estate investors and I have been enjoying it as a complete blessing.
Connected Investors has genuinely come to the aid of real estate investors for raising funding, finding new projects for flip investors and matching us with prospective buyers. In case you are wondering how much you will need to shell out to avail all these features, the answer is that there are no fees nor any commission involved. Just sign up and get connected with potential investors for free.
No matter what level of Digital Maturity you and your business are at, I would highly recommend joining Connected Investor for free, build your network and then get access to multiple time-saving Apps as required.
Also for those who like making decisions based on Statistics and Data (like me), I also endorse using Housing Alerts (Free). Market trends are so volatile and ensuring you understand your markets before you purchase is a high priority requirement. You need to provide your decisions are calculated, and you minimise your risk position. It frustrates me when I hear sad news stories, even though I feel for people losing money (and often it's their life savings), proper Due Diligence is non-negotiable. Decisions based on facts, not emotions are a must. Watching the news on TV or getting a tip from friends, is often old information and often is the cause of bad investments.
Both Investor Carrot (Free Resources) and RehabValuator (Free) are great, however, start with the above recommendations and onboard technology when you are ready. Worried about how you can go about it all? Don't worry – I can help you.
Your first step is formulating a Digital Transformation Strategy and Plan; the next step is taking action. I've always been a believer of one change a day, keeps the doctor away.
P.s. You might be able to tell I just made that up, however, if you can do one little change a day, after a year you have complete 365 things, that's a big list to complete in a weekend, however, in bite-size chunks it is very achievable. Like all things in life, consistency is critical, have a plan and stick to it.
Next PostLet's Get Digital, What The Is Digital?


text:
// Copyright (c) Microsoft.  All Rights Reserved.  Licensed under the Apache License, Version 2.0.  See License.txt in the project root for license information.

using Microsoft.CodeAnalysis.Diagnostics;
using Microsoft.CodeAnalysis.FxCopAnalyzers;
using Microsoft.CodeAnalysis.FxCopAnalyzers.Design;
using Microsoft.CodeAnalysis.Test.Utilities;
using Xunit;

namespace Microsoft.CodeAnalysis.UnitTests
{
    public partial class CA1012Tests : DiagnosticAnalyzerTestBase
    {
        protected override DiagnosticAnalyzer GetBasicDiagnosticAnalyzer()
        {
            return new CA1012DiagnosticAnalyzer();
        }

        protected override DiagnosticAnalyzer GetCSharpDiagnosticAnalyzer()
        {
            return new CA1012DiagnosticAnalyzer();
        }

        [Fact, Trait(Traits.Feature, Traits.Features.Diagnostics)]
        public void TestCSPublicAbstractClass()
        {
            var code = @"
public abstract class C
{
    public C()
    {
    }
}
";
            VerifyCSharp(code, GetCA1012CSharpResultAt(2, 23, "C"));
        }

        [Fact, Trait(Traits.Feature, Traits.Features.Diagnostics)]
        public void TestCSPublicAbstractClassWithScope()
        {
            var code = @"
public abstract class C
{
    public C()
    {
    }
}

[|public class D
{
    public D()
    {
    }
}|]
";

            VerifyCSharp(code);
        }

        [Fact, Trait(Traits.Feature, Traits.Features.Diagnostics)]
        public void TestVBPublicAbstractClass()
        {
            var code = @"
Public MustInherit Class C
    Public Sub New()
    End Sub
End Class
";
            VerifyBasic(code, GetCA1012BasicResultAt(2, 26, "C"));
        }

        [Fact, Trait(Traits.Feature, Traits.Features.Diagnostics)]
        public void TestCSInternalAbstractClass()
        {
            var code = @"
abstract class C
{
    public C()
    {
    }
}
";
            VerifyCSharp(code, GetCA1012CSharpResultAt(2, 16, "C"));
        }

        [Fact, Trait(Traits.Feature, Traits.Features.Diagnostics)]
        public void TestVBInternalAbstractClass()
        {
            var code = @"
MustInherit Class C
    Public Sub New()
    End Sub
End Class
";
            VerifyBasic(code, GetCA1012BasicResultAt(2, 19, "C"));
        }

        [Fact, Trait(Traits.Feature, Traits.Features.Diagnostics)]
        public void TestVBInternalAbstractClassWithScope()
        {
            var code = @"
MustInherit Class C
    Public Sub New()
    End Sub
End Class

[|Class D
    Public Sub New()
    End Sub
End Class|]
";

            VerifyBasic(code);
        }

        [Fact, Trait(Traits.Feature, Traits.Features.Diagnostics)]
        public void TestCSAbstractClassWithProtectedConstructor()
        {
            var code = @"
public abstract class C
{
    protected C()
    {
    }
}
";
            VerifyCSharp(code);
        }

        [Fact, Trait(Traits.Feature, Traits.Features.Diagnostics)]
        public void TestVBAbstractClassWithProtectedConstructor()
        {
            var code = @"
Public MustInherit Class C
    Protected Sub New()
    End Sub
End Class
";
            VerifyBasic(code);
        }

        [Fact, Trait(Traits.Feature, Traits.Features.Diagnostics)]
        public void TestCSNestedAbstractClassWithPublicConstructor1()
        {
            var code = @"
public struct C
{
    abstract class D
    {
        public D() { }
    }
}
";
            VerifyCSharp(code, GetCA1012CSharpResultAt(4, 20, "D"));
        }

        [Fact, Trait(Traits.Feature, Traits.Features.Diagnostics)]
        public void TestVBNestedAbstractClassWithPublicConstructor1()
        {
            var code = @"
Public Structure C
    MustInherit Class D
        Public Sub New()
        End Sub
    End Class
End Class
";
            VerifyBasic(code, GetCA1012BasicResultAt(3, 23, "D"));
        }

        [Fact, Trait(Traits.Feature, Traits.Features.Diagnostics)]
        public void TestNestedAbstractClassWithPublicConstructor2()
        {
            var code = @"
public abstract class C
{
    public abstract class D
    {
        public D() { }
    }
}
";
            VerifyCSharp(code, GetCA1012CSharpResultAt(4, 27, "D"));
        }

        [Fact, Trait(Traits.Feature, Traits.Features.Diagnostics)]
        public void TestVBNestedAbstractClassWithPublicConstructor2()
        {
            var code = @"
Public MustInherit Class C
   Protected Friend MustInherit Class D
        Sub New()
        End Sub
    End Class
End Class
";
            VerifyBasic(code, GetCA1012BasicResultAt(3, 39, "D"));
        }

        [Fact, Trait(Traits.Feature, Traits.Features.Diagnostics)]
        public void TestNestedAbstractClassWithPublicConstructor3()
        {
            var code = @"
internal abstract class C
{
    public abstract class D
    {
        public D() { }
    }
}
";
            VerifyCSharp(code, GetCA1012CSharpResultAt(4, 27, "D"));
        }

        [Fact, Trait(Traits.Feature, Traits.Features.Diagnostics)]
        public void TestVBNestedAbstractClassWithPublicConstructor3()
        {
            var code = @"
MustInherit Class C
   Public MustInherit Class D
        Sub New()
        End Sub
    End Class
End Class
";
            VerifyBasic(code, GetCA1012BasicResultAt(3, 29, "D"));
        }

        internal static string CA1012Name = "CA1012";
        internal static string CA1012Message = FxCopRulesResources.TypeIsAbstractButHasPublicConstructors;

        private static DiagnosticResult GetCA1012CSharpResultAt(int line, int column, string objectName)
        {
            return GetCSharpResultAt(line, column, CA1012Name, string.Format(CA1012Message, objectName));
        }

        private static DiagnosticResult GetCA1012BasicResultAt(int line, int column, string objectName)
        {
            return GetBasicResultAt(line, column, CA1012Name, string.Format(CA1012Message, objectName));
        }
    }
}


Sterling Hearing Aid Center's flagship office is located in Sterling, IL. Kelly Reynolds, Hearing Instrument Specialist and owner of Sterling Hearing Aid Center, provides full service hearing healthcare including hearing screenings, hearing aid fittings, hearing aid cleaning and repairs, and much more. We offer appointment Monday - Friday, 8:00 am-4:00 pm and Saturday 9:00 am-noon. Walk-Ins Are Welcome!


Here's a wonderfully hand crafted sterling silver cuff bracelet, featuring traditional Southwestern designs and genuine turquoise cab, by Anglo silversmith Don Lucas circa late 70s early 80s.
The handsome and hefty weight bracelet consists of a thick gauge sterling silver, incised with traditional Native American motifs and bezel set with an oval green colored turquoise cab measuring 20mm in size.
The cuff measures approximately 5&7/8 inches from end to end with a 1&1/4 inch gap, weighs a considerable 60.3 grams, and is signed "D. Lucas" along with "Sterling ©" and the artist's double arrow logo.


text:
Priest, teacher get prison in sex abuse case
by Joseph A. Slobodzian, Inquirer Staff Writer, Posted: June 12, 2013
Even as they professed their innocence, a Catholic priest and an ex-parochial schoolteacher got long prison terms Wednesday in the serial sexual assault of a 10-year-old altar boy in the late 1990s.
Common Pleas Court Judge Ellen Ceisler exceeded sentencing guidelines in handing out punishment to the Rev. Charles Engelhardt, 66, and Bernard Shero, 50, saying their crimes called for long terms. She sentenced Engelhardt to six to 12 years in prison and Shero to eight to 16.
The sentences stunned the standing-room-only crowd that filled her courtroom.
Engelhardt's frown grew longer as it became clear that Ceisler would not impose probation - served in a Maryland home for old, ailing priests - as urged by lawyer Michael McGovern.
Shero, who knew he faced a mandatory minimum five-year term, stood in shock, and attorney Burton A. Rose put a hand on his arm to steady him.
In the gallery, members of Engelhardt's family began weeping loudly, and court staff ordered them to leave.
"We're not allowed to cry?" said Engelhardt's niece Tracey Wilbekaitis.
Ceisler said she felt the state sentencing guidelines were too low. "The guidelines," she said, "shock my conscience and my sense of justice."
McGovern and Rose told the judge they would appeal.
Engelhardt insisted he had no memory of meeting the former altar boy who said the priest raped him when he was a child. He denied assaulting him.
"I have accepted this injustice and will continue to do so until it is righted," said Engelhardt, "as I believe it will be righted."
Shero, usually quiet and often weeping at trial, was suddenly pugnacious. He berated Assistant District Attorney Evangelia Manos for "twisting the facts," and challenged her: "Go ahead, I know you want to say something, go ahead."
Ceisler cut Shero off, telling him he was "out of line."
Shero's mother, Bonita, pleaded for mercy, telling Ceisler her son had been born with congenital cataracts and has had 23 surgeries that left him with limited sight in one eye.
Shero's thick glasses and his need to get close when he talks made him a target for bullies - and the victim in this case, she said.
Manos angrily dismissed the defense arguments, saying the jury believed the victim: "They just didn't like the verdict."
Engelhardt, a priest for 45 years, was found guilty of child endangerment, corruption of a minor, and indecent assault. The jury deadlocked on a count of involuntary deviate sexual intercourse.
Shero was found guilty of all charges: rape, involuntary deviate sexual intercourse, child endangerment, corruption of a minor, and indecent assault.
Engelhardt had reason to hope for a lesser sentence. Earlier, Ceisler granted McGovern's motion to acquit Engelhardt of conspiracy, a count on which he was found guilty.
Engelhardt and Shero were originally charged in the assaults along with another priest, Edward V. Avery.
Prosecutors alleged that the boy had been sexually assaulted and passed from Engelhardt to Avery to Shero when he was in fifth and sixth grades at St. Jerome's parish school.
Avery, now 70 and defrocked, pleaded guilty last year and was sentenced to 21/2 to 5 years in prison.
Contact Joseph A. Slobodzian at 215-854-2985 or jslobodzian@phillynews.com, or follow on Twitter @joeslobo.
Posted: June 12, 2013 - 10:54 AM
Joseph A. Slobodzian, Inquirer Staff Writer


text:
Tricalysia ferorum är en måreväxtart som beskrevs av Elmar Robbrecht. Tricalysia ferorum ingår i släktet Tricalysia och familjen måreväxter.

Artens utbredningsområde är Kamerun. Inga underarter finns listade i Catalogue of Life.

Källor

Måreväxter
ferorum


UPDATE January 30th, 2015: A few more posts have been written about this same issue, discussing really the merits of the panic or not. Interesting reading.
So today, twitter went crazy announcing that Microsoft had released Microsoft Outlook for iOS which is a re-badged version of the Acompli mail client that they acquired. It was reported that the acquisition was worth more than $200 million, so it should be a great application being released.
I like most people downloaded the App to play with and have to say I like it. Works really well, I like the "PUSH" notifications but then again how does that work? Have you ever wondered that?
"PUSH" Notifications require a fairly complex back end system to work.
To understand this better, an application written for iOS needs to tell the operating system that it wants to get notifications for an App. The operating system contacts Apple's Server and obtains a "device token" that is used to uniquely identify the device that the custom application is running on. The device token is what is used by the custom applications servers to send the push notifications.
The device token is passed along with the actual push notifications to Apple's Servers, then of course Apple's servers distribute the notifications to the devices, hopefully the one you're on.
There are in fact a few providers that you can use to send notifications to a device, a very cool one is Urban Airship, of course whatever vendor you use, you still need to register everything with Apple and your Developer account to make it work. In a normal application like this notifications are triggered by a remote server that says "send notification to Liam" based on some logic.
So back to the Microsoft Outlook for iOS Application. Courtesy to Marc Anderson, who pointed me to this guy's post today, who outlined a few security issues that he was seeing, where even when he removed the App he was still getting iOS notifications.
Thinking that through, that means that something in between the mail server and the device is still able to validate new emails coming in and sending notifications.
I also tested it but did NOT get the same things happening for me, I chose to use an Exchange 2013 Environment. As soon as I removed the App and sent test emails to myself I got NO notifications anymore.
I have been unable to reproduce the same issue, however that does not mean that this does not happen.
The thing that is very interesting though is the wording that is called out from the Acompli Privacy Statement.
So before you go an uninstall it and go crazy, let's just think about what it actually says. Deciding to use any device with a mobile application these privacy statements should be read and picked apart before choosing one. As with any application we use to access email, we need to do a better job on checking what we actually sign up before moving forward.
Now that does not change the issue being called out in the post, of the accounts being saved and used so maybe that is one for Microsoft to look at and address the issues.
Now for you paranoid Administrators, you can use the PowerShell in the last comment on that post which simply tells Exchange to block the app by validating the query string: "Outlook for iOS and Android".
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