text:
Chinese billionaire to scoop up failing car battery maker that got $240M from feds
Posted by aurelius77 on October 12, 2012
A struggling Massachusetts-based company that makes batteries for electric cars and got $240 million in stimulus money is being saved from bankruptcy by a Chinese billionaire who could move operations overseas.
A123 Systems received a $241.1-million grant from the Obama administration three years ago and more than $125 million in State of Michigan tax credits in the hopes that the company would create jobs, while leading the country away from conventional gas-guzzling vehicles and toward clean energy.
But the lithium ion battery maker is now poised to hand over the reins to Wanxiang Group, a Chinese firm, which agreed in a tentative deal last August to invest up to $450 million so that it can take over as much as 80 percent of the company.
A123 Systems supplies batteries to customers, including California luxury carmaker Fisker Automotive. The deal with Wanxiang Group — owned by Chinese billionaire Lu Guanqiu — also raises the question of whether the manufacturer will be forced to move to China. A representative from A123 Systems did not return a request for comment on the matter.
Pike Research analyst John Gartner told the Detroit Free Press that such a scenario is highly probable.
Full article: Chinese billionaire to scoop up failing car battery maker that got $240M from feds (Fox News)
This entry was posted in Axis Powers, Barack Obama, Business & Economics, China, Economic Crisis, Economic Warfare, Espionage, Government Corruption, National Security & Terrorism, Politics, USA and tagged A123 Systems, cars, China, Chinese billionaire, Chinese billionaire to scoop up failing car battery maker that got $240M from feds, Chinese firm, clean energy, company, Detroit Free Press, economic warfare, exporting jobs, lithium ion battery, lithium ion battery maker, luxury carmaker, moving operations overseas, Obama administration, Pike Research analyst John Gartner, Wanxiang Group. Bookmark the permalink.
Reset Claims Nunn-Luga
Officials say Chinese spies have targeted every sector of the U.S. economy


Dreadnaughts?! How about Imperial Star Destroyers! One awesome game.
Notes: 1- or 2-player game; cool Dreadnaught Destroyer patch!
The Dreadnaught Factor is an awesome game. If you're also a Star Wars geek, this game is a dream-come-true for the Intellivision aficionado. How can you not see the first ship as anything but an Imperial Star Destroyer, and your fighter as an X-Wing? Superb graphics, gameplay, and sound effects make this game a standout on the platform.
If you were good enough, and bothered to take a picture of your game score and send it in, you could join the Dreadnaught Destroyers club. I did! Here's my patch - still has the iron-on instructions on the back!
A total of two The Dreadnaught Factor box variants have been documented.
A total of two The Dreadnaught Factor manual variants have been documented.
A total of two The Dreadnaught Factor overlay variants have been documented.
A total of two The Dreadnaught Factor label variants have been documented.
Heck, skip that manual business and just start!
On your way to the jump gate.
You get a good sense of the scale of the ships. Movement and control are smooth and well-done—the bit of inertia in ship control is sweet. Sound is also great.


Kinji Toyama is a second-year student at Tokyo Butei High and the male protagonist of Aria the Scarlet Ammo. He is an E-rank, simply because he didn't take the last term test, while in the first one he was an S-rank, the highest possible rank.
Kinji, when sexually aroused, shifts into Hysteria Mode, in which he is so skilled he can shoot bullets into a gun and swing his butterfly knife too fast to be seen. It is a trait possessed by all the Toyamas, and it is an amplified version of a man's instinct to protect women. Because of that, he cannot directly harm a woman, even if the latter's causing a lot of damage to him. Though he is not above simple tricks, such as in the first episode when Aria was attempting to attack him for looking up her shirt, and Kinji dropped her bullets on the ground so she slipped. Also in Hysteria Mode Kinji acts smug and quite seductive in a way many women fall for him.


The storage system comprises parallel shelves (10) which are mounted on a paternoster-type endless chain system (3, 4, 5a). The shelves are pivoted on the chain so that their axis of rotation intersects an axis of rotation of the chain. An independent claim is included for an item of furniture fitted with the system.
A chain-saw structure, comprising a chain-saw, a bar-frame therefor lying wholly within the cutting plane of the saw, having saw-supporting wheels at its ends and pivoted at one end on the axle of one of said wheels, and a handle at the outer or free end extending beyond the cutting range of the saw.
The chain guide in general comprises a pair of cage plates and two chain pulleys rotationally supported between the cage plates. The movable member connects the chain guide to the linkage mechanism. The movable member connects the chain guide to the linkage mechanism.
Land O' Lakes Resort is a housekeeping facility situated on the Marten River. Break away from your everyday routine and come to an endless chain of lakes.
Find great deals on eBay for endless chain. Shop with confidence.
The endless chain comprises chain links, some of them having transportation discs attached thereto. The transportation discs comprise at least two portions attached together around and against the corresponding chain links.
Paul's treatise on "The Endless Chain," which contains three new throws that deliver different outcomes, advances this plot to new dimensions. It's a fully explained three-phase routine that opens with Patrick Page's classic, "Flash Cash," and ends with a novel finish that leaves the participants utterly dumbfounded.
The endless conveyor comprises a plurality of nut-transporting units connected to and carried by a feed chain. The endless conveyor moves about the sprockets and moves nuts from the hopper to a pickup point.
Purpose of Chain Rotators is rotating and positioning pieces with planar section. Required welding edge can be created by realizing positioning according to the rotated part on an endless chain.


If you have a business-critical project, use these 6 steps to make sure the deployment is a success. This is a process that guarantees your business is safe during the deployment.
The recording lasts about 40 minutes.
With our guides you will succeed in your ERP-project and get the most benefit from our ProjectTOP-software!


text:
\section{Introduction}
Lack of information about neuron population, distribution and morphology at cell level has existed as a critical problem for the study of brain development and aging for many years. Traditionally, neurobiologists estimate manually the number of neurons in the region of interest. However, this method is tedious and subjective because its accuracy relies on the complexity of images. Several automated cell individualization methods have been proposed such as Watershed~\cite{cousty2008watershed} and iCut~\cite{he2015icut}. However, these methods have several limitations. Watershed algorithm can be easily affected by noise in the images, often resulting in over- and under-segmentations. The iCut algorithm proposed to segment touching cells, fails in the regions where massive cells aggregate, and does not take into account size-varying cells such as neurons. Recently, the development of Deep Learning (DL) has revolutionized computer vision. Integrating DL models in cell detection and cell instance segmentation improves accuracy compared to traditional approaches~\cite{falk2019u, cui2019deep, naylor2018segmentation, kumar2017dataset}. To achieve robust and rigorous segmentation, a large training dataset is mandatory. However, pixel-level annotation is laborious and time consuming. In addition, aforementioned methods are designed mainly for analyzing oncological data, in particular H\&E staining, whereas in neuroscience, the study of neurons usually relies on NeuN staining. Moreover, the task of neuron segmentation is extremely challenging due to the variety in neuron shape, size and density in the brain.  To the best of our knowledge, few studies have specifically investigated cell individualization for neurons~\cite{you2019automated}.

In this paper, we propose a weakly supervised DL method for neuron instance segmentation, which requires only point annotations. The main contributions of this work are as follows: i) inspired by~\cite{you2019automated}, we developed a new strategy to synthesize pixel-level labels using Random Forest (RF) segmentation and a competitive Region Growing algorithm; ii) we tested different configurations of DL networks on NeuN stained images. The DL architecture used in this work is Topcoders, which ranked first during the nucleus segmentation competition: Data Science Bowl 2018 (DSB) and demonstrated promising performances on H\&E staining images and fluorescent images stained with DAPI and Hoechst~\cite{caicedo2019nucleus} and iii) by employing an overlapping patch extraction/assembling method [4], we were able to process large high-resolution images despite the limitation of GPU random access memory (RAM).

\section{Materials and Methods}
\subsection{Dataset}
Dataset was derived from a previously published study~\cite{you2019automated}, a representative histological section with thickness of 40 $\mu$ m was obtained from a healthy macaque brain and scanned by an AxioScan.Z1 (Zeiss) with the resolution of 0.22 $\mu$ m/pixel ($\times$ 20 magnification) ($\sim$ 150 GB). Based on 30 images of 5000 $\times$ 5000 pixels, 24 images were chosen to create the training dataset including 11k patches (224 $\times$ 224 pixels), 6 other images were chosen as the test dataset. The datasets contained the main anatomical regions where the neuron distribution is investigated including cortex, hippocampus, caudate, etc. The datasets presented a rich diversity in terms of neuron shape and size, with both sparse and highly aggregated neuron distributions, as illustrated in Figure \ref{fig:fig1} .

\begin{figure}
  \centering
  \includegraphics[width=0.7\textwidth]{3-3_training_images.png}
  \caption{Training dataset examples: (a) caudate, (b-c) cortex, (d) hippocampus, (e) subiculum and (f) thalamus.}
  \label{fig:fig1}
\end{figure}

\subsection{Pipeline of pixel-level label synthesis}
Traditionally, cell instance segmentation is addressed as a binary classification problem, the output of the classifier contains two classes: cells and tissue background. Recent works indicate that a more accurate segmentation can be achieved by classifying pixels into three classes: inter-cell contours, interior of cells and background~\cite{caicedo2019nucleus}. We designed an algorithm for constructing pixel-level labels including these three classes, based on centroid point labels provided by the expert for each neuron and binary semantic segmentation results produced in previous works~\cite{you2019automated} (as shown in Figure \ref{fig:fig2}. i) The binary segmentation of neurons and background was generated by applying a RF model of 100 decision trees, which was trained and optimized with following features: H, S, V color channels and local intensity~\cite{you2019automated}. ii) Manually pinpointed centroid labels were identified by an expert, a disk with a radius of 5 pixels in the center of the neuron was marked as a point label (size and form adapted to visual checking and region growing initialization). iii) A competitive region growing process was applied to define pixel-level labels, the expansion of sub-region was constrained using the segmentation result of RF. Each pixel inside the neuron had the same label as its centroid. By applying the contours generated by region growing on raw image Figure \ref{fig:fig2} (d), we visually assessed the quality of synthesized labels; iv) based on the pixel-level labels, three-classes masks were generated using morphological operations, including background, neurons and the border of touching neurons (thickness of 4 pixels), which is proved to enhance the segmentation result~\cite{caicedo2019nucleus}.

\begin{figure}
  \centering
  \includegraphics[width=0.7\textwidth]{3-4_label_synthesis.png}
  \caption{Flowchart of pixel-level label synthesis: (a) raw image, (b) RF segmentation, (c) point labels, (d) region growing result (red contours) and (e) synthesized masks (black: background, blue: neurons and green: inter-cell contours).}
  \label{fig:fig2}
\end{figure}

\subsection{Neural network architecture}
We applied the winner algorithm of the 2018 Data Science Bowl~\cite{caicedo2019nucleus}, an ensemble model of 8 U-Net-like encoder-decoder architectures, with encoders pretrained on ImageNet database, including three ResNets (34, 101, 152)~\cite{he2016deep}, two Dual Path Networks (DPN) 92~\cite{chen2017dual}, two DenseNets (121, 169)~\cite{huang2017densely} and one Inception-ResNet~\cite{szegedy2017inception}. Such strategy also enabled us to compare the performance of the different neural networks individually and to evaluate the effectiveness of the entire model. Pretrained model derived from DSB (Topcoders\_bowl)~\cite{caicedo2019nucleus}, model trained on the neuron dataset (Topcoders\_neuron) and its constituents (8 models) were all tested. Training dataset was randomly divided into two groups ($ \frac{3}{4} $ and $ \frac{1}{4} $) for training and validation respectively. A heavy data augmentation was applied to prevent over-fitting, including rotation, flipping, channel shuffling, color inversion, etc. The training set was expanded to 6 times compared to its original size. Once the training of DL models was accomplished, a post-processing step aiming to optimize the segmentation results was applied: a regression model (gradient-boosted trees) was trained to predict Intersection-over-Union (IoU) for all cell candidates, candidates with small predicted I oU ($<$ 0.3) were removed in order to decrease false predictions.

\subsection{Overlapping extraction \& assembling}
The test dataset contained 6 large high-resolution images (5000 $\times$ 5000 pixels) which included various anatomical regions. However, most DL based segmentation algorithms cannot process such large-scale images due to GPU RAM limitation. Moreover, one constraint of CNN is that the prediction at the border of the input image is not accurate due to the lack of context information. To address this problem, we adopted the strategy of overlapped patch extraction and assembling proposed in [4]. The patches were extracted from raw images by a sliding window of 1340 $\times$ 1340 pixels (determined according to the GPU RAM resources), a stride of 1220 pixels in height and width produced an overlap of 120 pixels. The prediction results of patches were seamlessly stitched to reconstruct the final result (5000 $\times$ 5000 pixels) using the same settings as well as a weight map, which was applied to each predicted patch so that the pixels closer to the edge of the patch have lower weights. The use of the weight map reduced the impact of inaccurate prediction of pixels at the border of the patches.

\subsection{Evaluation metrics}
To evaluate the proposed method, we computed F1 score (F) for both detection (det-F1) and instance segmentation (seg-F1) tasks:

\begin{equation}
P=\frac{TP}{TP+FP}; R=\frac{TP}{TP+FN}; F=2\frac{P\times R}{P+R}
\end{equation}

where True Positive (TP), False Positive (FP) and False Negative (FN) represent the numbers of true, false and missing detection/instance segmentation respectively. For the detection task, a neuron detected was considered as a true positive when it was superimposed with exactly one centroid defined by the expert. As for the instance segmentation, the true positive was defined as the IoU greater than 0.5 between the detected neuron and the corresponding label.

Dice coefficient was calculated to evaluate the semantic segmentation. Another evaluation criterion was the relative count error (RCE):

\begin{equation}
\varepsilon =\frac{\left | N_{a}-N_{e} \right |}{N_{e}}
\end{equation}

Where N\_a, N\_e are the number of neurons detected by the proposed method and the expert respectively.

\subsection{Training details}
Models were trained using PyTorch, Keras and Tensorflow frameworks. Each model needed different epoch number to converge (from 17 to 70 depending on network depth), with Adam optimizer and a starting learning rate of 1e-4 which decreased during the training. DL models with ResNet101, ResNet152 and one of DPN encoders used sigmoid activation, the other models used softmax activation. For loss calculation, the combination of soft dice and binary cross-entropy/categorical cross-entropy was chosen for sigmoid/softmax activation respectively.

This work was conducted on a workstation equipped with bi-processors (operating system: Ubuntu 16.04 LTS 64-bits, CPU: Intel Xeon E5-2630 v3 at 2.4 GHz, RAM: 128 GB, GPU: NVIDIA GTX 1080Ti).

\section{Results}
The results of Topcoders\_bowl, Topcoders\_neuron and its constitutive models (named according to the encoder network) were evaluated on a test dataset including $\sim$16k neurons. Table \ref{tab:table} reports the performance of neuron detection (det-F1), instance segmentation (seg-F1), semantic segmentation (Dice) and neuron counting (RCE).
Although the training dataset of Topcoders\_bowl did not include neuron data, it was able to detect most neurons correctly (det-F1 score of 0.83 and RCE of 0.22). Nevertheless, it performed less well in both instance (seg-F1: 0.71) and semantic (Dice: 0.75) segmentation. A significant improvement of 10\% was obtained by training with NeuN stained data. Topcoders\_neuron achieved the highest detection accuracy (det-F1: 0.927), it was also one of the best models for instance segmentation (seg-F1: 0.87). Among the constitutive models, ResNet101 outperformed others in neuron detection (det-F1: 0.926) and counting (RCE: 0.037). DPN softmax was the best model for instance and semantic segmentation (seg-F1: 0.88 and Dice: 0.95), followed by ResNet34 (seg-F1: 0.87 and Dice: 0.93).

\begin{table}
 \caption{Detection and segmentation performance of Topcoder\_bowl, Topcoder\_neuron and its constitutive models. Best and second best results are in bold with the best also underlined.}
  \centering
  \begin{tabular}{llllll}
    \toprule
    \multicolumn{2}{c}{Model}	&	det-F1	&	seg-F1	&	Dice	&	RCE	\\
    \midrule
    \multicolumn{2}{c}{Topcoder\_bowl}     &	0.825	& 0.705	&	0.751	&	0.219	\\
    \midrule
    \multicolumn{2}{c}{Topcoder\_neuron}     &	\underline{\textbf{0.927}}	& \textbf{0.870}	&	0.928	&	0.040	\\
    \midrule
    \multirow{8}{*}{\rotatebox[origin=c]{90}{Constituents}}	&	DenseNet121	&	0.910	&	0.783	&	0.830	&	0.063	\\
     & DenseNet169	&	0.912	&	0.839	&	0.869	&	0.062	\\
     & DPN sigmoid	&	0.869	&	0.803	&	0.885	&	0.130	\\
     & DPN softmax	&	0.918	&	\underline{\textbf{0.880}}	&	\underline{\textbf{0.951}}	&	0.059	\\
     & Inception-ResNet	&	0.901	&	0.848	&	0.910	&	0.116\\
     & ResNet34	&	0.914	&	\textbf{0.870}	&	\textbf{0.934}	&	0.088	\\
     & ResNet101	&	\textbf{0.926}	&	0.868	&	0.921	&	\underline{\textbf{0.037}}	\\
     & ResNet152	&	0.923	&	0.865	&	0.923	&	\textbf{0.038}	\\
    \bottomrule
  \end{tabular}
  \label{tab:table}
\end{table}

To better compare the performance of each model, the detection accuracy (det-F1) distribution of the 10 models are plotted in Figure \ref{fig:fig3}.  Topcoders\_neuron, which had the best average detection accuracy on the test dataset, was also one of the most robust models, it performed well for all tested anatomical regions. The best constitutive model, ResNet101, was also robust, whilst it performed less well for most tested anatomical regions than Topcoders\_neuron.

\begin{figure}
  \centering
  \includegraphics[width=0.7\textwidth]{3-5_boxplot.png}
  \caption{Comparison of detection performance (det-F1) on the test set (several outlier values are below the vertical scale range).}
  \label{fig:fig3}
\end{figure}

Figure \ref{fig:fig4} shows cropped examples of synthesized masks and segmentation results in three anatomical regions. Neurons are presented by colored labels. The results are displayed according to an increasing density of neurons (first row: caudate, sparse; second row: cortex, dense and last row: hippocampus, very dense). For better illustrating the results, the neurons in these regions were segmented manually, as shown in Figure \ref{fig:fig4} (b). Figure \ref{fig:fig4} (c) shows the synthesized pixel-level masks, compared to the manual annotations, the proposed method produced satisfying masks for most regions (both in distribution and shape based on visual evaluation). Figure \ref{fig:fig4} (d) illustrates the results of Topcoders\_bowl, the neurons were correctly detected in the sparse regions, but the contour of neurons were often distorted. Moreover, the region of massive touching neurons was wrongly considered as the background. Figure \ref{fig:fig4} (e) presents the predictions of Topcoders\_neuron, it was a solid model performing very well for all tested anatomical regions, at both object-level and pixel-level.

\begin{figure}
  \centering
  \includegraphics[width=\textwidth]{3-6_segmentation.png}
  \caption{Results of pixel-level label synthesis and segmentation in three anatomical regions. Top row: caudate; Middle row: cortex; Bottom row: hippocampus. (a) Raw images, (b) manual segmentation, (c) synthesized pixel-level masks and (d, e) results of Topcoders\_bowl and Topcoders\_neuron respectively.}
  \label{fig:fig4}
\end{figure}

\section{Discussion}
Topcoders\_bowl was believed to be a well generalized model based on DSB 2018 results. It successfully separated neurons in regions with sparse distribution while it did not respect the neuron shape and it failed in regions where massive neurons aggregated. This is probably due to the absence of cells with various forms and highly clustered distribution such as neurons in the dataset of DSB.

The performance of Topcoders\_neuron demonstrated the superiority of the ensemble model. Although the results of Topcoders\_neuron were obtained by combining the predictions of the constitutive models, it achieved better results than most constituents for all the tasks.

Among all the constitutive models, all ResNet backbone models performed well, including ResNet34, the best model for semantic and instance segmentation, and ResNet101, the best constitutive model for neuron detection. Generally, a deeper network can capture more complex features. While the deepest model ResNet152 did not achieve the best performance in any task, it might be related to the fact that deeper networks are generally more difficult to train owning to the vanishing gradient problem~\cite{tan2019efficientnet}. Another interesting finding is that the choice of activation had an important influence on segmentation results. Although two DPN models had exactly the same architecture, the one with softmax activation performed better than that with sigmoid activation.
Since most tested neural networks achieved good results, we believe that it is feasible to apply DL techniques for neuron counting. However, compared to stereology~\cite{west1991unbiased}, which takes into account the thickness of tissue for possible superimposition of cells and provides unbiased quantifications, DL methods can only deal with 2D images and provide a valuable estimation of cell counting. A specific dataset and study need to be designed to quantify discrepancies between DL methods and stereology.

\section{Conclusion}
In this work, we investigated the ability of a weakly supervised method to specifically detect and segment neurons in NeuN stained histology images. By applying state-of-the-art DL architectures, this study provides the first comprehensive assessment of different neural networks for neuron individualization. An optimal model trained using neuron data was obtained, it was able to separate size, shape and density-varying neurons successfully. Experimental results in the main anatomical regions demonstrated the effectiveness of the proposed method against the default DSB model. The current study was carried out with the default settings, further optimization in training parameters and architecture need to be investigated. Besides, developments in high-performance computing (HPC)$\dagger$ are also ongoing to test the efficiency of cross-validation. Further work is required to establish a comparative analysis of Topcoders and other deep learning-based instance segmentation methods, as well as stereology – the reference method used in biomedical analysis. An exciting perspective will be to extend this study to whole sections and brains, which will improve our understanding of brain development, aging and neurodegeneration.

\section*{Compliance With Ethical Standards}
The experimental procedures involving animal models described in this paper were approved by the Institutional Animal Care and Ethics Committee.

\section*{Acknowledgment}
$\dagger$ This work was granted access to the HPC resources of TGCC under the allocation 2019-(A0040310374) made by the GENCI.

\bibliographystyle{unsrt}

redpajama_set_name: RedPajamaArXiv


text:
Q: Using div tag in html without using css I have to make an html page without using css...
the problem is that, that i want to use div tag and add an image as my background and write some content over the image, so please suggest me ways to do it.
Thanks in advance!

A: Some examples, with this you don't need to include/insert all css file
<body style="background-image:url('http://example.com/background.png')">

or
<div style="background-image:url('bakground.png')">

Without css you can't set background

redpajama_set_name: RedPajamaStackExchange


text:
Channel 1: Police to question Olmert on Cremieux home affair
Prime Minister Ehud Olmert will be summoned by police in the next few days for questioning on the Cremieux Street home affair, Channel 1 reported Tuesday. Olmert is suspected of receiving a deduction of hundreds of thousands of dollars in the price of his Jerusalem home in return for using his influence to obtain substantial building concessions for the contractor from the Jerusalem Municipality. Channel 1 also said that Olmert's former law partner and close friend, Uri Messer, would also be investigated again. Olmert, during his 2003-2005 tenure as minister of industry, trade and labor led the Investment Center which was operated by the ministry. During that period, investigators believe Olmert may have given Messer favorable treatment, granting 'approved industry' status to Silicat Industries, Inc. - which Messer represented.

redpajama_set_name: RedPajamaCommonCrawl


text:
Ксенофо́нтов — русская фамилия. Женская форма .

Известные носители

 Ксенофонтов, Гавриил Васильевич (1888—1938) — исследователь якутской этнографии, фольклора и истории.

 Ксенофонтов, Игорь Борисович (1939—1999) — заслуженный тренер России по фигурному катанию.
 Ксенофонтов, Павел Васильевич (1890—1928) — руководитель антибольшевистского движения в Якутии.
 Ксенофонтов, Тимофей Иванович (1912—1990) — российский советский живописец и график.
 Ксенофонтов, Филипп Алексеевич (1903—1938) — партийный деятель ВКП(б).

redpajama_set_name: RedPajamaWikipedia


KickBoxFit is an exciting non-contact fitness class based in Ho Chi Minh City, Vietnam, aimed at both men and women. KickBoxFit classes combine high tensity interval training (HIIT) and kickboxing techniques.
2. What does a typical KickBoxFit class consist of?
Classes start with a warm up then move onto some circuits, high intensity interval training and resistance training. After a little break, we spend a short amount of time teaching kickboxing techniques, then it's time to increase the intensity again by punching and kicking the kickboxing pads and heavy bags.
3. What are the health benefits of KickBoxFit?
.....and arguably the most important... it's extremely FUN!
4. Will I get big arms and shoulders if I join KickBoxFit?
This is a question we get asked a lot by worried females! The answer is no, you will not get big arms and shoulders. KickBoxFit promotes fat loss and does build muscle so your arms and shoulders will begin to change shape after a number of weeks of training and begin to look stronger and more athletic, but they will not be big or manly. You need to lift heavy weights and eat a lot for this to happen!
5. How long does a class last?
The first ten minutes of each class is to wrap your hands and prepare for the session. Exercise in each class lasts one hour.
6. What do I need to bring to a class?
We provide hand wraps, boxing gloves, towels and water. You just need to wear workout clothes ready to train.
7. I've never done boxing or martial arts before, can I still attend a class?
Of course. We have had several members who joined as complete beginners and we adjust the classes to cater for all levels of ability.
8. Will I get hit or hurt during a class?
No. There is no contact with other members of the group. After we have done our circuits and high intensity interval training, we continue our workout by punching and kicking pads and heavy bags. There is absolutely no sparring, fighting or contact between KickBoxFit members.
9. I don't want to be a kick boxer, I just want to get fit, is KickBoxFit right for me?
Absolutely. We use kickboxing techniques as part of our workout. We do not teach people to be kick boxers or how to fight.
10. Who is the coach?
The coach is Mike Sherry from England. He has competed in kickboxing, Thai boxing, Brazilian jiu jitsu and MMA in the UK and Thailand. He created KickBoxFit in 2017 after consistent positive feedback from personal training clients and demands for him to form a class using his expertise in a number of training methods. His highlight video can be found on the 'Meet The Team' page of this website.
11. How many people are in a class?
We currently have space for around 20 people in a class.
12. How much does a class cost?
NB - All packages are non-refundable an non-transferable.
13. When do KickBoxFit classes take place?
Please refer to the timetable on our "schedule" page on this website.
14. Where do KickBoxFit classes take place?
Parking is reserved for Food House customers so please park in Co-op Mart which is a very short walk away.
15. Is KickBoxFit for males and females?
Yes! Both males and females of varying fitness levels and abilities attend the class and all get a great workout.
16. I'm really out of shape and haven't done any exercise in a long time, will I survive a class?
Yes! Unlike other fitness classes that might require you to complete a certain number of exercises, our workouts require you to exercise in a time limit, therefore you complete what you can in that time. This is beneficial because if you are out of shape, you complete the number of sit-ups or squats, for example, required to tire you out in the time limit. However, the fitter you get, the more you can complete in the time limit; this means the exercise never really gets easier and you will always get a great workout!


Think You Should Forget Email? Stop Right There – Growing your List Has Never Been More Important.
Particularly for small and medium sized businesses, there is a never-ending to do list of daily tasks to keep the business running efficiently. On top of that, a small business can never really take its eye off marketing and always nurturing its customers to keep the business growing.
But in this world of social media chatter and sales funnels, is email dead? Does it really matter that we capture every address? And once we have them – does our audience really want to hear from us everyday? If not, then how often? Managing emails for a massive database sounds like a daunting task with little pay-off. Is it really worth it?
Email is crucial in the pursuit of growing your business for a number of reasons. First, let's take a look at why you need an email list.
Consistently, email outperforms every other channel. For one thing, with email, you are going directly into someone's inbox, rather than hoping your 'target customer' sees your social or digital ads.
Furthermore, as social rules continue to change, owning your database (and building it out with robust data points in a CRM like Salesforce) is crucial – otherwise, you're just renting those contacts and they could be ripped away from you at any time.
And for those customers that have engaged with your site or social channels in the past, but don't have that reason to return just yet – email is your way to stay connected with them while they are away from your business.
Finally, if we haven't sold you yet, building an email list is one of the most powerful ways to learn more about your customers through how they engage with email. You can monitor open rates, preferences for frequency of communication, the type of content they prefer and continue to segment your list as you hone in on their behavior.
So, how do you grow that email list into the powerful database that you want?
As should always be the driving principle behind communication, lead with value.
Create a call to action (CTA) over Facebook for followers to 'sign up' for your newsletter. Make sure you clearly communicate what type of content and what sorts of 'value' audiences will receive via your emails. Sell the value, rather an keeping it vague.
Encourage subscribers to share: Consider adding a footer at the close of each email, suggesting those that loved the content share with a friend and include a link to where they can opt-in for your emails. Nothing resonates more than the recommendation of a trusted friend.
Create multiple subscription types: Every customer is at a different point in his or her lifecycle, and may be looking for different benefits from engaging with your brand. Use multiple ways to engage and deliver value as a way to service and learn more about these customer groups. For example, some customers are only looking for promotional offers, while others are seeking tips or news updates. Provide your audience a way to communicate their preferences to deliver the best results.
Launch an online contest to gather an influx of emails: If you're wondering how to grow your business leads quickly, offering a relevant 'give-away' or special promo is a great way to capture contacts (and email addresses) that are interested in your services. Once in the funnel, you can engage with them on a deeper level to promote your brand.
Sure, growing your list sounds great, but once you've got all those contacts… how do you manage the different segments and lists. It's not as overwhelming as it sounds.
Here, is where having a CRM like Salesforce is crucial. Leveraging your database effectively, is all about understanding how key customer criteria aligns to your different service offerings.
Get clear on 'who' is the target customer for each of your product offerings. Create engaging content designed around these customer groups and key checkpoints along the customer journey.
Build out these areas of engagement via automated workflows directly in Salesforce, using templated emails and data points captured in your CRM to make this whole process automatic.
Continue to gather insights and iterate on the data to segment your customer groups more effectively based on their behaviour and test content designed to re-engage inactive users.
And – trust us – if you want to save time in the long-run, build a strategy that aligns to your CRM, so you can keep your data clean from the start. This means understanding which fields you need to capture from customers from the outset and ensuring you have those fields present in every landing page.
When done right, email is a powerful way to build a connection with your audience and become part of their lives on a daily or weekly basis. It can even be used as a form of two-way communication to help you segment your customers more effectively and market to them better, by integrating your process with a CRM like Salesforce.
To learn more about how the Salesforce CRM will amp up your email marketing strategy, helping you better target leads, segment customers and increase sales, set up a discovery call with Sol Business Solutions today.