The Best Tips for Perfectly Priced Travel.
My brother and his wife (who runs this awesome blog btw!), have been living in Singapore for a little while and I've been keen to see them; not to mention my ex housemate L having moved to Australia, it seemed it was high time to high tail it across the world.
I've picked up a few tips along the way for securing great flight and hotel deals, as well as alternatives to the usual. Grab yourself a cuppa, get ready to create a Pinterest board like this one and let's talk through my tips for perfectly priced travel!
This is a tough one, but everyone has to start somewhere 🙂 for me, I found it easier to withdraw cash and put it in a jar. Even if you commit to just £10, or leaving all your loose change in there, you'll be surprised at how quickly you can total out your savings.
OK – this trick saved my travels. I'd been looking on Skyscanner for a while, going back and forth and checking what was available. I knew I had I needed £600 for the flights I wanted; London, to Singapore and returning from Melbourne back to the UK again… I was all ready and set to go, when the prices went up from £800, to £1200(!). I was gutted. My big sister saved the day though with this little trick!
When you're on Skyscanner, you're being cookied. They 'watch' you leave, and then return, and serve you higher prices as they know you have a strong intent to purchase, and are therefore more likely willing to pay more to secure your flights.
You can overcome this by either clearing your history or browsing in a 'Private Window'. In the end, I got my flights for just under £600.
I'm a bit nervy when it comes to flying, so I was pretty chuffed to have locked in these flights with Emirates.
Booking.com is great for loads of options. They show you the price % decrease. Booking.com will also serve you independent reviews however if you're a worrier like me, or you just like to have a nose at where you'll be staying just check the name on Trip Advisor or look directly at the hotel website. Again I'd advise doing this as a "superior" room which looked amazing from the pictures, was actually a "standard" room. Also great as most are pay on arrival, with free cancellation policies.
Air B n B – if you're more in the market for a home-stay, Air B n B is your friend! You can select the various filters that you want (wi-fi, hot tub etc!) and then go from there. You can sense check what the owner is like by checking against the reviews. Cancellation policies here vary from host to host and you can incur a fairly big fee should you wish to cancel depending on what your host has opted for.
Also – if you don't fancy 'house sharing' double check to see if the owner lives in the property too. You can also just do room rental which is a great option. It's the perfect way for getting to be "part" of a city!
I've also been using Trivago to some degree which is really good and operates on a similar principle to Booking.com – I just think the user interface is much cleaner.
Not only is it GREAT for dreamy inspiration, but it will also help you find loads of great trips direct from travellers; we found some really good tips on visiting Bali in cute / handy little infographics. It's the perfect way to pick up little tips that you would usually only get from people who have been to the more remote parts of the places you're going!
I can't wait to share my travels with you!
Good for you for working to overcome the anxiety, I can't wait to see/hear about all your upcoming trips!
Oh my gosh I know! I hadn't even thought about the fact you could do that on the likes of Expedia – thanks for *your* tip!
Great tips! I love finding a good deal on Air BnB. Also when it comes to saving, you're right, you have to start somewhere. My boyfriend started putting all of the pound coins he received into a tub and soon he had a hundred of them!
I know! It's funny how quickly you save when you mindlessly collect!
These are some great tips, Skyscanner and defo Airbnb's have been used for our trip!
Fabulous tips! I'm not actually on Pinterest but I'll need to make an account to join in on the wanderlust pics & inspo!
Good luck on your travels, I hope you have a lovely time! Looking forward to hearing all about them!
Aw perfect I'm so glad they helped!
I was absolutely amazed when I found out that you could be "cookied" when booking travel, it's certainly a very cheeky approach, you wouldn't think it'd be legal! I always book in a private browser now, it's definitely a good way of making everything more cost-effective! I've always been a bit nervous about Air B&Bs but I think I need to give it a go!
So glad to hear that you're not letting anything hold you back with your travels this year, you go girl!
Aww thanks for your lovely comment Abbey! I know I definitely agree it's quite cheeky!
I had the same worry – I wasn't too sure about them, but as it stands I find the reviews quite a source of comfort, as people tend to be very honest. I've also been quite fortunate in that the Air B n B hosts I've had have often offered (after I've stayed with them) to book with them direct in future, which means you don't succumb to the % mark up that Air B n B put on top of the stay fare.
Thank you for your lovely comment! I def agree you find so many good deals on there don't you! I'm so excited to book some more trips through them! Thank you for your lovely comment!
Skyscanner and bookings.com is definitely a 'go-to' of mine!
It's incredible that you've managed to overcome your anxiety! You go girl! Enjoy all your travels!
Awww Steph this was such a lovely comment, thank you so much!
To save money I have a little rule that I don't spend anything smaller than a £10 note! I put all £5 notes and change in a jar and it helps me save so well! The private window idea is fab, I never thought of that!
Oooh I love that! I have a little jar too but just threw anything in there – great idea on nothing smaller than £10!
Some great tips here! We're going to be flying to New Zealand later in the year so I'll be using some of these.
Yay I'm glad it could be of help!
Ooh thanks so much for these tips! My other half and I managed to save a lot of money for when we went to Japan because of skyscanner, and even more so when we stayed at an Airbnb instead of a hotel. It makes me want to travel a lot more now!
m going to have to download some of the longest movies I can think of! Lord of the Rings trilogy any one? Ha! Thank you for your lovely comment – I def agree, sometimes you have to put yourself first!


Daniel C. Dennett is a Co-Director for the Center for Cognitive Studies of the Tufts University with five videos in the C-SPAN Video Library; the first appearance was a 2003 Speech. The year with the highest average number of views per program was 2019 with an average of 1,451 views per program. Most appearances with John Brockman (2). Most common tag: Science & Technology.


text:
Musa Naranj (în , de asemenea, romanizat ca Mūsá Nāranj) este un sat din Districtul rural Sar Firuzabad, Districtul Firuzabad, Shahrestānul Kermanshah, provincia Kermanshah, Iran. La recensământul din 2006, populația sa era de 136 de locuitori, în 36 de familii.

Note

Localități din Iran

redpajama_set_name: RedPajamaWikipedia


text:
2021 MG ZS EV Launched in India at Rs. 20.99 lakh
Published by Vivek kumar on February 9, 2021 February 9, 2021
In E vehicles Segment 2021 MG ZS EV now offered with higher claimed driving range of 419 km on a single charge; offered in two variants
MG Motors India has announced the launch of the new 2021 ZS EV in the Indian market. The car is now priced at Rs. 20.99 lakh. MG motors are the new seller in the domestic market. It's just a second product in the market. Last year the car was the second most selling EV after Tata Nexon.
Last year MG MOTORS have sold 3160 units of MG ZS EV. The car is also listed as one of the safest cars in the country with 5-star safety ratings. The Zero-emission car is loaded with premium features. This is a globally well-reputed car.
2021 MG ZS EV
Moving towards the new 2021 MG ZS EV The car is equipped with the latest connectivity technology comes with an improved range of 419KM in Single charge. The dashboard features a big 20.32 cm touchscreen infotainment system. The infotainment system is suitable for both Apple car play and android autoplay.
The one most interesting features you will witness that is introduced for specially Indian market. This new i-smart system supports Hinglish voice commands and it can understand 35 Hinglish commands like hello MG AC CHLAO, HELLO MF SUNROOF KHOLO, and many more like this.
Tags: 2021 MG2021 MG ZS2021 MG ZS EVCar

redpajama_set_name: RedPajamaCommonCrawl


Zach (Dylan Minnette) and his mom have just moved to a small town in Delaware after Zach's father died. Zach's mom is the new Vice Principal at the local high school. Zach immediately forms a friendship with his neighbor Hannah. Zach has no friends and Hannah (Odeya Rush) is a home schooled shut in. It turns out that her father is the world famous author R.L. Stine. Stine (Jack Black) warns Zach to stay away, but after he catches Zach and Hannah out he punishes her. Zach is afraid she is actually going to be hurt and breaks into the house.
During the break in, one of Stine's manuscripts is accidentally opened and the Abominable Snowman from the R.L Stine novel The Abominable Snowman of Pasadena escapes. Zach and company hunt down the snowman only to discover that the infamous dummy Slappy was also freed. Slappy then took the liberty to free all of Stine's other creations and burned the manuscripts. Stine and company must find a way to put the monsters back in the manuscripts.
The acting is unremarkable, but adequate enough. The story is simple and ridiculous all at the same time. It has an unnecessary love story that works at times. I was a huge fan of the Goosebumps series as a kid and I liked the movie despite its flaws.
It is a nostalgia fest that revisits great childhood stories if you are of a certain generation. See it.


Not that China's much-hyped Belt and Road Initiative (BRI), which calls for massive investment in and development of trade routes, has been without trouble in Africa. Just this week Sierra Leone scrapped plans to build a China-funded 318-million US dollar airport outside the capital, Freetown. This is the first instance of an African country announcing the cancellation of a China One Belt One Road project. Both the World Bank and the International Monetary Fund had warned that the project could place an unnecessary debt burden to Sierra Leone.
It's not too late to apply all brakes and parachutes. In my 60 years experience I never met a chinese brethren, who was a courteous driver and yielded to MY way of right, or did not fail to forget being able to speak English when I returned goods that did not work.


With each passing day, humans are becoming more and more accustomed to convenience and multi-functionality features in their daily life. There was a time when people would only want looks and elegance when buying furniture. Class and the quality of material would be their highest concern. They were not worried about paying high prices. All they wanted was the furniture in their houses to look good. All this has changed with development of modern advanced furniture designs. One such design that has changed the concept of furniture is wardrobes with an assembly service.
Wardrobes with an assembly service are a bold new furniture idea. Wardrobes have always been an important part of all houses. You will need separate wardrobes in all of your bedrooms. Adults and children both need their own wardrobes. You want distinct styles in wardrobe to meet the style of each room. Now with the addition of wardrobes with an assembly service in furniture designs, these types of furniture have been given extra functionality. Here are a number of things that you should look for when you are considering buying wardrobes with an assembly service.
First, you need to look at the convenience factor. A wardrobe with an assembly service adds a lot of convenience to your home. The first advantages of wardrobes with an assembly service are their unique design and style. They can add a touch of uniqueness never before possible. They art multi-purpose as well: they can be used in many ways. Wardrobes come in different styles these days, such as with sliding doors, glass doors, mirror doors and so on. With these distinctive features adding style to the wardrobe, the assembly service makes it even more convenient for purchasers.
This service is very useful for people who are accustomed to moving to new places. If you ever have to move to a new house, you can easily disassemble your wardrobe and move it with ease. Similarly, it gives you convenience inside the house as well. You can move it from one room to another whenever you want to change the décor of your house. You can simply change the entire look of a room by changing the position of your wardrobes with an assembly service.
Another important feature to consider when buying wardrobes with an assembly feature is to thoroughly study their assembly mechanisms and instructions. Some wardrobes with an assembly service are more complex than others. Good wardrobes with an assembly service come with manuals. You can use the how-to-manuals to assemble or disassemble the wardrobe whenever you want. You should make sure that the wardrobe you buy is easy to assemble and its manual is readable. Once you get your wardrobe, assemble it carefully first before you place it in position to see if there is any fault.
You can check out internet websites and the catalogues of various furniture shops to go through the details of wardrobes with an assembly service. Read about all their distinctive features and compare prices. If you are willing to spend some time, you will easily find suitable furniture for yourself.


text:
The Roland JX-305 Groovesynth synthesizer was produced in 1998 by Roland.  It is very similar to a Roland MC-505 with an additional set of 61 keys. The keyboard is sensitive to velocity and has channel aftertouch. This keyboard was aimed at producers of dance and trance music and was during the late 90s also popular for live performances.

The synth has 9 MIDI control knobs and a 2 line LCD display.

Specifications
Keyboard
61 keys (with velocity & channel aftertouch)

Maximum Polyphony
64 voices

Patches
Presets: 640
User: 256
Card: 512

Rhythm Sets
Preset: 32
User: 20
Card: 20

Effects
Reverb
Delay
Multi-effect

Sequencer
Tracks: 8 + Mute Control Track
Resolution: 96 ticks per quarter note
Tempo: 20.0 - 240.0
Songs: 50

Patterns
Presets: 274
RPS: 494
User: 200 (maximum)
Card: 200 (maximum)

Maximum Note Storage
Internal: approximately 75,000 notes
Card (option 2mb): approximately 220,000 notes
Card (option 4mb): approximately 440,000 notes

Quantize
Grid
Shuffle
Groove (71 types)

Arpeggiator
53 styles

RPS Set
60

Display
LCD: 16 characters, 2 lines, beat LED

Connectors
Output Jacks (L(Mono), R)
Phones Jack
MIDI Connectors (In, Out, Thru)
Pedal Hold Jack
Pedal Control Jack
Pedal Switch Jack
Memory Card Slot

Power Supply
AC Adaptor (DC9 V)

Current Draw
450 mA

Dimensions
Width 1011 mm (39-13/16 inches)
Depth 289 mm (11-7/16 inches)
Height 83 mm (3-5/16 inches)

Weight
6.9 kg / 15 lbs 4 oz (excluding AC adaptor)

Accessories
Quick Start
Owner's Manual
AC Adaptor: ACI-120C, ACI-230C, ACB-230E, ACB-240(A)

Options
SmartMedia: S2M-5 / S4M-5

References

Further reading

JX-305
Polyphonic synthesizers
Digital synthesizers


I want to describe the Body as a Book, a Book as a Body, and this Body and this Book will be the first Volume of Thirteen Volumes.
— From the first book, "The Agenda"
— 1949 Nineteen Eighty-Four, pt.1, ch.1.
— Reported in Eva Shaw, For the Love of Children (1998), p. 133.
1,000 children have been killed, and their bodies are left in the streets and are being eaten by wild hyenas . . . You can see the heaped-up bodies of murdered children, most of them aged eleven to Thirteen, lying in the gutter, as you drive out of Addis Ababa.


Come to Harrisonburg, Virginia (in the Shenandoah Valley) to celebrate the 4th of July with us. Highlights of the full day festival include a beer and wine garden, live music, a marketplace of vendors, and the annual parade and fireworks finale.
For many people, Beers N' Cheers in the Park is a crowd favorite. Bring a blanket and listen to live music while enjoying craft brews and local wines. There's food for every appetite, too!
For more information about Valley Fourth, including Beers N' Cheers, please call 540.432.8922. Or, click the link below to visit our website.


text:
\section{Introduction}
\label{intro}

Document digitization has been an important topic for the decades, and a huge number of methods have been proposed to address many kinds of sub-tasks such as optical character recognition (OCR), text-line segmentation, layout analysis, and so on \cite{b1,ww1,ww2}.
Therefore, there have been many advances in machine-printed document understanding and handwritten text recognition.  However, the understanding of mixed cases ({\em i.e.}, documents having handwritten and machine-printed texts on the same page) still remains a challenging problem, especially when they are overlapping.
As shown in Fig.~\ref{fig:example}, these situations frequently occur in the formed documents, where we need to understand documents in the presence of handwritten notes and/or separate the handwritten and machine-printed texts.

\begin{figure}[t]
	\begin{center}
		\begin{tabular}[t]{cc}
			\includegraphics[width=3.8cm]{syn_ori.png}
			&\includegraphics[width=3.8cm]{real_ori.png} \\
			(a) & (b) \\
			\includegraphics[width=3.8cm]{syn_sg.png}
			&\includegraphics[width=3.8cm]{real_sg.png} \\
			(c) & (d)
		\end{tabular}
	\end{center}
	\caption{The proposed method performs handwritten text segmentation from scanned document images: (a), (b) input images (synthetic and real), (c), (d) segmentation results (handwritten pixels are in red).
	}
	\label{fig:example}
\end{figure}

\begin{figure*}[t]
	\centerline{\includegraphics[width=0.98\linewidth]{synthesis_diagram.pdf}}
	\caption{Overall diagram of proposed data synthesis method.}
	\label{fig:synthesis_diagram}
\end{figure*}

Many researchers addressed this  problem  by separating  handwritten (or machine-printed) texts from the input  \cite{b2,b3,b4,b5,b6,b7,b8}. In \cite{b6}, they extracted connected components (CCs) and assigned feature vectors to them by exploiting the distribution of vectorized heights, widths, and distances between components. Finally, they classified each component by applying a $k$-nearest neighbor (NN) classifier.
Similarly, Kandan {\em et al.} \cite{b4} classified each component by using support vector machines (SVMs) and $k$-NN classifiers. Also, they improved descriptors so that the algorithm is robust to deformations.
Recently, CNNs outperform traditional methods based on hand-crafted features in a variety of applications \cite{c1,c2,c3,c4}, and Li {\em et al.} used CNNs to classify  CCs \cite{b2}. Also, they incorporated conditional random fields into their framework to consider relations with neighboring CCs.

However, most of these conventional methods employed binarization and CC extraction as essential preprocessing steps and thus used the binary classification of CCs. These two-step approaches have advantages in that they allow us to exploit a variety of conventional modules (e.g., binarization, CC extraction, etc.), which also means that they have drawbacks that the final performance heavily depends on the performance of each module. Also, the CC extraction methods are prone to errors when two different kinds of texts are overlapping.

To alleviate these problems, we propose a new handwritten text segmentation method based on an end-to-end CNN. To be precise, we formulate the task as a pixel-wise classification problem, assigning `$+1$' for pixels of handwritten text and `$-1$' for others (background, machine-printed text,  table boundaries, and so on).
For the segmentation network, we adopt the U-Net \cite{c3} that naturally exploits contextual information.
In training the segmentation network, we address two challenges.
First, the number of handwritten text pixels is much smaller than the number of other pixels (mainly due to background pixels) \cite{e2}, so we develop a new loss function based on the conventional cross-entropy \cite{c1, c3}.
Second, since there is no publicly available dataset (the manual pixel-level annotation of documents is time-consuming and costly), we also propose a new synthesis method that can generate realistic images along with ground truth labels.

The  contributions of this paper can be summarized as follows:
\begin{itemize}
	\item  To the best of our knowledge, this is the first method that applies end-to-end learning to the separation of mixed-handwritten-machine-printed texts.
	\item For training the network under imbalanced situations, we propose a new loss function based on cross-entropy.
	\item For training, we also develop a data synthesis method, yielding realistic scanned documents as shown in Fig.~\ref{fig:synthesis_result}(b).
\end{itemize}

\section{Dataset synthesis}
\label{dataset}

Although deep learning methods outperform conventional methods in many fields, training the deep networks requires a huge number of training samples.
Especially, for the learning of segmentation networks (our application), pixel-level annotations are needed.
However, there is no publicly available dataset for this goal, and we address this problem by synthesizing training samples

\subsection{Scan image dataset}

Synthesizing realistic images from scratch is a difficult task, and we develop a method that uses existing scanned images.
We use 13,353 sentence images of IAM dataset \cite{d1} as the handwritten texts, which was written by a variety of writers.
For machine-printed parts, PRImA dataset \cite{d2} is used, which consists of scanned images of magazines and technical/scientific publications.
Additionally, we manually crawled 141 images of questionnaire forms from the Internet. We augmented dataset by including these images in training dataset so that our dataset covers a wide range of formed documents. Typical examples are shown in $red$ boxes in  Fig.~\ref{fig:synthesis_diagram}.

\subsection{Dataset Synthesis}\label{syn}
For the realistic data synthesis, our main considerations are preserving textures of handwritten text images and noise of original documents.
First, it is noted that textures of handwritten text images can be crucial evidence to differentiate them from the machine printed texts.
Secondly, consistent noise inherited from the scanning process must be preserved to diminish discrepancies between the distributions of synthetic and real data. To be precise, if we simply add a handwritten text image and a machine-printed text image, then the background will be saturated, and most of the scan-noises will disappear. We address this issue by inverting their intensities because backgrounds do not suffer from saturation if two inverted images are added. Another issue is undesirable block artifacts shown in Fig.~\ref{fig:synthesis_result}(a). Actually, these artifacts are from IAM dataset, since sentence images in IAM dataset were made by concatenating separate word images.
To remove these artifacts, we extract only handwritten text pixels by multiplying images with its binary mask generated by {\em Otsu} binarization method \cite{otsu}. As shown in
Fig.~\ref{fig:synthesis_result}(b), the proposed method yields realistic images, which have spatially consistent scan-noise.

In order to reflect the diversity of appearance observed in real environments, we apply randomized transformations, such as resizing, translation and rotation to each handwritten sentence image.
We also augment the dataset by adding random off-set values to the handwritten text patch to simulate a variety of intensities of handwritten texts. We have synthesized $146,391$ patches for training and $8,128$ for validation. The overall synthesis method is shown in Fig.~\ref{fig:synthesis_diagram}.
We will make our synthesized dataset publicly available.

\begin{figure}[t]
	\begin{center}
		\begin{tabular}[t]{cc}
			\includegraphics[width=4cm]{fig2_1.png}
			&\includegraphics[width=4cm]{fig2_2.png} \\
			(a) & (b)
		\end{tabular}
	\end{center}
	\caption{(a) synthesis result w/o artifacts handling, (b) synthesis result using the proposed method.}
	\label{fig:synthesis_result}
\end{figure}

\section{Proposed method}
With synthesized training samples, we train a network in an end-to-end manner. This section presents our network structure and loss functions that are appropriately designed for our purpose and environment.

\subsection{Network Structure} \label{section3}

As a neural network architecture, we adopt U-Net \cite{c3} that consists of  encoder and decoder parts as shown in Fig.~\ref{network}.
The encoder captures the context of images by using a large-scale receptive field (downsampling operators), and the decoder generates high-resolution segmentation results by using contextual information and features from the encoder.
As downsampling operators, we empirically select max-pooling instead of strided-convolutions, and  $4\times4$ transposed-convolution layers with stride $2$ are used to up-sample the concatenation of encoder and decoder signals.

\subsection{Cross entropy based loss function}\label{section4}
The cross-entropy loss function is commonly used for training the recognition \cite{c2,c4} and segmentation networks \cite{c1, c3}, which is described as
\begin{equation}
\label{eq:CE2}
\mathcal{L}_{\mbox{\tiny CE}}(\theta) = \frac{1}{N}\sum_{n=1}^N \sum_{c=1}^C\mathbf{CE}(n,c),
\end{equation}
where
\begin{equation}
\label{eq:CE1}
\mathbf{CE}(n,c) = -~t_{n,c}\log y_{n,c},
\end{equation}
where $n \in \left\lbrace 1,2,...,N \right\rbrace $ is the pixel index and $c \in \left\lbrace 1,2,...,C \right\rbrace $ is the class index. In our case, $C$ is set to $2$, since our model makes a decision just whether this pixel is handwritten text pixel or not.
Also, $t_{n,c}\in \left\lbrace 0,1 \right\rbrace $ and $y_{n,c}\in \left[0,1 \right] $ represent one-hot encoding of the ground truth label and the softmax result of the network, respectively. The $\theta$ denotes  parameters of the network.

However, eq.(\ref{eq:CE2}) is likely to yield a poor local minimum for our task.
That is, in most document images, the number of background pixels is approximately 20 times larger than that of text pixels. Therefore, the model is likely to converge to a trivial solution that classifies all pixels as background (the {\em class imbalance} problem).
Moreover,  background pixels consist of many easy cases and a tiny number of hard cases. In other words, most background pixels can be easily classified even by a simple thresholding method, and the CNN is very likely to converge to a sub-optimal solution by focusing on easy but dominant cases. That is, the loss summed over a large number of easy background examples would {\em overwhelm} the loss of rare hard examples, {\em i.e., ``many a little makes a mickle''} (the {\em overwhelming} problem).

\begin{figure}[t]
	\centerline{\includegraphics[width=0.98\linewidth]{model.pdf}}
	\caption{Our network architecture based on U-Net.}
	\label{network}
\end{figure}

\subsection{Dynamically Balanced Cross Entropy}
In order to alleviate the aforementioned {\em class imbalance} problem, we propose a new loss function:
\begin{equation}
\label{eq:DBCE}
\mathcal{L}_{\mbox{\tiny DBCE}}(\theta) = \frac{1}{N}\sum_{n=1}^{N}\sum_{c=1}^{C}\frac{1}{\beta(c) + \epsilon}~ \mathbf{CE}(n,c),
\end{equation}
where
\begin{equation}
\label{eq:B}
\beta(c) = \frac{1}{N} \sum_{n=1}^{N}\left[t_{n,c}==1\right].
\end{equation}
Unlike eq.(\ref{eq:CE2}), $\mathbf{CE}(n,c)$ is (dynamically) divided by the {\em frequency} of pixels in each class (in {\em mini-batch}) in $\mathcal{L}_{\mbox{\tiny DBCE}}(\theta)$. That is, the amount of contribution of each pixel is weighted by the scarcity of its class (fewer cases will have larger weights). By employing our loss function, the model can be trained even in imbalanced situations.
\begin{figure*}
	\begin{subfigure}{.245\textwidth}
		\centering
		\includegraphics[width=.98\linewidth]{ablation_0.pdf}
		\caption{original image}
		\label{fig:sfig1}
	\end{subfigure}%
	\begin{subfigure}{.245\textwidth}
		\centering
		\includegraphics[width=.98\linewidth]{ablation_1.pdf}
		\caption{CE}
		\label{}
	\end{subfigure}
	\begin{subfigure}{.245\textwidth}
		\centering
		\includegraphics[width=.98\linewidth]{ablation_2.pdf}
		\caption{DBCE}
		\label{fig:sfig2}
	\end{subfigure}
	\begin{subfigure}{.245\textwidth}
		\centering
		\includegraphics[width=.98\linewidth]{ablation_3.pdf}
		\caption{DBCE + F}
		\label{fig:sfig2}
	\end{subfigure}
	\caption{Segmentation results on a real scribbled document by the proposed network trained by each loss function. CE: conventional cross entropy, DBCE: dynamically balanced cross entropy, F: focal loss.}
	\label{fig:ablation_figure}
\end{figure*}

\subsection{Focal Loss}
In order to alleviate the {\em overwhelming} problem, we adopt focal loss in \cite{c4}. For the presentation of focal loss, we first define  $\mathbf{FCE}(n,c)$ as
\begin{equation}
\label{eq:FE}
\mathbf{FCE}(n,c) = -~({1-{ y}_{n,c}})^{\gamma}{ t}_{n,c} \log { y}_{n,c},
\end{equation}
where $\gamma$ is the hyperparameter that determines the boost degree of the penalty.
As shown in eq.(\ref{eq:FE}), the term $\mathbf{FCE}(n,c)$ is the $({1-{ y}_{n,c}})^{\gamma}$ scaled version of $\mathbf{CE}(n,c)$ in eq.(\ref{eq:CE1}).
This scaling factor automatically lessens the contribution of easy examples and makes the model focus on hard examples during the training. By putting two ideas together, the final loss function is given by
\begin{equation}
\label{eq:BFE}
\mathcal{L}_{\mbox{\tiny DBCEF}}(\theta)= \frac{1}{N}\sum_{n=1}^{N}\sum_{c=1}^{C}\frac{1}{\beta(c) + \epsilon} \mathbf{FCE}(n,c).
\end{equation}

\begin{figure}[t]
	\centerline{\includegraphics[width=0.98\linewidth]{ablation.pdf}}
	\caption{IoU error rate of handwritten text in validation set. \textit{Max}: max-pooling,  \textit{SConv}: strided-convolution, \textit{CE}: conventional cross entropy, \textit{DBCE}: dynamically balanced cross entropy, \textit{F}: focal loss.}
	\label{fig:ablation}

\end{figure}

\subsection{Training details}
We have trained the network using Adam optimizer \cite{e1} with a mini-batch size of $32$. We used $0.0002$ as the initial learning rate with $0.8$ decay rate in every $30$ epochs. For hyper-parameter of {loss function}, we empirically set $\epsilon=0.0001$ and $\gamma= 1$.

\begin{figure*}
	\begin{subfigure}{.24\textwidth}
		\centering
		\includegraphics[width=.98\linewidth]{OCR_test_input_0.png}
		\caption{input 1}
		\label{fig:sfig1}
	\end{subfigure}%
	\begin{subfigure}{.24\textwidth}
		\centering
		\includegraphics[width=.98\linewidth]{OCR_test_input_1.png}
		\caption{input 2}
		\label{}
	\end{subfigure}
	\begin{subfigure}{.24\textwidth}
		\centering
		\includegraphics[width=.98\linewidth]{OCR_test_result_0.png}
		\caption{OCR result 1}
		\label{fig:sfig2}
	\end{subfigure}
	\begin{subfigure}{.24\textwidth}
		\centering
		\includegraphics[width=.98\linewidth]{OCR_test_result_1.png}
		\caption{OCR result 2}
		\label{fig:sfig2}
	\end{subfigure}
	\caption{Comparisons of OCR performance. (a) Input 1 to OCR (real scribbled image), (b) Input 2 to OCR (removal of handwritten pixels from (a)), (c) and (d) OCR results for input 1 and 2 ({\color{blue}blue: correctly recognized characters}, {\color{red} red: missing or incorrect ones}).}
	\label{fig:OCR_result}
\end{figure*}

\section{Experimental Results} \label{section5}
In this section, we perform an ablation study to see whether our loss functions are effective for training the network. Then, we will show the performance of the proposed method on synthetic and real data. We did not perform the comparison with existing works \cite{b2,b3,b4,b5,b6,b7,b8}, since there is none that publicly provides the code and data to compare the performance.
In a recent research of \cite{b2}, they tested CC-level and region-level segmentation with their own  {\em TestPaper 1.0} dataset, and the {\em Maurdor} dataset from \cite{m1}. However, these datasets are currently not accessible, and also they cannot be directly compared with ours because we deal with pixel-level results. Hence, we will instead make our dataset and codes publicly available for future research and comparisons.

\subsection{Ablation Study}
Fig.~\ref{fig:ablation_figure} demonstrates the effect of each loss function on the segmentation results, where the first column shows the input and the ground truth of segmentation results on green-box region. As shown in Fig.~\ref{fig:ablation_figure}(b), using the conventional cross entropy loss function ($\mathcal{L}_{\mbox{\tiny CE}}$) for training, a lot of handwritten pixels are classified as background, which means that the trained model experiences the {\em class imbalance} problem.
By applying the proposed $\mathcal{L}_{\mbox{\tiny DBCE}}$, we can see that the {\em class imbalance} problem is quite mitigated as shown in Fig.~\ref{fig:ablation_figure}(c). However, there are lots of misclassified pixels in the machine printed text region, which is the example of {\em overwhelming} problem.
To alleviate this problem, we incorporate focal loss \cite{c4} with proposed $\mathcal{L}_{\mbox{\tiny DBCE}}$. Finally, we can get the segmentation results without any degradation from {\em imbalance} situations as shown in Fig.~\ref{fig:ablation_figure}(d).

For the quantitative comparisons, we evaluate the proposed method using the pixel-level intersection-over-union (IoU) on synthesized sets. Fig.~\ref{fig:ablation} shows that the proposed loss function ($\mathcal{L}_{\mbox{\tiny DBCEF}}$)  improves validation performance.
Also, as shown in Table~{\ref{table}},  $\mathcal{L}_{\mbox{\tiny DBCE}}$ and focal loss term achieves $1.32\%$p and $0.60\%$p improvements of test performance on synthesized  set, respectively. These qualitative and quantitative experimental results showed that $\mathcal{L}_{\mbox{\tiny DBCE}}$ and focal loss term is meaningfully functioning during network training, {\em i.e.,} mitigating {\em class imbalance} and {\em overwhelming} problem well.

\begin{table}[t]
	\centering
	\caption{IoU results on synthesized test set. The best results are highlighted in \textbf{bold face} and the second best results are \underline{underlined}. \textit{H}: handwritten text, \textit{Max}: max-pooling, \textit{SConv}: strided-convolution, \textit{CE}: conventional cross entropy, \textit{DBCE}: dynamically balanced cross entropy, \textit{F}: focal loss}
	\label{table}
	\resizebox{0.9\linewidth}{!}{
		\begin{tabular}{lllclclcl}
			\toprule[1pt]
			\multicolumn{3}{c}{\textit{}} & \multicolumn{2}{c}{\multirow{3}{*}{\textit{\begin{tabular}[c]{@{}c@{}}Number of \\ Parameters \\ (M)\end{tabular}}}} & \multicolumn{4}{c}{\multirow{2}{*}{\textit{IoU (\%)}}} \\
			\multicolumn{3}{c}{\multirow{2}{*}{\textit{Model}}} & \multicolumn{2}{c}{} & \multicolumn{4}{c}{} \\ \cline{6-9}

			\multicolumn{3}{c}{} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{\textit{non-H}} & \multicolumn{2}{c}{\textit{H}} \\
			\midrule[1pt]
			\multicolumn{3}{l}{\textit{Max + CE}} & \multicolumn{2}{c}{6.61} & \multicolumn{2}{c}{99.89} & \multicolumn{2}{c}{95.88} \\
			\multicolumn{3}{l}{\textit{Max + DBCE}} & \multicolumn{2}{c}{6.61} & \multicolumn{2}{c}{\underline {99.93}} & \multicolumn{2}{c}{\underline {97.20}} \\
			\multicolumn{3}{l}{\textit{\textbf{Max + DBCE +F}}} & \multicolumn{2}{c}{6.61} & \multicolumn{2}{c}{\textbf{99.94}} & \multicolumn{2}{c}{\textbf{97.80}} \\
			\multicolumn{3}{l}{\textit{SConv + DBCE + F}} & \multicolumn{2}{c}{7.39} & \multicolumn{2}{c}{99.92} & \multicolumn{2}{c}{97.11} \\ \hline
		\end{tabular}
	}
\end{table}

\begin{table}[t]
	\centering
	\caption{OCR performance on a real scribbled-document image. Accuracy is calculated by Correct / (Correct + Incorrect + Missing). Visualized results are shown in Fig.~\ref{fig:OCR_result}.}
	\label{table2}

	\begin{tabular}{clcllcllcll}
		\toprule[1pt]
		& \multicolumn{1}{c}{} & \multicolumn{9}{c}{Document states} \\ \cline{3-11}
		& \multicolumn{1}{c}{} & \multicolumn{3}{c}{original} & \multicolumn{3}{c}{scribbled} & \multicolumn{3}{c}{separated} \\ \midrule[1pt]
		\multicolumn{2}{c}{Correct} & \multicolumn{3}{c}{2,141} & \multicolumn{3}{c}{1,584} & \multicolumn{3}{c}{2,071} \\
		\multicolumn{2}{c}{Incorrect} & \multicolumn{3}{c}{3} & \multicolumn{3}{c}{125} & \multicolumn{3}{c}{164} \\
		\multicolumn{2}{c}{Missing} & \multicolumn{3}{c}{7} & \multicolumn{3}{c}{518} & \multicolumn{3}{c}{4} \\
		\multicolumn{2}{c}{Accuracy (\%)} & \multicolumn{3}{c}{99.54} & \multicolumn{3}{c}{71.13} & \multicolumn{3}{c}{92.50} \\ \hline
		\multicolumn{1}{l}{} &  & \multicolumn{1}{l}{} &  &  & \multicolumn{1}{l}{} &  &  & \multicolumn{1}{l}{} &  &  \\
		\multicolumn{1}{l}{} &  & \multicolumn{1}{l}{} &  &  & \multicolumn{1}{l}{} &  &  & \multicolumn{1}{l}{} &  &
	\end{tabular}

\end{table}

\subsection{Generalization performance}

In the case of {\em real} test-set,  IoU evaluation is infeasible due to the lack of (pixel-level) ground truth. Rather, we measure the OCR performance on handwritten-pixel-removed-images, which is naturally proportional to the handwritten text segmentation performance.
To be precise, given a scribbled image like Fig.~\ref{fig:OCR_result}(a), we evaluate the OCR performances of original documents (w/o handwritten components) and handwritten-pixel-removed-images (Fig.~\ref{fig:OCR_result}(b)).
As shown in Table~\ref{table2} and Fig.~\ref{fig:OCR_result}, there are a lot of missing or incorrectly detected characters in the scribbled document, mainly due to scribbles overlapping the machine-printed text. After removing them, which is segmented as handwritten text by proposed network, the OCR performance is improved from $71.13\%$ to $92.50\%$.
Note that the model is trained only with the synthesized data, and these results show that the model has learned features having generalization ability.

\section{Conclusion}
In this paper, we have proposed a  method to separate handwritten text from the machine-printed documents based on a deep neural network. Unlike conventional methods, we proposed a method that works in an end-to-end manner, and addressed the {\em class imbalance} and {\em overwhelming} problems in the training phase.
The network was trained with synthetic training samples generated by the proposed synthesis method.
The experimental results show that the proposed model also works well for real document images. Although the proposed method shows a good handwritten text extraction performance, it can  reconstruct only handwritten part when it is overlapping with the machine-printed text. As a future work, we will address the layer separation problem to reconstruct both components.

redpajama_set_name: RedPajamaArXiv


I watched hundreds of motivational videos and listened to countless motivational speeches, and many of them have helped to get me fired up to take action. But for some reason, none of them have had quite the same affect on me as this one did.
I think part of what I really like about this message is that it helps me to see that challenges I face as opportunities to grow, rather than something to avoid or feel sorry for myself about. I've taken on a lot of new projects this year and I've faced a few obstacles, as I think most of us have, and it's easy to feel overwhelmed and to start saying "why me?". But isn't it more productive to embrace these challenges and face them head on? I believe it is.
The idea isn't that we should pursue "pain", or look for struggles in life, but rather that we shouldn't shy away from it either. We can overcome that pain. We can learn from our struggles. We will grow from it... and the bigger we get, the smaller our problems will seem. Be grateful for the opportunity to become a stronger person... to grow into your greatness.
"We are made strong by what we overcome."
"Whatever does not kill you will make you stronger."
"Don't pray for an easy life... pray to be a stronger person."
"Without pain there is no gain. Without pain there is no glory."
After you watch it I'd appreciate hearing from you... can you relate to it? What did you get from it? Please take a minute to post a comment below.


text:
class CSinFunction : public IFunctionAst
{
public:
    double Call(CInterpreterContext &context, const std::vector<double> &arguments) const override
    {
        (void)context;
        if (arguments.size() != 1)
        {
            std::cerr << "sin needs exactly 1 argument" << std::endl;
            return std::numeric_limits<double>::quiet_NaN();
        }
        return std::sin(arguments[0]);
    }

    unsigned GetNameId() const override
    {
        return 0;
    }
};

class CRandFunction : public IFunctionAst
{
public:
    double Call(CInterpreterContext &context, const std::vector<double> &arguments) const override
    {
        (void)context;
        if (arguments.size() != 2)
        {
            std::cerr << "rand needs exactly 2 argument" << std::endl;
            return std::numeric_limits<double>::quiet_NaN();
        }
        if (arguments[0] > arguments[1])
        {
            std::cerr << "rand: first argument must be less than or equal to second" << std::endl;
            return std::numeric_limits<double>::quiet_NaN();
        }
        double rand0to1 = double(std::rand()) / std::numeric_limits<unsigned>::max();
        return (arguments[1] - arguments[0]) * rand0to1 + arguments[0];
    }

    unsigned GetNameId() const override
    {
        return 0;
    }
};

CInterpreterContext::CInterpreterContext(CStringPool &pool)
    : m_pool(pool)
{
    AddBuiltin("sin", std::unique_ptr<IFunctionAst>(new CSinFunction));
    AddBuiltin("rand", std::unique_ptr<IFunctionAst>(new CRandFunction));
}

CInterpreterContext::~CInterpreterContext()
{
}

void CInterpreterContext::DefineVariable(unsigned nameId, double value)
{
    m_scopes.back()->AssignVariable(nameId, value);
}

void CInterpreterContext::AssignVariable(unsigned nameId, double value)
{
    if (CVariablesScope *pScope = FindScopeWithVariable(nameId))
    {
        pScope->AssignVariable(nameId, value);
    }
    else
    {
        DefineVariable(nameId, value);
    }
}

double CInterpreterContext::GetVariableValue(unsigned nameId) const
{
    if (CVariablesScope *pScope = FindScopeWithVariable(nameId))
    {
        return *pScope->GetVariableValue(nameId);
    }
    std::cerr << "error: unknown variable " << m_pool.GetString(nameId) << std::endl;
    return std::numeric_limits<double>::quiet_NaN();
}

void CInterpreterContext::PushScope(std::unique_ptr<CVariablesScope> &&scope)
{
    m_scopes.emplace_back(std::move(scope));
}

std::unique_ptr<CVariablesScope> CInterpreterContext::PopScope()
{
    std::unique_ptr<CVariablesScope> ret(m_scopes.back().release());
    m_scopes.pop_back();
    return ret;
}

size_t CInterpreterContext::GetScopesCount() const
{
    return m_scopes.size();
}

IFunctionAst *CInterpreterContext::GetFunction(unsigned nameId) const
{
    try
    {
        return m_functions.at(nameId);
    }
    catch (std::exception const&)
    {
        std::cerr << "error: unknown function " << m_pool.GetString(nameId) << std::endl;
        return nullptr;
    }
}

void CInterpreterContext::AddFunction(unsigned nameId, IFunctionAst *function)
{
    if (function)
    {
        m_functions[nameId] = function;
    }
}

void CInterpreterContext::PrintResult(double value)
{
    std::cout << "result: " << value << std::endl;
}

void CInterpreterContext::SetReturnValue(boost::optional<double> const& valueOpt)
{
    m_returnValueOpt = valueOpt;
}

boost::optional<double> CInterpreterContext::GetReturnValue() const
{
    return m_returnValueOpt;
}

CVariablesScope *CInterpreterContext::FindScopeWithVariable(unsigned nameId) const
{
    auto range = boost::adaptors::reverse(m_scopes);
    auto it = boost::find_if(range, [=](const auto &pScope) {
        return pScope->HasVariable(nameId);
    });
    if (it != range.end())
    {
        return it->get();
    }
    return nullptr;
}

void CInterpreterContext::AddBuiltin(const std::string &name, std::unique_ptr<IFunctionAst> &&function)
{
    m_builtins.emplace_back(std::move(function));
    unsigned nameRand = m_pool.Insert(name);
    m_functions[nameRand] = m_builtins.back().get();
}

redpajama_set_name: RedPajamaGithub


text:
Newscast headlines
23 States Sue Trump To Keep California's Auto Emission Rules
SACRAMENTO, Calif. (AP) — Nevada has joined twenty-two other states in suing to stop the Trump administration from revoking California's authority to set emission standards for cars and trucks.
California Attorney General Xavier Becerra is leading the lawsuit filed Friday, along with Gov. Gavin Newsom and the California Air Resources Board.
The Trump administration on Thursday revoked California's authority to set its own auto emission standards. The state has had that power for decades under a waiver from the federal Clean Air Act.
The lawsuit argues that the National Highway Traffic Safety Administration does not have the authority to revoke California's waiver.
Becerra says the Trump administration's action fails to respect states' authority to protect public health.
Four automakers have said they would voluntarily follow California's standards.
Nevada & the Southwest
auto emissions standards
emissions standards


text:
Why People With a Savior Complex Sacrifice Their Own Needs to Help Others
Image Credit: Alamy
If 2020 was anything, it was a year when people needed help. A sudden vulnerability and helplessness blanketed the world, laying bare systemic inadequacies and prompting individuals to band together, supporting each other where institutions had failed. But while lending others a helping hand is typically a good thing, for those with savior complex, it becomes an unhealthy means of coping or validation.
Also known as white knight syndrome, savior complex occurs when individuals feel good about themselves only when helping someone, believe their job or purpose is to help those around them, and sacrifice their own interests and well-being in the effort to aid another.
Although this knight in shining armor, straight-out-of-a-fairy-tale behavior might sound too good to be true, it's an unhealthy coping mechanism that can do more harm than good.
People with savior complex try to feel in control by fixing the lives of others — often in order to distract themselves from their own anxiety or powerlessness, Cynthia Catchings, a therapist, told Thrive Global. Helping others also induces a sense of validation in such people, helping them feel better about their own lives — resulting in an obsessive need to fix in order to maintain this good feeling.
Predispositions to the savior complex can sometimes be traced back to dysfunctional family dynamics in childhood — resulting in the unhealthy coping mechanism that continues into adulthood.
Related on The Swaddle:
What Is the 'Victim Complex' and Why Do People Experience It?
People with savior complex often believe they are somehow better than others because they help people all the time, leading to feelings of being morally superior. In addition, experts note that savior complex can induce feelings of omnipotence, making people who experience it prone to believing no one else can save others they way they can.
Because people with savior complex tend to "seek people who desperately need help and to assist them, often sacrificing their own needs," they are often "identified as 'nice guys,' but the truth is emotionally healthy [people] will never have a compelling need to seek that kind of validation. That itself should be an alerting sign," notes Sara Benson, PsyD, a psychologist.
While helping people out generally isn't harmful, an individual with savior complex can actually harm more than they help, by trying to fix something they don't have the skills to fix, rather than entrusting the job to someone who does. "If your partner has a drug or alcohol problem and you refuse to leave them because they 'need' you — this is also enabling behavior. They have a serious health problem that your presence alone cannot fix," Julie Williamson, a counselor, notes as an example.
Savior complex behavior can also hinder the growth of the individual being aided and constant attempts to fix their lives and can lead to codependency, they neither learn to take responsibility for their own actions nor develop independent, internal motivation.
Why People‑Pleasing Is a Common Human Instinct
Because of these effects, savior complex can, in fact, add an unhealthy and often toxic dynamic to romantic relationships, as the individual with savior complex treats the relationship more like a parent-child or teacher-student relationship in a constant endeavor to fix their adult partners. Being "made to feel as if [they aren't liked] as they are" and need fixing can make the partner frustrated and resentful, Maury Joseph, PsyD, a psychologist, explains.
"Relationships are supposed to be mutually enjoyable and give-take, not charity cases. … You should enter into relationships because you share common values and have a connection. If you are entering a committed relationship with the goal of changing your partner then [they're a] project, not a partner," David Bennett, a counselor, explains.
The savior complex harms the fixer as well as their people-projects. Constant helping and sacrificing for others can cause them to feel they are taken for granted when those around them get used to their helpfulness. It can also cause them to experience burnout due to the amount of energy they expend in trying to help others. "Saviors might see symptoms similar to those in people taking care of ailing family members. … They might feel fatigued, drained, depleted in various ways," Joseph adds.
Experts note that to overcome the complex, recognizing the fact that one is experiencing it could act as the stepping stone to stopping the behavior. Additionally, talking to a therapist, especially to uncover experiences from one's past that may have led to the complex, could be helpful too.
coping | Knight In Shining Armor | mental health
Written By Devrupa Rakshit
Devrupa Rakshit is an associate editor with The Swaddle. She is a lawyer by education, a poet by accident, and a painter by shaukh. She has her own podcast called #DateNightsWithD on Spotify. You can find her on Instagram @devruparakshit.
See all articles by Devrupa
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1. I was born and raised in Europe, in the beautiful country of Albania! Yes, Albania is a country, small… but exists!
2. My daughter's name is Alina. I named her after my favorite doll I had when I was little. Since then, I wished that if I had a daughter I would name her Alina.
3. I speak Albanian, Italian, English and I have studied Romanian language for a full year. Knowing Italian helps to learn other Latin languages easily. I want to learn Portuguese next.
4. Albania is a post communist country and I was raised during communism time. I have lot's of stories to share about that. I love my childhood!
5. My husband is American so we have lot's of fun exploring the two different languages and cultural differences.
6. I love traveling. I traveled pregnant different times, mainly on a bus for 18 hours on 3 trips back and forth to Colorado. I hope my daughter enjoys traveling too.
8. I have worked most of my life for and with children. I have even dressed as a clown many times to entertain them.
9. I have published many stories and poems mainly for kids and I have so many unpublished. I am trying to write in English but the words don't come as easy as in Albanian.
10. Thanks to my dad I have such a love for animals. I have helped others adopt stray animals I would pick up from the side of the road or dumpsters of course after cleaning and taking care of them. So lucky my husband and daughter are the same.
You are a great person Ledia!!
Flm moj Ketush! Miss you! sa here takoj ndonje nga elbasani ketej u them mos te njohin ty se sigurisht duhet te te njohin te gjithe! Te perqafoj me mall!
I really liked it! I like writing that make me think deeper. 🙂 Very creating!


text:
Q: Cannot get js file to execute from html with Flask in PyCharm I'm trying to get a html page to run a js script from the static files of the host using Flask (as it's a test project it's a localhost) and for some reason every script comes back as a 404 - I'm using this file structure
¬static
  ¬html
    ¬user.html
  ¬js
    ¬script.js

https://i.stack.imgur.com/vIXzr.png
I've followed as many guides and other queries on fixing this but I don't get what else I'm missing. I've used <script src="js/script.js"></script> to call it, I've used src="{{url_for('static', filename='js/script.js') }}" in place of src="js/script.js, I've tried using just script.js instead of js/script.js, I've tried putting it in different places in the file tree and using as many ways as I can think of to call it from these places, I've downloaded plugins, I looked at official sites explaining how to create html and js and etcetera...  Can't get anything to work
EDIT - solved, for some reason clearing my cache for the 2000th time fixed it. Copied the project to another device, same problem, fixed it by replacing js/script.js with static/js/script.js. That doesn't seem like that's how it should be but it works I guess

A: Static content in Flask loaded as follows:
   <script src="{{ url_for('static', filename='js/script.js') }}"></script>

But in this case your html-template should be located in separate folder:
static
--js
    scripts.js
templates
  user.html

Folder templates is the default place where all html-templates are located in Flask.

redpajama_set_name: RedPajamaStackExchange


text:
Virginia Military Institute Archives
Papers, 8 items, of James W. Breedlove. Includes two letters (October 13, 1859; March 11, 1861) from Cadet Breedlove to his grandfather in which he discusses his studies, expenses; two letters from Superintendent Francis H. Smith to Cadet Breedlove's grandfather, acknowledging receipt of money; three VMI quarterly grade reports; one account sheet detailing VMI expenses.
Breedlove, James W. (James Winchester), 1843-1889 (Person)
There are no restrictions
Conditions Governing Access Manuscript collections in the VMI Archives are made available for educational and research use. The VMI Archives should be cited as the source. The user assumes all responsibility for identifying and satisfying any copyright holders. Materials from our collections may not be redistributed, published or reproduced without permission from the VMI Archives. Contact the VMI Archives for additional information.
James Winchester Breedlove, b. 1843, New Orleans, LA; VMI Class of 1863; served with CSA Engineers during Civil War; Civil Engineer; d. 1889 Houston Texas.
Manuscripts Stacks
Virginia Military Institute -- Class of 1863
Virginia Military Institute -- History -- 19th century
Smith, Francis H. (Francis Henney), 1812-1890 (Person)
Part of the Virginia Military Institute Archives Repository
http://www.vmi.edu/archives
Lexington VA 24450
James W. Breedlove papers. Manuscript # 0168, Virginia Military Institute Archives https://archivesspace.vmi.edu/repositories/3/resources/248 Accessed January 21, 2020.


text:
Simon's Hat Trick Leads Jeffs Over St. Michael's, 6-2
AMHERST, MA - First-year Rachel Simon's (West Orange, NJ) hat trick in the first period led a six-goal outburst for the Amherst College women's ice hockey team, which steamrolled visiting non-league foe St. Michael's College on Wednesday night, 6-2. Amherst senior Ali White (Brookline, MA), junior Alicia Pichard (Chevy Chase, MD) and first-year Emily Cronin (Arlington, MA) each joined Simon in the scoring column, while the Jeff defense was stifling, allowing only 14 shots on goal.
Pichard wasted no time lighting the lamp as White found her from behind the net just over a minute after the puck dropped. Simon accounted for the next two goals, punching the first in off an Amanda Muller (Princeton, NJ) assist and finding the net unassisted for her second. St. Michael's answered at the 7:17-mark as first-year Melissa Gagne (Eliot, ME) scored off an assist from fellow rookie Kasie Scafariello (Wallingford, CT). White struck the next blow for Amherst, tickling the twine on a feed from first-year Alena Harrison (Westwood, MA), and Cronin's score came a minute later as sophomore Leah Kaplan (Buffalo, NY) and first-year Meghan Dickoff (Minnetonka, MN) found her for the power-play goal. Simon capped the Jeffs' scoring with an unassisted goal to complete her first collegiate hat trick. St. Michael's first-year Martha Salois (Winthrop, ME) scored the game's final goal off an assist from junior Kate Talbot (Marblehead, MA).
Amherst first-year Lindsay Grabowski (Princeton, NJ) had 12 saves between the pipes as the Jeffs improved to 3-2 with the win. Junior Marcy Ring (North Attleboro, MA) and sophomore Annice Mason (Berlin, VT) combined for 48 saves for the Purple Knights, who fell to 0-6-1 with the loss
Amherst returns to action Friday at 7 p.m. against visiting NESCAC rival Middlebury College.


text:
Tomorrow's Warriors marks 30 years with A Great Day in London event
by Miranda Bardsley
November 15th 2021	at 5:47PM
Award-winning talent development organization and charity Tomorrow's Warriors has announced A Great Day In London – Celebrating 30 Years – A Jazz Happening, on December 4 at Queen Elizabeth Hall at The Southbank Centre.
The 30th anniversary event will be fundraising to support future generations of jazz musicians, and will feature artists including Nubya Garcia, Moses Boyd, Eska, Theon Cross, Binker Golding, Denys Baptiste, Zara McFarlane, Camilla George and Cherise. The artists will also be joined by Shabaka Hutchings, Gary Crosby OBE, and many more.
Tomorrow's Warriors has provided a platform for many well-known artists on the international jazz scene, including Cassie Kinoshi and Nérija, Femi Koleoso and Ezra Collective, Binker Golding, Shabaka Hutchings and Sons Of Kemet, Denys Baptiste, Soweto Kinch, Zara McFarlane, among others.
With a resident space at The Southbank Centre's Royal Festival Hall to host its Young Artist Development Programme, Tomorrow's Warriors is also a National Portfolio Organisation of Arts Council England and a PRS Foundation Talent Development Partner.
Alongside the long line-up of artists, A Great Day In London – inspired by Art Kane's 1958 photo essay, A Great Day in Harlem – will be featuring an exclusive VIP afterparty and guest DJs, with further details to be announced.
"This fitting headline to Tomorrow's Warriors 30th anniversary year celebrates a glorious moment in our collective musical history, and proudly shines a light on the epic journeys of Warriors musicians," said Gary Crosby, Tomorrow's Warriors co-founder and artistic director. "The original image of A Great Day In Harlem by Art Kane documented a moment in time of a burgeoning scene, and the parallel we see today in UK jazz is undeniable. It will be magical to bring our Warriors family together in homage to this iconic image and document our 30th anniversary milestone at the same time! It will be an unmissable and joyous occasion and your support will help us raise funds to protect and develop the next generation of outstanding jazz talent."
Denys Baptiste said: "It will be a wonderful moment to come together with Tomorrow's Warriors, to celebrate its 30 year history and legacy with our Warrior family for A Great Day In London. It's perfect timing for our jazz movement, which is at its peak with so much incredible talent creating major waves around the world. It's definitely going to be a Great Day to remember, and I'm really looking forward to it!"
Tomorrow's Warriors,
Shabaka Hutchings,
Gary Crosby,
A Great Day In London
Meet the 2021 Women In Music Roll Of Honour: Indy Vidyalankara
Sons Of Kemet's Shabaka Hutchings on his new label
Tomorrow's Warriors brand proposed cuts to arts education a travesty
Tomorrow's World: Celebrating three decades of Tomorrow's Warriors


text:
Ferrari Nachrichten
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Ferrari, Double Digit Growth, on the Way to Another Record Year
Total shipments of 2,750 units, up 18.9% versus Q3 2020 and 11.2% versus Q3 2019
Net revenues of Euro 1,053 million, up 18.6% versus prior year and up 15.1% versus Q3 2019
EBITDA(1) of Euro 371 million, up 12.4% versus prior year and up 19.6% versus Q3 2019. EBITDA(1) margin of 35.2% in Q3 2021.
EBIT of Euro 270 million, up 21.8% versus prior year and up 19.2% versus Q3 2019. EBIT margin of 25.7% in Q3 2021.
Net profit of Euro 207 million and diluted EPS(1) at Euro 1.11
Extraordinarily strong industrial free cash flow(1) generation of Euro 242 million sustained by the collection of advances on the 812 Competizione
Upgraded 2021 Guidance
Benedetto Vigna, CEO of Ferrari, commented: "The strong third quarter results are an important step forward towards the upward revised 2021 guidance. The exceptional client relationships, fundamental in achieving the double digit growth in this quarter and year to date, are reflected in the record order intake worldwide, particularly in China and USA. These results together with the soundness of our vision and the team I am honoured to lead, make me look to the future with great confidence and optimism."
For the three months ended (In Euro million, For the nine months ended
September 30, unless otherwise stated) September 30,
2021 2020 Change 2021 2020 Change
2,750 2,313 437 19% Shipments (in units) 8,206 6,440 1,766 27%
1,053 888 165 19% Net revenues 3,099 2,391 708 30%
371 330 41 12% EBITDA(1) / Adj. EBITDA(1) 1,133 771 362 47%
35.2% 37.2% (200 bps) EBITDA(1) / Adj. EBITDA(1) margin 36.6% 32.3% 430 bps
270 222 48 22% EBIT / Adj. EBIT(1) 810 465 345 74%
25.7% 25.0% 70 bps EBIT / Adj. EBIT(1) margin 26.2% 19.5% 670 bps
207 171 36 21% Net profit / Adj. net profit(1) 619 346 273 79%
1.12 0.92 0.20 22% Basic EPS / Adj. basic EPS(1) (in Euro) 3.34 1.87 1.47 79%
1.11 0.92 0.19 21% Diluted EPS / Adj. diluted EPS(1) (in Euro) 3.33 1.86 1.47 79%
Maranello (Italy), November 2, 2021 – Ferrari N.V. (NYSE/MTA: RACE) ("Ferrari" or the "Company") today announces its consolidated preliminary results(2) for the third quarter and nine months ended September 30, 2021.
Shipments(3)(4)
For the three months ended Shipments For the nine months ended
September 30, (units) September 30,
1,308 1,288 20 2% EMEA 4,104 3,510 594 17%
706 504 202 40% Americas 2,110 1,635 475 29%
249 119 130 109% Mainland China, Hong Kong and Taiwan 609 181 428 236%
487 402 85 21% Rest of APAC 1,383 1,114 269 24%
2,750 2,313 437 19% Total Shipments 8,206 6,440 1,766 27%
Shipments totaled 2,750 units in the third quarter of 2021, up 437 units or 18.9% versus the prior year quarter.
Sales of 8 cylinder models (V8) were up 39.4%, while 12 cylinder models (V12) were down 35.1% mainly due to reduced volume of the 812 Superfast. The shipments of the quarter were driven by the range models, with the first deliveries of the SF90 Spider that began in the quarter, while the Portofino M entered the ramp up phase. The Ferrari Monza SP1 and SP2 continued to be delivered in line with planning.
All geographic regions positively contributed in the quarter. EMEA(4) increased by 1.6%, Americas(4) by 40.1%, Mainland China, Hong Kong and Taiwan by 109.2%, boosted by the arrival of new models in particular the Ferrari Roma and the SF90 Stradale, and Rest of APAC(4) by 21.1%.
Total net revenues
For the three months ended (Euro million) For the nine months ended
2021 2020 at constant 2021 2020 at constant
883 727 22% 24% Cars and spare parts(5) 2,619 1,965 33% 36%
55 44 25% 25% Engines(6) 145 97 49% 49%
95 93 1% 6% Sponsorship, commercial and brand(7) 277 265 4% 7%
20 24 (16%) (17%) Other(8) 58 64 (8%) (4%)
1,053 888 19% 21% Total net revenues 3,099 2,391 30% 33%
Net revenues for the third quarter 2021 were Euro 1,053 million, up 20.7% at constant currency(1).
Revenues from Cars and spare parts(5) were Euro 883 million (up 21.6% or 23.5% at constant currency(1)), thanks to higher volume and positive product mix, together with strong contribution from personalizations.
The increase in Engines(6) revenues (Euro 55 million, up 24.8%, also at constant currency(1)) was attributable to higher shipments to Maserati and, to a lesser extent, the rental of engines to other Formula 1 racing teams.
Sponsorship, commercial and brand(7) revenues reached Euro 95 million (up 1.3% or 5.8% at constant currency(1)) mainly due to brand-related activities.
Currency – including translation and transaction impacts as well as foreign currency hedges – had a negative impact of Euro 15 million, mostly related to FX hedges.
EBITDA(1) and EBIT
371 330 12% 18% EBITDA(1) 1,133 771 47% 55%
270 222 22% 30% EBIT 810 465 74% 88%
Q3 2021 EBITDA(1) stood at Euro 371 million, up 12.4% versus prior year and with an EBITDA(1) margin of 35.2%.
Q3 2021 EBIT was Euro 270 million, increased 21.8% versus prior year and with an EBIT margin of 25.7%.
Volume was positive (Euro 39 million), reflecting shipments' increase versus prior year.
The positive Mix / price variance performance (Euro 41 million) was driven by the contribution coming from the richer product mix thanks to the SF90 family and the Ferrari Monza SP1 and SP2, along with personalizations, partially offset by the Ferrari Roma ramp up and reduced contribution of the 812 Superfast.
Industrial costs / research and development expenses increased (Euro 12 million) mainly due to product innovation and start-up costs as well as Formula 1 expenses.
SG&A increased Euro 4 million mainly reflecting communication and marketing activities.
Other decreased Euro 2 million mainly due to the cost increase related to improved assumptions on in-season Formula 1 ranking and other supporting activities.
Net financial charges in the quarter stood at Euro 10 million, down versus Euro 14 million of the prior year, reflecting lower net foreign exchange losses, including hedging costs.
The tax rate in the quarter was 20.4%, reflecting the current estimate of the benefit attributable to the Patent Box, the Allowance for Corporate Equity (ACE)(9), deductions for eligible research and development costs, hyper and super-depreciation of machinery and equipment.
As a result, the Net profit for the period was Euro 207 million, up 20.8% versus prior year, and the Diluted earnings per share for the quarter reached Euro 1.11, compared to Euro 0.92 in Q3 2020.
Industrial free cash flow(1) for the quarter was exceptionally strong at Euro 242 million, driven by EBITDA(1) and the collection of advances on the 812 Competizione, partially offset by capital expenditures(10) of Euro 189 million.
Net Industrial Debt(1) as of September 30, 2021 was Euro 368 million, compared to Euro 552 million as of June 30, 2021. During the third quarter a total of Euro 55 million of shares were repurchased. Lease liabilities per IFRS 16 as of September 30, 2021 were Euro 59 million.
As of September 30, 2021, total available liquidity was Euro 2,040 million (Euro 1,689 million as of June 30, 2021), including undrawn committed credit lines of Euro 767 million.
Upgraded 2021 guidance, subject to trading conditions unaffected by Covid-19 pandemic restrictions and the following assumptions:
Core business sustained by volume and mix
Revenues from Formula 1 racing activities based on targeted calendar and reflecting lower 2020 ranking versus prior year
Brand-related activities dealing with Covid-19 challenges
Operational and marketing costs gradually resuming
Improved net working capital sustaining industrial free cash flow thanks to the advances on the new special series and lower payments in connection with the cadence planned for our spending
(€B, unless otherwise stated) 2019A 2020A 2021GUIDANCE
NET REVENUES 3.8 3.5 £4.3
ADJ. EBITDA (margin %) 1.333.7% 1.133.0% ~1.52 ~35.6%
ADJ. EBIT (margin %) 0.924.4% 0.720.7% ~1.05 ~24.6%
ADJ. DILUTED EPS (€) 3.71 2.88(11) ~4.30(12)
INDUSTRIAL FCF 0.7 0.2 >0.55
Q3 2021 highlights
Ferrari N.V. places Euro 150 million in bonds with U.S. private investors, due 2032On July 29, 2021, Ferrari N.V. announced that it completed a private placement to certain US institutional investors of Euro 150 million aggregate principal amount of 0.91% senior notes due 2032.
Exor, Ferrari and LoveFrom announce creative partnershipOn September 27, 2021, Exor N.V. and Ferrari N.V. announced a long term, multi-year collaboration with the creative collective LoveFrom. The first expression of this new partnership will bring together Ferrari's legendary performance and excellence with LoveFrom's unrivalled experience and creativity that has defined extraordinary world changing products.
Subsequent events
Multi-year share repurchase program: completion of the fourth tranche and announcement of the fifth trancheOn October 4, 2021, Ferrari N.V. informed that it had completed on September 30, 2021 the Fourth Tranche of the multi-year share repurchase program. On the same date, the Company announced the continuation of its already disclosed program with a Fifth Tranche of up to Euro 150 million from October 5, 2021 to no later than March 31, 2022.
Share repurchase programUnder the common share repurchase program, from October 5, 2021 to October 28, 2021, the Company purchased a further 111,495 common shares for a total consideration of Euro 20.9 million. At October 28, 2021 the Company held in treasury an aggregate of 9,884,398 common shares. As of the same date, the Company held 3.84% of the total issued share capital including the common shares and the special voting shares, net of shares assigned under the Company's equity incentive plan.
About Ferrari Ferrari is among the world's leading luxury brands focused on the design, engineering, production and sale of the world's most recognizable luxury performance sports cars. Ferrari brand symbolizes exclusivity, innovation, state-of-the-art sporting performance and Italian design. Its history and the image enjoyed by its cars are closely associated with its Formula 1 racing team, Scuderia Ferrari, the most successful team in Formula 1 history. From the inaugural year of Formula 1 World Championship in 1950 through the present, Scuderia Ferrari has won 238 Grand Prix races, 16 Constructors' World titles and 15 Drivers' World titles. Ferrari designs, engineers and produces its cars in Maranello, Italy, and sells them in over 60 markets worldwide.
Forward Looking StatementsThis document, and in particular the section entitled "Upgraded 2021 guidance", contains forward-looking statements. These statements may include terms such as "may", "will", "expect", "could", "should", "intend", "estimate", "anticipate", "believe", "remain", "continue", "on track", "successful", "grow", "design", "target", "objective", "goal", "forecast", "projection", "outlook", "prospects", "plan", "guidance" or similar terms. Forward-looking statements are not guarantees of future performance. Rather, they are based on the Group's current expectations and projections about future events and, by their nature, are subject to inherent risks and uncertainties. They relate to events and depend on circumstances that may or may not occur or exist in the future and, as such, undue reliance should not be placed on them. Actual results may differ materially from those expressed in such statements as a result of a variety of factors, including: the Group's ability to preserve and enhance the value of the Ferrari brand; the success of the Group's Formula 1 racing team and the expenses the Group incurs for its Formula 1 activities, the impact of the application of the new Formula 1 regulations progressively coming into effect in 2021 and 2022, the uncertainty of the sponsorship and commercial revenues the Group generates from its participation in the Formula 1 World Championship, including as a result of the impact of the Covid-19 pandemic, as well as the popularity of Formula 1 more broadly; the effects of the evolution of and response to the Covid-19 pandemic; the Group's ability to keep up with advances in high performance car technology and to make appealing designs for its new models; Group's ability to preserve its relationship with the automobile collector and enthusiast community; changes in client preferences and automotive trends; changes in the general economic environment, including changes in some of the markets in which the Group operates, and changes in demand for luxury goods, including high performance luxury cars, which is highly volatile; competition in the luxury performance automobile industry; the Group's ability to successfully carry out its growth strategy and, particularly, the Group's ability to grow its presence in China and other growth markets; the Group's low volume strategy; global economic conditions, pandemics and macro events; reliance upon a number of key members of executive management and employees, and the ability of its current management team to operate and manage effectively; the impact of increasingly stringent fuel economy, emission and safety standards, including the cost of compliance, and any required changes to its products; the challenges and costs of integrating hybrid and electric technology more broadly into Group's car portfolio over time; the performance of the Group's dealer network on which the Group depend for sales and services; increases in costs, disruptions of supply or shortages of components and raw materials; disruptions at the Group's manufacturing facilities in Maranello and Modena; the effects of Brexit on the UK market; the performance of the Group's licensees for Ferrari-branded products; the Group's ability to protect its intellectual property rights and to avoid infringing on the intellectual property rights of others; the ability of Maserati, the Group's engine customer, to sell its planned volume of cars; the Group's continued compliance with customs regulations of various jurisdictions; product recalls, liability claims and product warranties; the adequacy of its insurance coverage to protect the Group against potential losses; the Group's ability to ensure that its employees, agents and representatives comply with applicable law and regulations; the Group's ability to maintain the functional and efficient operation of its information technology systems and to defend from the risk of cyberattacks, including on its in-vehicle technology; the Group's ability to service and refinance its debt; the Group's ability to provide or arrange for adequate access to financing for its dealers and clients, and associated risks; labor relations and collective bargaining agreements; exchange rate fluctuations, interest rate changes, credit risk and other market risks; changes in tax, tariff or fiscal policies and regulatory, political and labor conditions in the jurisdictions in which the Group operates, including possible future bans of combustion engine cars in cities and the potential advent of self-driving technology; potential conflicts of interest due to director and officer overlaps with the Group's largest shareholders; and other factors discussed elsewhere in this document. The Group expressly disclaims and does not assume any liability in connection with any inaccuracies in any of the forward-looking statements in this document or in connection with any use by any third party of such forward-looking statements.Any forward-looking statements contained in this document speak only as of the date of this document and the Company does not undertake any obligation to update or revise publicly forward-looking statements. Further information concerning the Group and its businesses, including factors that could materially affect the Company's financial results, is included in the Company's reports and filings with the U.S. Securities and Exchange Commission, the AFM and CONSOB.
For further information:Media Relationstel.: +39 0536 949337Email: media@ferrari.com
Investor Relationstel.: +39 0536 949695Email: ir@ferrari.com
www.ferrari.com
Capex and R&D
189 158 Capital expenditures(10) 506 465
84 79 of which capitalized development costs(13) (A) 264 222
130 111 Research and development costs expensed (B) 402 373
214 190 Total research and development (A+B) 666 595
40 47 Amortization of capitalized development costs (C) 137 132
170 158 Research and development costs as recognized in the consolidated income statement (B+C) 539 505
Operations are monitored through the use of various non-GAAP financial measures that may not be comparable to other similarly titled measures of other companies.
Accordingly, investors and analysts should exercise appropriate caution in comparing these supplemental financial measures to similarly titled financial measures reported by other companies.
We believe that these supplemental financial measures provide comparable measures of financial performance which then facilitate management's ability to identify operational trends, as well as make decisions regarding future spending, resource allocations and other operational decisions.
Certain totals in the tables included in this document may not add due to rounding.
Total net revenues, EBITDA, Adj. EBITDA, EBIT and Adj. EBIT at constant currency eliminate the effects of changes in foreign currency (transaction and translation) and of foreign currency hedges.
2021 at constant 2021 at constant
883 883 Cars and spare parts 2,619 2,670
55 55 Engines 145 145
95 96 Sponsorship, commercial and brand 277 282
20 20 Other 58 61
1,053 1,054 Total Net Revenues 3,099 3,158
270 EBIT 810
270 Adjusted EBIT 810
0 Currency (including hedges) (49)
270 EBIT at constant currency 859
270 Adjusted EBIT at constant currency 859
371 EBITDA 1,133
371 Adjusted EBITDA 1,133
371 EBITDA at constant currency 1,182
371 Adjusted EBITDA at constant currency 1,182
EBITDA is defined as net profit before income tax expense, net financial expenses and depreciation and amortization.
Adjusted EBITDA is defined as EBITDA as adjusted for certain income and costs which are significant in nature, expected to occur infrequently, and that management considers not reflective of ongoing operational activities.
207 171 36 Net profit 619 346 273
53 37 16 Income tax expense 162 81 81
10 14 (4) Net financial expenses 29 38 (9)
101 108 (7) Amortization and depreciation 323 306 17
371 330 41 EBITDA 1,133 771 362
- - - Adjustments - - -
371 330 41 Adjusted EBITDA 1,133 771 362
Adjusted Earnings Before Interest and Taxes ("Adjusted EBIT") represents EBIT as adjusted for certain income and costs which are significant in nature, expected to occur infrequently, and that management considers not reflective of ongoing operational activities.
270 222 48 EBIT 810 465 345
270 222 48 Adjusted EBIT 810 465 345
Adjusted net profit represents net profit as adjusted for certain income and costs (net of tax effect) which are significant in nature, expected to occur infrequently, and that management considers not reflective of ongoing operational activities.
207 171 36 Adjusted net profit 619 346 273
Adjusted EPS represents EPS as adjusted for certain income and costs (net of tax effect) which are significant in nature, expected to occur infrequently, and that management considers not reflective of ongoing operational activities.
For the three months ended (Euro per common share) For the nine months ended
1.12 0.92 0.20 Basic EPS 3.34 1.87 1.47
1.12 0.92 0.20 Adjusted basic EPS 3.34 1.87 1.47
1.11 0.92 0.19 Diluted EPS 3.33 1.86 1.47
1.11 0.92 0.19 Adjusted diluted EPS 3.33 1.86 1.47
Basic and diluted EPS(14)
For the three months ended (Euro million, unless otherwise stated) For the nine months ended
206 171 35 Net profit attributable to the owners of the Company 617 346 271
184,317 184,748 Weighted average number of common shares (thousand) 184,601 184,825
1.12 0.92 0.20 Basic EPS (in Euro) 3.34 1.87 1.47
184,617 185,344 Weighted average number of common shares for diluted earnings per common share (thousand) 184,901 185,422
1.11 0.92 0.19 Diluted EPS (in Euro) 3.33 1.86 1.47
Net Industrial Debt, defined as total Debt less Cash and Cash Equivalents (Net Debt), further adjusted to exclude the debt and cash and cash equivalents related to our financial services activities (Net Debt of Financial Services Activities).
(Euro million) Sept. 30, 2021 Jun. 30, 2021 Mar. 31, 2021 Dec. 31, 2020
Debt (2,591) (2,360) (2,283) (2,725)
of which leased liabilities as per IFRS 16 (simplified approach) (59) (61) (62) (62)
Cash and Cash Equivalents 1,273 922 980 1,362
Net Debt (1,318) (1,438) (1,303) (1,363)
Net Debt of Financial Services Activities (950) (886) (883) (820)
Net Industrial Debt (368) (552) (420) (543)
Free Cash Flow and Free Cash Flow from Industrial Activities are two of management's primary key performance indicators to measure the Group's performance. Free Cash Flow is defined as cash flows from operating activities less investments in property, plant and equipment (excluding right-of-use assets recognized during the period in accordance with IFRS 16 - Leases) and intangible assets. Free Cash Flow from Industrial Activities is defined as Free Cash Flow adjusted to exclude the operating cash flow from our financial services activities (Free Cash Flow from Financial Services Activities).
392 218 Cash flow from operating activities 927 428
(189) (158) Investments in property, plant and equipment and intangible assets(10) (506) (465)
203 60 Free Cash Flow 421 (37)
(39) (17) Free Cash Flow from Financial Services Activities (81) (29)
242 77 Free Cash Flow from Industrial Activities(15) 502 (8)
On November 2, 2021, at 3:00 p.m. CET, management will hold a conference call to present the Q3 2021 results to financial analysts and institutional investors. Please note that registering in advance is required to access the conference call details. The call can be followed live and a recording will subsequently be available on the Group's website http://corporate.ferrari.com/en/investors. The supporting document will be made available on the website prior to the call.
1 Refer to specific paragraph on non-GAAP financial measures
2 These results have been prepared in accordance with International Financial Reporting Standards (IFRS) as issued by the International Accounting Standards Board and IFRS as endorsed by the European Union
3 Excluding the XX Programme, racing cars, one-off and pre-owned cars
4 EMEA includes: Italy, UK, Germany, Switzerland, France, Middle East (includes the United Arab Emirates, Saudi Arabia, Bahrain, Lebanon, Qatar, Oman and Kuwait), Africa and the other European markets not separately identified; Americas includes: United States of America, Canada, Mexico, the Caribbean and Central and South America; Rest of APAC mainly includes: Japan, Australia, Singapore, Indonesia, South Korea, Thailand, India and Malaysia
5 Includes net revenues generated from shipments of our cars, any personalization net revenues generated on cars, as well as sales of spare parts
6 Includes net revenues generated from the sale of engines to Maserati for use in their cars, and the revenues generated from the rental of engines to other Formula 1 racing teams
7 Includes net revenues earned by our Formula 1 racing team through sponsorship agreements and our share of the Formula 1 World Championship commercial revenues, as well as revenues generated through the Ferrari brand, including merchandising, licensing and royalty income
8 Primarily relates to financial services activities, management of the Mugello racetrack and other sports-related activities
9 Also known as Notional Interest Deduction - NID
10 Capital expenditures excluding right-of-use assets recognized during the period in accordance with IFRS 16 - Leases
11 Net of a tax benefit, with no cash impact on 2020, from the one-off partial step-up of the trademark's book value in accordance with the Italian tax regulations
12 Calculated using the weighted average diluted number of common shares as of December 31, 2020 (185,379 thousand)
13 Capitalized as intangible assets
14 For the three and nine months ended September 30, 2021 and 2020 the weighted average number of common shares for diluted earnings per share was increased to take into consideration the theoretical effect of the potential common shares that would be issued under the equity incentive plans
15 Free cash flow from industrial activities for the three and nine months ended September 30, 2020 includes approx. Euro 1 million related to withholding taxes
2021_11_02 - Ferrari Q3 2021 Results Press Release
Ferrari (BIT:RACE)
Von Dez 2021 bis Jan 2022
Von Jan 2021 bis Jan 2022
FERRARI N.V.: PERIODIC REPORT ON THE BUYBACK PROGRAM
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Der Markt LS (Lang&Schwarz) wird als Realtime Indikation kostenlos angezeigt und bietet Ihnen außerbörsliche Realtime Aktienkurse in der Zeit von Mo-Fr 08:00 bis 23:00, Samstags 10:00 bis 13:00 und Sonntags an.
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P: V:de D:20220118 04:43:50


text:
Q: Group stream elements and sum a field using JQ I am trying to group several items by their name based on the JSON-Data. Furthermore I want to calculate the sum of the size. Therefore I am using jq to transforming the data. Attached you see the raw data.
{
  "fields": {
    "issuetype": {
      "name": "Server"
    },
    "size": 2
  }
}
{
  "fields": {
    "issuetype": {
      "name": "Server"
    },
    "size": 2
  }
}
{
  "fields": {
    "issuetype": {
      "name": "Hardware"
    },
    "size": 0.58
  }
}

The transformed data should be structered like this:
[
  {
    "item": "Hardware",
    "size": 0.58
  },
  {
    "item": "Server",
    "size": 4
  }
]

I am using the following code
jq -s < input.json "group_by( .fields.issuetype.name ) | .[] | item: .[0].fields.issuetype.name), size:([.[].fields.customfield_1234] | add)" > transformedData.json

A: Almost there. To construct objects, you need object contructors.
  group_by(.fields.issuetype.name)
| map({item: .[0].fields.issuetype.name, size: map(.fields.size) | add})

Online demo


In our annual Art Alive contest—which celebrated its 20th anniversary in 2016—participants create living representations of famous works of art for more than $1,000 in cash prizes. Participants build their stunning replicas, or tableaux, using people, props, music, and more.
Art Alive takes place each year on the first Saturday of June. We invite the entire community to join us to see the overwhelming creativity on display, and vote for your favorites in the People's Choice Awards.


text:
cask 'vmware-horizon-client' do
  version '5.4.2-15910566,CART21FQ1'
  sha256 '2d5668402ef372656b487e8e868a8a5a139ec4b8ff88b10d15772231f0988c26'

  url "https://download3.vmware.com/software/view/viewclients/#{version.after_comma}/VMware-Horizon-Client-#{version.before_comma}.dmg"
  appcast 'https://docs.vmware.com/en/VMware-Horizon-Client-for-Mac/',
          must_contain: version.major_minor
  name 'VMware Horizon Client'
  homepage 'https://www.vmware.com/'

  depends_on macos: '>= :sierra'

  app 'VMware Horizon Client.app'
end


CHESTER - The Milkweed artist organization is opening a new exhibit with a reception on April 27.
The reception and social viewing of the "Imperatively Light" artwork of Melissa Spiegel and Alanna Floreck is scheduled to take place at it's Romer Alley location between 6-9 p.m. on April 27.
According to its website, Milkweed is an interdisciplinary art and event space dedicated to collaboration and community-building.
One half of the space is an ever-changing gallery, featuring the work of local contemporary artists working in a variety of media.
The second half of the space is a working studio used by the Milkweed Collective and open to the public, which frequently hosts arts and skill sharing workshops.
In this exhibit, Spiegel uses a 'from the ground up' approach to create handmade paper and pressed flower collages, focusing on the material itself.
Floreck will be displaying photography from her previous "Primavera" collection featured last year at Caffe a la mode, but this time conveying a new message, complemented by and blending with Melissa's work.
They will also be collaborating on two brand new mixed media pieces specifically for the show.
'Imperatively Light' emphasizes the importance of art that illustrates the softness and simplicities in our world.
During a time when there are extreme hardships and brutalities occurring every day, it Is important for artists to use the creative power to speak out on these events.
The group believes it is also quintessential to have work that is, in essence, aesthetically pleasant, with the hopes of instilling peace and serving as a reminder that the so-called "mundane" is worth looking at in depth.
Live music during the reception will be provided by Shannon Rogers, Dylan Holzhauer, and Dan Wixtrom and Kayla Holterman.
The organization is also planning its "Battle Candle" festival, which is coming Aug. 3.
Interactive Noise Collective (that's the group that runs the Milkweed space these days) has started planning its second annual music festival at the Wickham Woodlands, from 11 a.m. to 7 p.m. on Aug. 3.
"Battle Candle" will include, music all day, local arts and craft vendors, food trucks, interactive art, all by the backdrop of the beautiful Wickham Lake.
This year INC has joined forces with the village of Warwick and become the Warwick event for the Three Lakes Festival, a series of events aimed at creating more crossover and connection between the three villages (and their lakes) of Warwick, Florida and Greenwood Lake.
Tickets for the festival go on sale May 1 at battlecandle2019.brownpapertickets.com.


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Flip's new companion hardware to their pocket camcorders is a logical extension of the Flip brand. It brings Flip-taken videos (and nothing else) to a TV, and while some may find its simplicity refreshing, others will find it limiting.
We knew about the FlipShare TV awhile ago, thanks to an FCC leak. It's basically a companion piece to Flip's camcorders, streaming video shot with the Mino HD, Ultra HD or what have you to a TV. But oddly enough, it's dumbed-down enough that it somehow circles back around and becomes complicated again, starting with the seemingly-basic hardware.
The FlipShare TV is actually comprised of three necessary components: A cute, small, square white box about the size of a Roku that plugs into your TV (featuring RCA and HDMI ports); a fairly large USB dongle; and a super simple remote reminiscent of Apple's remote. The USB dongle is actually required to be plugged in (and your computer turned on) if you want to use the FlipShare TV at all, forming kind of an point-to-point wireless network. Yeah, it's supposed to eliminate the trouble of logging into a wireless home network, but it brings up problems of its own—what about multi-computer households? And while it may be a pre-release issue, my box dropped the connection with its dongle when the two were on opposite ends of my apartment, a problem my normal wireless router doesn't have.
Also released today is the updated FlipShare 5.0 software, which is easy enough to use. Plug in a camcorder, drag its videos to your library, and your precious moments can then be watched on your computer or via the FlipShare TV, or alternately shared via Facebook or YouTube. Also supported are FlipChannels, a sort of private online video storage that lets you upload videos and "share" access with other people. Anyone with whom the FlipChannel is shared can watch your videos on a computer, mobile device (iPhone, Android, and BlackBerry are supported) or, again, through the FlipShare TV. So those are your two choices for streaming: Computer or FlipChannel.
Therein lies another problem: The FlipShare TV can only play back the very specific video format used by Flip camcorders, some kind of WMV at 30fps. Nothing else. This is not a media streamer, it's a Flip streamer. Music? Forget it. Your ripped DVDs? No way. (JPEGs are also supported, but that's it for non-Flip media). Playback quality is good, but these aren't exactly super high-quality files we're dealing with here.


I finished Smooth & Easy Days & downloaded Masterly Inactivity but didn't start it. Maybe I'm getting smarter.
Still working on Smart Moves.
The Fitting Room. You know, every time I think I've made great progress with this book, I look down at the bottom and it doesn't even say 50% done. I think the format confuses me. I'll get use to e-books some day. It is going faster now that I've finished the difficult part. It's a long story, probably best for therapy. Suffice to say legalism wasn't my problem so I had to work through her words when I'd been beat over the head with similar words in defense of what I would consider antinomianism.
Somewhere in April I went from, "I'm going to read To Walk or Stay by Lara G Williams some day," to, "I'm going to read it now with the online book group." So now I've added that. Much more accountability as we post to the group weekly. It's really a very good book. I had put it in my "too read" list originally because my situation doesn't really mirror hers, then I read a quote I guess it was, wish I could remember exactly what the words were, and I realized it would be very beneficial to read it now. I like that it doesn't dwell on the specifics and thereby alienate readers who haven't been through her same experience. In short, we all need to grow in our faith. We all need to stop relying on ourselves and worshiping an ideal...oh, that was it. She said something about believing Hollywood's lie about what love was and I thought, well, it wasn't Hollywood's lie I fell for (it was a bizarre religious upbringing that basically stated that a woman could not be whole if she was un-wed and without children) but I had a feeling that her book was going to be extremely helpful...and it has been.


I know this site is dedicated to underground HipHop, but every now and we love to feature dope artist no matter the genre. BB Thomas is that type of artist, I love a good "Rags to Riches" story and hers definitely got my attention.
The "Deutschland" singer, songwriter, producer, is a self taught musician who plays the piano, and guitar with a Masters Degree in Musicology. BB started her career as a model for brands like Adidas, Reebok, Nike, and Playstation her passion for music is undeniable.


text:
Console processor for ASS subtitles.

Feature requests and any kind of feedback are welcome.

### Why?
Imagine you've got a few dozens of subtitle files and you want to apply TPP to each of them, and maybe also restyle one of the files and use the same group of styles for everything else. This script allows you to easily do it from console so you don't have to suffer with Aegisub.

### Usage
The main script is called `prass.py` and it provides a few commands for working with subtitles.
```bash
# to convert subtitles from SRT to ASS
prass convert-srt input.srt -o output.ass
# to copy styles from one ASS script to another
prass copy-styles --from template.ass --to input.ass -o output.ass
# to sort an ASS script
prass sort input.ass --by time -o output.ass
# to run tpp
prass tpp input.ass -s default,alt --lead-in 100 --lead-out 200 --keyframes kfs.txt --fps 23.976 --kf-before-start 150 --kf-after-start 150
# to cleanup a script
prass cleanup --comments --empty-lines --styles input.ass -o output.ass
# to shift start time back by 1 minute and 10 seconds
prass shift --start --by -1:10 input.ass -o output.ass
```
Some parameters are not mentioned - just run `prass --help` or `prass %command% --help` to see the full docs.

### Pipes
Prass more or less supports pipes and allows you to do fun stuff like
```bash
prass convert-srt input.srt | prass copy-styles --from template.ass | prass sort --by time | prass tpp --overlap 150 --gap 150 -o out.ass
```
If you don't provide some file arguments, most commands will use stdin/stdout by default. They will also do this if you provide "-" as file paths.

### Installation
Prass should work on OS X, Linux and Windows without any problems, both on Python 2.7.x and Python 3.x (but py2 is preferable). Right now the only dependency is [Click](http://click.pocoo.org/3/). Assuming you have python and pip, just run:
```bash
pip install git+https://github.com/tp7/prass
```