How are far are you willing to change your mind and evangelize?
Catholics need to change direction: to move from the Counter-reformation approach, what is now too ghetto-like today, to one that is more evangelical. We are indeed moving in that direction but as far as I can see, we are moving too slowly. The work started by Pope Leo XIII and concluded by Pope Benedict XVI still is highly instructive. What is clear to historians, it was Leo who changed the Church's approach to education, culture, politics, etc. that was evident in previous generations in the pontificates of Gregory XVI and Pius IX. Leo's 25 years and Benedict's 8 years are bookends. Leo dealt with all the points of human history; Benedict continued what John Paul started in completing the Second Vatican Council and bridging faith and reason. What Francis will give us is still too new to determine in a critical way.
All this is to encourage you to read and fully digest George Weigel's Evangelical Catholicism: Deep Reform in the 21st Century Church (Basic Books). I wrote about this book earlier here on Communio. What I said earlier remains the same now: Weigel is proposing to new evaluation, a new way for Catholics to engage the world. What we've been doing is simply not working anymore. Some Catholics are being eased into mediocre Christianity, others are walking away, others are searching for new ways to propose the truth of Jesus Christ and His sacrament, the beauty Catholic life. What Weigel suggests –and I am not going to give away the detail of his work here– is potentially going to anger some people and make others happy; he's challenging every concept we have with regard to our ecclesial life: catechetics, preaching, liturgical music, the celebration of sacraments, matters of governance, education at all levels, how we use resources (economic and human), and so forth. Nothing is left unaffected by this public intellectual.
It is my considered opinion that dioceses and religious orders ought to take heed and at the very least allow Weigel's ideas to ruminate in heart and mind. We can be uncritical of the ghetto mentality of Christian living and expect to be doing what Christ wants us to do. If it is, why are so many priests doing bad things, dioceses going bankrupt, Catholic hospitals merging with secularist institutions, religious order dying because they don't, won't, can't live their charism with new vigor, parishes closing, etc.?


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Kitchen design blog authored by susan serra ckd certified designer providing insight and information on style function products appliances, the kitchen design is our next project were renovating old victorian house and we need a plan for before gut it redesign. Realstone systems reclaimed wood is the perfect solution to giving that extra bit of warmth your space, systems milano portal by bulthaup receives two design awards this sleek online showcases bulthaups latest expansions to their b and as they, you arrived on this article searching for living room interior design ideas im sure and youll be delighted to hear that we have plenty, your small bedroom doesnt have to an elaborate color scheme be romantic the most appealing bedrooms are often decorated in neutral colors. Karen renee interior design is a full service firm specializing in residential and commercial projects as well model home merchandising we are an.
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text:
Q: Auto minify Javascript/CSS files after update? I know of ways to minify JS and CSS files but is there a way to auto-minify these files in the production system? For instance, I modify something inside the original JS files and update the production environment. However, while using the JS files, it should use an auto-minified version of the JS file (preferably update the current minified version if the source files changed).
Considering that I work in both Unix and Windows environment, writing a bash script did not seem like a plausible idea. So anything that is OS independent or maybe something that uses PHP initially to do this would be great. Any suggestions?

A: You might find value in minify. It uses PHP5, and can sit well on your production server.
From the website:

It combines multiple CSS or Javascript files, removes unnecessary whitespace and comments, and serves them with gzip encoding and optimal client-side cache headers.

A: I agree with @RC, read those existing Q/As.
Also other options for your specific question:

*

*Post-Commit hook - minify step

*Build step during development, commit minified files

*Server side filter that minifies on file change (Rails and other frameworks have this built in)

*Enhance you deployment script to do the minify during deployment

Write these hooks in a cross platform language instead of bash. (JavaScript, Python, Ruby, etc).

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text:
Final Fantasy 3 – When Magic Disappeared Forever
Ages ago, evil beings created powerful creatures called Espers, and unleashed them against each other. The resulting battles left their world a smoldering rubble. Legend has it, the Espers destroyed themselves and most of humanity. Magic disappeared forever.
Centuries have passed and a rational world now exists with Espers living only in myths, until one frozen solid since the ancient wars is unearthed. Suddenly, there are reports of magical attacks on civilians. Imperial Commandos launch raids using magic powered MagiTek weapons. Magic is obviously alive and the world is in danger again. Who or what is behind the rediscovery and redeployment of this legendary power? What chaotic plans exists that will wreak havoc on this orderly world?
Final Fantasy III is one of what many consider to be the classics for RPG genre games. Released as Final Fantasy III for the SNES in 1994, it is actually the 6th installment of the immensely popular Final Fantasy series produced by Squaresoft. The game takes place about 1000 years following the ending of a great war called "The War of the Magi" which removed magic from the face of the world.
It is a typical turn based RPG with the player having control of over 15 playable characters each one with his or her own strengths and weaknesses and different fighting styles and stories to tell. The main character is a young half-human, half-Esper girl whom is trying to find her place in a world torn asunder by war. The main villain in the story is one of the most colorful villains in the Final Fantasy series, a rather funny clown named Kefka.
Joining forces with him are a few other military style villains with lesser roles and even a few NPCs who get involved. There are many plot twists that include cut scenes involving characters that allow the player to have a "real-time" feel with the story. The characters have "expressions" that while being very basic, convey the general theme of each scene to the player. In my opinion, this game is perfect for the player who wants to see some of the best the SNES had to offer in terms of RPGs.
As far as games for the SNES go, there are only 1 or 2 other games as engrossing as Final Fantasy III. All of the elements that make the other games in the series enjoyable are here. The player can rename all of the characters in the game including the ever present summons (called Espers in FFIII).
There are a multitude of side quests in the game that vary in difficulty from easy to difficult in terms of time and involvement to complete, and the level of commitment necessary to complete the game can vary between 25 hours. To just finish the core storyline of the game, can be up to 100 hours give or take. This is if you want to obtain what is called a "complete" gaming experience meaning gathering all of the most powerful weapons, armor, and magic, and also leveling characters up to maximum levels.
The only reason the game is not getting a 10 rating in this department is the fact that while leveling characters is not a problem in the beginning and middle of the game, once a character reaches the higher levels (above 60) it becomes a very time consuming, tedious process to level up the character sometimes taking hours upon hours to raise a character just one level. This I would say is the main common problem with RPGs of this era. But, if you do not mind that sort of monotony, this game is for you.
The characters in Final Fantasy 3 offer a host of clever individual attacks. Each character has his or her own special talents and the player can choose to utilize each character's talents or can just ignore them. An essential part of each Final Fantasy is magic, and this game is no exception. There are a multitude of magics available to the player to use, each one learned from equipping certain Espers.
The longer an Esper is equipped, the more magic is obtained from the Esper and once the learning curve for the Esper reaches 100%, all of the magic available from that Esper is learned. Some magic is able to be learned from two to four Espers, while other magic may only be learned from one specific Esper. This makes Esper use a conscionable thought process. The player must plan their use of Espers in order to learn the needed spells.
Again, I am comparing this to other SNES games. This game is 2-D. Plain and simple. It features a 3/4 overhead view 90% of the time and also features an overworld which has since been all but removed from most RPGs. The graphics were considered state of the art in 1994 when this game was released. There are rich color textures and some very good use of the Mode-7 graphics capabilities of the SNES in both scaling and rotation which are show cased especially when the characters use the airship for transportation.
As far as actual graphic renderings are concerned, the game is 2-D, so if you are expecting to see walking, talking, fully rendered 3-D you are out of luck. In scenes where the graphics are made to be inflated or close up, they become pixilated the larger they become. These problems aside, the graphics for its day, when compared to other games out at the time, were considered to be very quite advanced.
Sound Quality:
Here's where the game shines. The score is enormous! Created by the world-renowned Nobuo Uematsu, there are at least 100 different songs in the game (including renditions of the main theme) and also includes a scene with one of the earliest examples of voiced "singing" in video games. The songs feature 128 note polyphony and a beautifully detailed musical story. Because the game's dialogue is text based, the music allows the player to get involved on a more emotional level with this game and the characters than many other games out at the time.
There is a great combination of deep bass, singing strings, and synthesized keyboards to keep the listener enthralled and engaged throughout the game. There are very few songs that last less than five minutes without repeating so the player never really gets the boring monotonous feeling that usually accompanies games from the SNES.
Replay Value:
There are very few games that can be left to sit for years on a shelf and then picked up and played again with the same level of commitment and enjoyment as Final Fantasy III. The game is just as much fun the every other time through as it was the first time through. As a matter of fact, with all of the side quests and obtainable items, weapons, armor, and magic, the game could possibly be one of the hardest RPG's created for the SNES to obtain a "perfect" or 100 percent complete game. There are always ways to expand the difficulty of the game and make each play through a unique experience.
Not exactly the most cutting edge in gaming, this game has the very familiar "fight the monsters and gain levels before fighting the final boss and saving the world" theme. While the Action RPG gamer will find this game very repetitive, the fan of the Turn Based style RPG gamer will love it.
Having a female as the main character in the game is a concept that was not used very much prior to Final Fantasy III. This seemed to be a risky idea but Square pulled it off flawlessly. Also, with all of the other characters in the game, the stories unfold rather nicely for each character. This adds to the depth of the game as well as the entertainment concept.
If you are a fan of the Final Fantasy series, a collector of vintage games, or a person who is interested in getting involved in the series but is worried about the complexity of the newer Final Fantasy titles, this game is for you. Final Fantasy III is great for the "old-school" player and the "newbie" alike. It has a great story, great sound, and WILL take over your life for a few days if you let it. The characters are original, have many different abilities to use, and have emotions that make playing this game really great.
The NPCs seem to have more of an impact in this game as opposed to most and the main characters are some of the most imaginative I've ever come across. The towns are sprawling, the graphics are engaging, and the sound is rich and vibrant. The story unfolds well, and from the opening scene, most players are hooked. The enemies are varied and numerous and the bosses difficult while not being impossible. I highly recommend this game to anyone who owns a SNES.

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This book tag was originally created by TheBookArcher on YouTube, and it looked like a lot of fun so I decided to do it!
Twilight by Stephanie Meyers…I didn't hate the first book but I could not get into it whatsoever. I read the first hundred or so pages, but then skimmed the rest and the only reason I know what happens is because I have seen the movies, which honestly, I prefer over the books.
I wouldn't necessarily say everyone hates it, but there is controversy surrounding this trilogy and that is The Summer I Turned Pretty trilogy by Jenny Han. It is very cliché, and I do see its flaws, but overall I really enjoyed it and can see myself re reading it in the future!
I am going to go with an OTP I don't like,and if you haven't read Lady Midnight by Cassandra Clare, you might not want to read this, but it isn't that big of a spoiler. For this I am going to have to say Mark Blackthorn and Kieran because I do not like Kieran. I think Mark deserves SO much better and while Kieran does have his moments, he did something that I will never, ever forgive him for.
I do have a few books of this genre, but I hardly ever reach for it on my own, and that is sci-fi. I do have the first two books in the 5th Wave trilogy by Rick Yancy, and also the Lunar Chronicles by Marissa Meyer, but other than that I don't really read to much sci-fi.
Hmm…okay a popular character I don't like that everyone else seems to is Lissa from Vampire Academy by Richelle Mead. Lissa is portrayed as a very sweet character, but I found her very, very annoying at times.
John Green! I have read three of his books and I mean, they were okay, but I don't think I will pick up anything else of his. I just find his stories are all the same and his writing just isn't for me!
Hmm, there are a lot but I am going to have to say the "I really like them but I am not good for them" book trope because ugh, it is so aggravating. If you like the person don't push them away! Even if you think you are "bad" for them, they probably won't care if they also like you.
Okay, so I am not going to say I will never read this because I am sure eventually I will, but at the moment I have no desire to read Illuminae by Jay Kristoff and Amie Kaufman…I am sure it is amazing, from what I have heard, but at the moment it just doesn't sound like something I would be into, but possibly sometime in the future!
9. The Saying Goes "The Book is Always Better than the Movie" but What Movie or TV Show Adaptation Do You Prefer Over the Book?
For this I am going to have to go with, hands down, The Maze Runner by James Dashner. I enjoyed the movie (both of them) way, way more than the books. I found the books very plain and not very captivating, but I thought the movies were pretty good!
Those are my answers for the Unpopular Opinions Book Tag and I hope you enjoyed. Don't forget to check out my last blog post: Reading/Writing Update!
Right now I am going through exams which of course, are extremely stressful, and during my study breaks, I find that reading a light and easy book is a great way to relieve the heavy stress upon my shoulders…since I am sure a lot of you can relate to this feeling, her are a few stress relieving books that I recommend!
Lola and the Boy Next Door by Stephanie Perkins – In this book you get totally engrossed with the life of the characters, and it erases any stress, anxiety or worry you were feeling before. It is playful, light hearted and a very fun read that I 100% recommend. Lola and the Boy Next Door is about a girl named Lola who is living a great life. She has a great boyfriend, an amazing best friend, and is almost finished high school, but when her first love moves back next door, her life is flipped upside down.
To All the Boys I've Loved Before by Jenny Han – This book is such a fun and entertaining read, and is one of my favourite contemporaries ever! In this book, we follow Lara Jean, a shy and quiet girl who writes love letters to every boy she has loved before. When her secret letters somehow get out, she finds herself digging herself in a deep hole of lies to prevent her current crush from knowing she truly likes him.
The Selection by Kiera Cass – The Selection is a very quick and easy read that will have you laughing and squealing at various times throughout. The Selection is about a girl named America who has entered herself into a competition for the Prince of Illea's heart, but things get complicated because she is in love with someone else. I love these books and if you haven't read them, read them!
Vampire Academy by Richelle Mead – Despite this book having supernatural people and fighting, you find yourself diving deep into the world and leaving our world behind easily. Vampire Academy is a great series that makes you feel a lot of great things. In this book, we follow Rose Hathaway, a girl who has been created to protect those higher than her, the Moroi (aka vampires), but when she and her best friend Lissa, return to the Vampire Academy after fleeing the previous year, they must both defend themselves against not only against the evil inside but outside as well.
Those are all my stress relieving book recommendations and I hope you enjoyed!


text:
\section{INTRODUCTION}

A principal tool in the analysis of sleep is the polysomnography (PSG).
Standard PSGs contain electroencephalography (EEG), electrooculography (EOG), electromyography (EMG) from below the chin and lower limbs, electrocardiography, respiratory effort, and blood oxygenation, which is manually analysed by sleep experts according to guidelines published by the American Academy of Sleep Medicine~\cite{Berry2020}.

Experts score sleep stages and annotate discrete events, such as arousals (short awakenings during sleep, $\leq \SI{15}{\second}$), limb movements, and decreased respiratory effort characterized by apneas (complete cessation of breathing), hypopneas (partial cessation of breathing), and desaturations (decreases in oxygen desaturation).
Low inter-rater reliability has been reported for sleep stages scoring in multiple studies~\cite{Norman2000, Rosenberg2013, Younes2016}, arousals~\cite{Bonnet2007}, and respiratory events~\cite{Magalang2013, Rosenberg2014a}, prompting extensive research in automated methods for sleep analysis~\cite{Olesen2018c, Stephansen2018, Chambon2018, Biswal2018, Phan2018, Carvelli2020, Brink-Kjaer2020}.

Designing reliable and robust systems for automated sleep analysis based on machine learning algorithms often require multiple heterogenous data sources of sufficient size.
However, due to differences in clinical practice, very few datasets in sleep science are standardized with regards to recording setups despite guidelines from the AASM.

In these cases, we end up with a \textit{channel mismatch problem}, in which the overlap between our source and target domains is small, and the domains are possibly disjointed.
Recent studies have investigated the use of deep transfer learning to solve the channel mismatch problem when training and testing sleep stage classification models~\cite{Phan2019, Phan2019c}.
The authors found that using a fine-tuning strategy significantly improved the performance of sleep stage scoring models when trained on various combinations of EEG and EOG channels.

We present results on using deep transfer learning to address the channel mismatch problem, when the source and target domains differ both in the number and type of channel modalities.
Specifically, our source domain consists of multivariate PSG data comprising left and right central EEG, left and right EOG, and submental EMG recordings, while our target domain consists of only a single central EEG channel.
We show that by employing a simple fine-tuning strategy on a pre-trained network stripped of the initial two layers, we can effectively reach the same level of F1 score as when using the full set of PSG data.

\section{METHODS}
\begin{table*}[tb]
    \centering
    \begin{threeparttable}
        \caption{Network architecture overview.}
        \label{tab:architecture}
        \begin{tabular}{@{}llllllll@{}}
            \toprule
            \textbf{Module} & \textbf{Layer type} & \textbf{Kernel} & \textbf{Stride} & \textbf{Feature maps} & \textbf{Input size} & \textbf{Output size} & \textbf{Activation} \\ \midrule
            \(\mathbf{x}\) & Input & --- & --- & --- & \( C \times T \) & \( 1 \times C \times T \) & --- \\
            \( \phi_{\mathrm{mix}} \) & 2D convolution & \( (C, 1) \) & \( (1, 1) \) & \( C \) & \( 1 \times C \times T \) & \( C \times 1 \times T \) & ReLU \\ \midrule
            \( \varphi_{\mathrm{conv},1} \) & 2D convolution & \( (1, c) \) & \( (1, s) \) & \( 2f_0 \) & \( C \times 1 \times T \) & \( 2f_0 \times 1 \times T/s \) & --- \\
            & Batch norm. & --- & --- & \(2f_0\) & \(2f_0 \times 1 \times T/s\) & \(2f_0 \times 1 \times T/s\) & ReLU \\
            \( \varphi_{\mathrm{conv},k} \) & 2D convolution & \( (1, c) \) & \( (1, s) \) & \( f_02^{k} \) & \( f_02^{k-1} \times 1 \times T/s^{k-1} \) & \( f_02^{k} \times 1 \times T/s^k \) & --- \\
            \( k \in \llbracket 2, k_{\mathrm{max}} \rrbracket \) & Batch norm. & --- & --- & \(f_02^{k}\) & \(f_02^{k} \times 1 \times T/s^k\) & \(f_02^{k} \times 1 \times T/s^k\) & ReLU \\
            \( \varphi_{\mathrm{rec}} \) & bGRU & --- & --- & $f^{\prime}$ & \( f^{\prime} \times 1 \times T^{\prime} \) & \( f^{\prime} \times 2 \times T^{\prime} \) & --- \\ \midrule
            \( \psi_{\mathrm{clf}} \) & 2D convolution & \( (2, 1) \) & \( (1, 1) \) & \( \left( K + 1 \right) \! N_d \) & $f^{\prime} \times 2 \times T^{\prime}$ & $\left( K + 1 \right) \! N_d \times 1 \times T^{\prime}$ & Softmax over \( K + 1 \) \\
            \( \psi_{\mathrm{loc}} \) & 2D convolution & \( (2, 1) \) & \( (1, 1) \) & \( 2 N_d \) & $f^{\prime} \times 2 \times T^{\prime}$ & $2 N_d \times 1 \times T^{\prime}$ & Linear \\ \midrule
            \(\mathbf{z}\) & Output, \(\mathbf{p}\) & --- & --- & --- & $\left( K + 1 \right) \! N_d \times 1 \times T^{\prime}$ & $N_d \times T^{\prime} \times \left( K + 1 \right)$ & --- \\
             & Output, \(\mathbf{y}\) & --- & --- & --- & $2 N_d \times 1 \times T^{\prime}$ & $N_d \times T^{\prime} \times 2$ & --- \\
            \bottomrule
        \end{tabular}
        \begin{tablenotes}
            \small
            \item \(\mathbf{x}\), input containing PSG data; $\mathbf{z}$, output containing predicted arousal probabilities and associated start and duration predictions; \( \phi_{\mathrm{mix}} \), non-linear mixing block; \(\varphi_{\mathrm{conv}}\), convolutional feature extraction block; \( \varphi_{\mathrm{rec}} \) recurrent feature extraction block; \( \psi_{\mathrm{clf}} \), event classification block; \( \psi_{\mathrm{loc}} \), event localization block; $C$, number of input channels; $T$, number of samples in a segment of PSG data; $c$, temporal kernel size; $s$, temporal stride; $f_0$, base number of feature maps; $f^{\prime}=f_02^{k_{\mathrm{max}}}$, maximum number of feature maps; $T^{\prime} = T/s^{k_{\mathrm{max}}}$, reduced temporal dimension in samples; $N_d$, number of default event windows in segment; $K$, number of classes; ReLU, rectified linear unit; bGRU, bidirectional gated recurrent unit.
        \end{tablenotes}
    \end{threeparttable}
\end{table*}
\paragraph*{Notation} We denote by \(\llbracket a, b \rrbracket\) the set of integers \(\lbrace n \in \mathbb{N} \mid a \leq n \leq b\rbrace\) with \(\llbracket N \rrbracket\) being shorthand for \(\llbracket 1, N \rrbracket\), and by \(n \in \llbracket N \rrbracket \) the \(n\)th sample in \(\llbracket N \rrbracket\).
A model for a given experiment is denoted by $\mathcal{M}_{(\cdot)}$, while an optimized model is superscripted with a star as $\mathcal{M}_{(\cdot)}^{*}$.
A segment of PSG data is denoted by $\mathbf{x} \in \mathbb{R}^{C \times T}$, where $C, T$ is the number of channels and the duration of the segment in samples, respectively.

\subsection{Data}
We collected PSGs from \num{1500} subjects in the MrOS Sleep Study~\cite{Blank2005, Orwoll2005, Blackwell2011} from the National Sleep Research Resource repository~\cite{Dean2016, Zhang2018}.
From each PSG, we extracted left and right EEG, left and right EOG, and chin EMG.
EEG and EOG channels were referenced to the contralateral mastoid process.
For each PSG, we also extracted time-stamped arousal scorings containing starts and durations of scored arousal events.
We did not exclude any PSGs from this study based on sleep duration, number of arousal events, or similar criteria.

\subsection{Data partitioning}
The 1500 PSGs were initially partitioned into three subsets \textsc{train}\textsubscript{1}, \textsc{eval}\textsubscript{1}, and \textsc{test}\textsubscript{1} containing 400, 100 and 1000 PSGs, respectively.
Furthermore, we additionally partitioned \textsc{test}\textsubscript{1} into three smaller subsets \textsc{train}\textsubscript{2}, \textsc{eval}\textsubscript{2}, and \textsc{test}\textsubscript{2} containing 400, 100, and 500 PSGs, respectively.

\subsection{Preprocessing pipeline}
All signals were resampled to \SI{128}{\hertz} using poly-phase filtering with a Kaiser window ($\beta = 5.0$) prior to subsequent processing.
Extracted EEG and EOG signals were filtered with \nth{2} order Butterworth IIR bandpass filters with cutoff frequencies \SI{0.3}{\hertz} and \SI{35}{\hertz}.
Chin EMG was filtered with a \nth{4} order Butterworth IIR highpass filter with a cutoff frequency of \SI{10}{\hertz}.
Filtered signals were subsequently standardized by
\begin{equation}
    \mathbf{x}^{(i)} = \frac{\tilde{\mathbf{x}}^{(i)} - \boldsymbol{\mu}^{(i)}}{\boldsymbol{\sigma}^{(i)}},
\end{equation}
where $\tilde{\mathbf{x}}^{(i)} \in \mathbb{R}^{C \times T}$ is the raw matrix containing $C$ input channels and $T$ samples, and $\boldsymbol{\mu}^{(i)}, \boldsymbol{\sigma}^{(i)} \in \mathbb{R}^{C}$ are the mean and standard deviation vectors for the \textit{i}'th PSG, respectively.

\subsection{Model setup}

We expand upon previous work using similar models for sleep event detection~\cite{Chambon2018b, Chambon2019, Olesen2019}.
Briefly, the model takes as input a tensor of PSG data $\mathbf{x} \in \mathbb{R}^{C \times T}$ and outputs
\begin{equation}
    \mathbf{z} = \left( \mathbf{p}, \mathbf{y} \right) \in \mathbb{R}^{N_d \times T^{\prime} \times (K+1)} \times \mathbb{R}^{N_d \times T^{\prime} \times 2}
\end{equation}
containing predicted arousal probabilities $\mathbf{p}$ and associated start and durations for predicted arousal events $\mathbf{y}$.
The differentiable function underlying the model comprises a deep neural network architecture consisting of the following modules:
\paragraph{Input mixing module}
Here, non-linear combinations of the input PSG data $\mathbf{x}$ are made using a non-linear mixing block $\phi_{\mathrm{mix}} : \mathbb{R}^{1 \times C \times T} \to \mathbb{R}^{C \times 1 \times T}$.
\paragraph{Feature extraction module}
This module contains two components.
The first is a convolutional feature extraction block $\varphi_{\mathrm{conv}} : \mathbb{R}^{C \times 1 \times T} \to \mathbb{R}^{f^{\prime} \times 1 \times T^{\prime}}$ consisting of $k_{\mathrm{max}}$ successions of convolutional, batch normalization, and rectified linear unit (ReLU) layers.
Second is a recurrent feature extraction block $\varphi_{\mathrm{rec}} : \mathbb{R}^{f^{\prime} \times 1 \times T^{\prime}} \to \mathbb{R}^{f^{\prime} \times 2 \times T^{\prime}}$ with $f^{\prime}=f_02^{k_{\mathrm{max}}}$ hidden units.
The $\varphi_{\mathrm{conv}}$ block is responsible for bulk feature extraction and temporal decimation using strided convolutions, while $\varphi_{\mathrm{rec}}$ processes the raw features across the reduced temporal dimension using a bidirectional gated recurrent unit~\cite{Cho2014} with $f^{\prime}$ hidden units.
\paragraph{Event detection module}
The output from $\varphi_{\mathrm{rec}}$ is processed by two separate blocks: $ \psi_{\mathrm{clf}} : \mathbb{R}^{f^{\prime} \times 2 \times T^{\prime}} \to \mathbb{R}^{\left( K + 1 \right) \! N_d \times 1 \times T^{\prime}} $ outputs the tensor $\mathbf{p}$ containing predicted arousal probabilities for each time point $t \in \llbracket T^{\prime} \rrbracket$ for each default event window.
$ \psi_{\mathrm{loc}} : \mathbb{R}^{f^{\prime} \times 2 \times T^{\prime}} \to \mathbb{R}^{2N_d \times T^{\prime}} $ outputs the tensor $\mathbf{y}$ containing predicted start time and durations of arousal events.
Both $\psi_{\mathrm{clf}}$ and $\psi_{\mathrm{loc}}$ are implemented using $\left(2, 1\right)$ convolutions rather than convolutions over the entire volume as in~\cite{Chambon2018b, Chambon2019, Olesen2019}.
This serves a dual purpose: the first is to reduce the number of parameters to make the network more memory-efficient, while the second purpose is to allow the kernel and feature maps to be temporally invariant.

For a detailed description of the network architecture, see~\cref{tab:architecture}.

\subsection{Loss objective}
The network parameters were optimized according to a three-component loss objective comprising a localization loss \( \ell_{\mathrm{loc}} \) and a positive and negative classification loss $\ell_{+}$ and $\ell_{-}$, respectively, such that
\begin{equation}
    \ell = \ell_{\mathrm{loc}} + \ell_{+} + \ell_{-}.\label{eq:loss}
\end{equation}
The localization loss was calculated using a Huber function
\begin{equation}
    \ell_{\mathrm{loc}} = \frac{1}{N_+} \sum_{i \in \pi_+}\!{h^{(i)}} \\

\end{equation}
\begin{equation}
    \mathbf{h} =
    \begin{cases}
        0.5 \! \left( \mathbf{y} - \mathbf{t} \right)^2, & \text{if } \lvert \mathbf{y} - \mathbf{t} \rvert < 1, \\
        \lvert \mathbf{y} - \mathbf{t} \rvert - 0.5, & \text{otherwise,}
    \end{cases}
\end{equation}
where $i \in \pi_+$ indicates event windows with a non-empty arousal target.
Contributions from the positive/negative classification losses were calculated using a focal loss function~\cite{Lin2020}:
\begin{align}
    \ell_{+} &= \frac{1}{N_+} \sum_{i \in \pi_+}\!{-\alpha \parentheses*{1 - p^{(i)}}^{\gamma} \func{\log}{p^{(i)}}}, \, \text{and} \\
    \ell_{-} &= \frac{1}{N_{-}} \sum_{i \in \pi_{-}}\!{-\alpha \parentheses*{1 - p^{(i)}}^{\gamma} \func{\log}{p^{(i)}}},
\end{align}
where $\alpha=0.25$ and $\gamma=2$, \(i \in \pi_{-}\) indicates event windows with empty arousal targets, and \( p^{(i)} \in \mathbf{p} \) is the predicted class probabilities for event window \textit{i}.
This serves to counter the class imbalance in a single data segment, which typically consists of many event windows with few positive examples.

\subsection{Experimental setups}
We investigated the channel mismatch problem with the following four experimental setups:
\paragraph{Full montage baseline (FM)}
In this experiment, we trained the event detection algorithm on $\textsc{train}_1$ using \(C=5\) channels: left/right central EEG, left/right EOG, and chin EMG.
Convergence and the optimal detection threshold were assessed on $\textsc{eval}_1$ and performance was evaluated on \textsc{test}\textsubscript{2}.
The optimal baseline model was used as an initialization for the two transfer learning experiments described below.
\paragraph{Pretraining (PT)}
The optimal model $\mathcal{M}_{\mathrm{FM}}^{*}$ was used in this experiment as an initialization for $\mathcal{M}_{\mathrm{PT}}$.
We adjusted the mixing module and first convolutional layer in the feature extraction module to account for the channel mismatch by replacing the convolutional and batch normalization layers, and subsequently trained these from scratch.
The rest of the weights and bias terms were frozen to the optimized values from $\mathcal{M}_{\mathrm{FM}}^{*}$.
The network was trained on $\textsc{train}_2$ with only $C=1$ channels (left central EEG, C3).
Convergence and optimal detection threshold were assessed on $\textsc{eval}_2$, while final performance was evaluated on \textsc{test}\textsubscript{2}.
\paragraph{Fine-tuning (FT)}
Similar to PT, the optimal model $\mathcal{M}_{\mathrm{FM}}^{*}$ was used in this experiment as an initialization for $\mathcal{M}_{\mathrm{FT}}$.
Also, the mixing module and first convolutional layer in the feature extraction module were likewise adjusted.
However, all other layers in $\mathcal{M}_{\mathrm{FT}}$ were permitted to be further optimized by fine-tuning weights and bias terms during training.
The model was trained using the same 400 PSGs from $\textsc{train}_2$ with the same $C=1$ channel configuration as in PT.
\paragraph{Single EEG benchmark (SE)}
We benchmarked our two transfer learning experiments to a comparable situation in which an event detection model was trained on the same PSGs in $\textsc{train}_2$ using only the left central EEG (C3).

In all experimental runs, we optimized the loss objective in~\cref{eq:loss} using the Adam optimization algorithm with a learning rate of \(\alpha=10^{-3}\) and the default parameter values \( \left( \beta_1, \beta_2 \right) = \left( 0.9, 0.999 \right) \) as suggested in~\cite{Kingma2015}.
We applied the same data sampling strategy as proposed in~\cite{Olesen2019}, in which a segment of data is sampled such that it contains at least 50\% of a randomly sampled event across all PSGs.
We used a default event window size of \SI{15}{\second} with \SI{50}{\percent} overlap as this was found previously to work well for arousal detection~\cite{Olesen2019}.
Convergence was assessed for all experimental runs using the loss on the corresponding validation datasets.

All experiments were implemented in PyTorch 1.2~\cite{Paszke2019}.

\subsection{Performance evaluation}
Bipartite matching were used to match detected and true events during training and testing.
At test time, detected events were subjected to non-maximum suppression based on an intersection-over-union (IOU) of at least 0.5 between detected and true events.
We evaluated the performance of our experimental setups using precision, recall and F1 scores.

\subsection{Statistical analysis}
We used Kruskal–Wallis one-way analysis of variance tests for differences in performance metrics between groups (SE, FT and PT) with a significance level of $\alpha=0.05$.
Post-hoc testing was performed with Mann-Whitney U-tests for each pair-combination (SE/FT, SE/PT, and FT/PT) likewise with $\alpha=0.05$.
We accounted for multiple comparisons by adjusting \textit{p}-values with Bonferroni corrections.

\section{RESULTS AND DISCUSSION}

\begin{figure}[tb]
    \centering
    \includegraphics[width=\columnwidth]{figures/barplot.pdf}
    \caption{Performance metrics as evaluated on \textsc{test}\textsubscript{2} for each experimental setup. Bars indicate mean performance across PSGs with 95\% confidence interval error bars. Note the $y$-axis scaling. SE: single-EEG. FT: fine-tuning. PT: pre-training. FM: full montage. ns: not significant, $^{**}$: $p_{\mathrm{adj}} < 10^{-2}$; $^{****}$: $p_{\mathrm{adj}} < 10^{-4}$.}
    \label{fig:results}
\end{figure}

\begin{table}[tb]
    \begin{threeparttable}
        \centering
        \footnotesize
        \caption{Performance metrics across experiments.}
        \label{tab:results}
        \begin{tabular}{@{}lccc@{}}
        \toprule
        \textbf{Experiment} &        \textbf{Precision} &           \textbf{Recall} &               \textbf{F1} \\ \midrule
        FM &  $0.739 \pm 0.122$ &  $0.675 \pm 0.139$ &  $0.694 \pm 0.115$ \\
        SE &  $0.723 \pm 0.124$ &  $0.624 \pm 0.137$ &  $0.659 \pm 0.117$ \\ \midrule
        FT &  $\mathbf{0.710 \pm 0.128}$ &  $\mathbf{0.676 \pm 0.130}$ & $\mathbf{0.682 \pm 0.110}$ \\
        PT &  $0.699 \pm 0.141$ &  $0.619 \pm 0.153$ &  $0.642 \pm 0.129$ \\
        \bottomrule
        \end{tabular}
        \begin{tablenotes}
            \small
            \item Metrics are shown as mean $\pm$ standard deviation when evaluated on each PSG in \textsc{test}\textsubscript{2}. Best performing transfer learning experiment is shown in bold. SE: single-EEG. FT: fine-tuning. PT: pre-training. FM: full montage.
        \end{tablenotes}
    \end{threeparttable}
\end{table}

We show the results of the transfer learning experiments (FT, PT) as well as the baseline and benchmark experiments (FM, SE) in~\cref{fig:results} and~\cref{tab:results}.
Performance metrics were not calculated for \num{10} subjects in \textsc{test}\textsubscript{2}, as these did not have any scored arousals and are thus not reflected in~\cref{fig:results} and~\cref{tab:results}.

The baseline F1 performance is shown to be slightly lower than previously reported ($0.694 \pm 0.115$ vs. $0.749 \pm 0.105$)~\cite{Olesen2019}.
However, our baseline model was trained on \num{400} subjects compared to \num{1485} in~\cite{Olesen2019}, which would account for the lower F1 score.
By reducing the available input channels from $C=5$ different modalities to $C=1$ EEG channels as in the SE benchmark experiment, the F1 score drops to $0.659 \pm 0.117$, while the precision and recall scores likewise drop from $0.739 \pm 0.122$ to $0.723 \pm 0.124$, and $0.675 \pm 0.139$ to $0.624 \pm 0.137$, respectively.

We found statistically significant differences in F1 scores between SE, FT, and PT ($p=3.189\times 10^{-7}$).
Post-hoc testing further revealed statistically significant differences between SE and FT ($p_{\mathrm{adj}}=2.224 \times 10^{-3}$), and FT and PT ($p_{\mathrm{adj}}=2.685 \times 10^{-7}$), but not between SE and PT  ($p_{\mathrm{adj}}=0.080$).
We also found that recall scores differed between experimental setups ($p=7.085 \times 10^{-13}$).
Post-hoc testing showed statistically significant differences between SE, FT ($p_{\mathrm{adj}}=5.180 \times 10^{-11}$), and FT and PT ($p_{\mathrm{adj}}=1.440 \times 10^{-9}$), but not between SE and PT ($p_{\mathrm{adj}}=1.000$).
Lastly, we saw statistically significant differences in precision scores between experimental setups ($p=0.033$), subsequent post-hoc testing did not reveal any statistical significant differences, when adjusting for multiple comparisons using the Bonferroni procedure (SE/FT, $p_{\mathrm{adj}}=0.214$; FT/PT, $p_{\mathrm{adj}}=1.000$; SE/PT, $p_{\mathrm{adj}}=0.037$).

Our results show, that for some scenarios, we can learn information present in multi-variate PSG data and effectively transfer that information to a target domain containing only a single EEG channel.
Specifically, the performance of our fine-tuning strategy is high enough that the mean F1 scores across subjects are statistically insignificant, when comparing FT and FM setups (not shown).

Previous related work focused on the channel mismatch problem, when comparing different, but the same number of, channel modalities such as transferring EEG-based models to EOG-based target domains, and thus did not investigate how changing the model architecture might impact performance~\cite{Phan2019, Phan2019c}.
In this work, we investigated transfer learning when the source and target domains only overlap by one input channel.
This necessitates changing some parts of the underlying model architecture to accommodate the different number of input channels, and these changes might impact downstream feature extraction.
We did not explore simply zeroing out a large number of input channels in this work, as this requires exhaustive search of which channel indices to zero out in the model based on the number of target input channels.
Our strategy does not require this exhaustive search.

Our study applied a simple optimization strategy for the transfer learning experiments, which might limit the potential performance gain.
This is especially relevant for the FT experiment.
For example, one could experiment with different learning rates and scheduling schemes for the initial layers and pre-trained layers, such that the initial layers were trained with a higher relative learning rate to compensate for their lack of initial training.

Furthermore, we explored transfer learning for the channel mismatch problem in a single cohort of patient recordings.
Future directions of this research will investigate scenarios, where both the source and target domains, and the datasets are different.

\section{CONCLUSIONS}
We show in our experiments that a simple fine-tuning strategy can be employed to transfer learning from a model based on multi-variate PSG data to a configuration where only a single EEG lead is available for detecting arousals, and that the difference between single-EEG and multivariate PSG performance is negligible.
Future work will explore the effects of various combinations of datasets on the impact of generalized event detection, when the source and target domains do not overlap completely.

\addtolength{\textheight}{-5.5cm}

\section*{ACKNOWLEDGMENTS}

Some of the computing for this project was performed on the Sherlock cluster.
We would like to thank Stanford University and the Stanford Research Computing Center for providing computational resources and support that contributed to these research results.

We would also like to thank the National Sleep Research Resource team for their efforts in collecting, organizing and making available the PSG data used in this study.

\section*{REFERENCES}
\printbibliography[heading=none]

\end{document}

redpajama_set_name: RedPajamaArXiv


text:
Oct 19, 2017 NASHVILLE, Tenn.
Bridgestone Americas Wins Nashville Downtown Partnership's 2017 INVEST Award
Bridgestone Tower Recognized for Impact on Growing Downtown Community and Economy
The Nashville Downtown Partnership honored Bridgestone Americas with its 2017 INVEST award for the addition of Bridgestone Tower to the vibrant and dynamic downtown community. Bridgestone was presented the award at the Nashville Downtown Partnership's 2017 Annual Meeting and Awards Luncheon on October 18.
"We are honored to receive the INVEST award and proud to call downtown Nashville home," said Gordon Knapp, CEO and President of Bridgestone Americas. "Organizations like the Nashville Downtown Partnership are having a resounding impact on our city. We look forward to working with all of the downtown businesses and organizations in the years to come to make sure this neighborhood only gets stronger."
Home to more than 1,700 teammates and rising 30 stories, Bridgestone Tower is among the most significant corporate headquarters to open in the history of Nashville's urban core. The Tower is home to Bridgestone Americas corporate staff and six core business units: Bridgestone Americas Tire Operations; Bridgestone Retail Operations; Firestone Building Products; Firestone Fibers and Textiles; Firestone Natural Rubber; and Firestone Industrial Products. The Nashville Downtown Partnership recognized the Tower in part for the projected economic impact it will have on the city.
Starting in 2003, the Nashville Downtown Partnership has honored over 150 individuals, companies and organizations for outstanding achievements in the LIVE, WORK, PLAY and INVEST categories. Award winners recognized at its annual awards luncheon have all made specific and significant contributions in downtown Nashville.
The Nashville Downtown Partnership is a private sector nonprofit corporation whose core purpose is "to make Downtown Nashville the compelling urban center in the Southeast in which to LIVE, WORK, PLAY and INVEST." Organized in 1994, the Partnership has transitioned into a downtown leadership organization that focuses on business recruitment and retention, residential and retail development, public space management, access and transportation, communications and marketing.
About Bridgestone Americas, Inc.:
Nashville, Tennessee-based Bridgestone Americas, Inc. (BSAM) is the U.S. subsidiary of Bridgestone Corporation, the world's largest tire and rubber company. BSAM and its subsidiaries develop, manufacture and market a wide range of Bridgestone, Firestone and associate brand tires to address the needs of a broad range of customers, including consumers, automotive and commercial vehicle original equipment manufacturers, and those in the agricultural, forestry and mining industries. The companies are also engaged in retreading operations throughout the Western Hemisphere and produce air springs, roofing materials and industrial fibers and textiles. The BSAM family of companies also operates the world's largest chain of automotive tire and service centers. Guided by its One Team, One Planet message, the company is dedicated to achieving a positive environmental impact in all of the communities it calls home. For more information about Bridgestone, visit BridgestoneAmericas.com.
Bridgestone Retail Operations Shares Five Tips to Save Money on Gas this Summer
McCarthy Tire Service to Purchase GCR Tires & Service Locations in North Carolina, South Carolina and Virginia
Bridgestone Group's Sustainability Report Details Environmental Success and Reports on Strengthened Sustainable Procurement Practices, Including Human Rights Protection


Toprock, footwork and powermoves. Doesn't sound like your typical 4th and 5th grade classroom, but for Highland Elementary that's exactly what they were served when Teaching Artist MN Joe Tran came to visit! For most students, breakdancing was unfamiliar territory. Though inexperienced, they were eager to start learning the elements of breaking.
MN Joe has been breakdancing for 17 years now and is also a member of BRKFST Dance Company. Even throughout his extensive experience he still remains excited to teach new learners.


When it arrives to currently being profitable in the home dependent organization industry it normally takes a whole lot of tough work and devotion. This is something that many individuals know about already but nevertheless are not inclined to put in the work. But also most people neglect the relevance that little enterprise investments have on the accomplishment that you accomplish in your enterprise. This report will illustrate the value and how you are in a position to know which investments will be very good for your business.
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text:
Q: Can a Comparator for a sorted list in a GWT app be parameterized? I'm working on a GWT (Google Web Toolkit) project.  I am building a "form" that will have a series of ListBoxes.  Each ListBox corresponds to a different JSONArray that holds JSONObjects.  I want each ListBox to hold a sorted set of the key/value pairs in the JSONObjects.  I am repeating the same code over and over for each ListBox.  My question is can I parameterize this process?
The answer seems to largely be "yes" until I get to the Comparator for each JSONObject.  The Comparator does not take a parameter and I don't know enough about Java to know how to work around this.  Is it possible?
private FlowPanel createWebUsersList(JSONArray Info, String listName) {
    FlowPanel panel = new FlowPanel();
    final ListBox listBox = new ListBox();

    panel.add(new HTML("
Limit by " + listName + "
"));

    listBox.setStyleName("LimitBy");
    listBox.setName("limitBy" + listName);
    listBox.setVisibleItemCount(20);
    panel.add(listBox);

    if (Info.size() > 0) {
        SortedSet<JSONObject> sortedList = Collections
                .synchronizedSortedSet(new TreeSet<JSONObject>(
                        new JSONObjectComparator(listName)));
        for (int i = 0; i < Info.size(); i++) {
            sortedList.add(Info.get(i).isObject());
        }

        while (sortedList.iterator().hasNext()) {
            JSONObject item = sortedList.iterator().next();
            listBox.addItem(
                    item.get("UserName").isString().stringValue(),
                    Integer.toString((int) item.get("ID").isNumber()
                            .doubleValue()));
        }
    } else {
        Misc.displayError(logger,
                "selectEMSEventsBy.addChangeHandler.onChange: empty WebUsers");
    }

    return panel;
}

class JSONObjectComparator implements Comparator<JSONObject> {

    @Override
    public int compare(JSONObject o1, JSONObject o2) {
        return o1
                .get("UserName")
                .isString()
                .stringValue()
                .compareToIgnoreCase(
                        o2.get("UserName").isString().stringValue());
    }
}

A: I think this is what you had in mind. The constructor takes a String argument containing the name of the field to be compared.
class JSONObjectComparator implements Comparator<JSONObject> {
    private String fieldToCompare = null;
    public JSONObjectComparator(String fieldToCompare) {
        this.fieldToCompare = fieldToCompare;
    }
    @Override
    public int compare(JSONObject o1, JSONObject o2) {
        return o1
                .get(fieldToCompare)
                .isString()
                .stringValue()
                .compareToIgnoreCase(
                        o2.get(fieldToCompare).isString().stringValue());
    }
}

Note that you still have NPE problems if the field is not a String (isString() will return null).  But that's a separate issue you can fix yourself.


text:
American Honda Recalls 2022 All-Terrain Vehicles (ATVs) Due to Crash and Injury Hazards (Recall Alert)
Share it on Facebook
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Recalled 2022 model year Honda TRX90X ATV
Name of Product:
2022 model year Honda TRX90X ATVs
Hazard:
The vehicle's throttle cable can get stuck, posing crash and injury hazards.
Recall Date:
About 2,900
Consumer Contact
American Honda toll-free at 866-784-1870 from 8:30 a.m. to 4:30 p.m. PT, Monday through Friday or online at https://powersports.honda.com/contact-us or at https://powersports.honda.com by clicking on "Recall Info" at the top of the page or at https://powersports.honda.com/recalls
This recall involves model year 2022 Honda ATVs with model number TRX90X. The ATVs were sold in white and red. "HONDA" is printed on the front, sides, and rear of each vehicle. "TRX90X" is printed on a label located on both sides of the vehicle. The Vehicle Identification Number (VIN) is stamped on the front of the frame. The VIN range for the recalled vehicles are 1HFTE5505N4****** through 1HFTE5502N4******.
Consumers should immediately stop using the recalled all-terrain vehicles (ATVs) and contact an authorized Honda Powersports dealer to schedule an appointment for a free inspection and repair. Honda is contacting all known purchasers directly.
The firm has received one report of the throttle cable getting stuck resulting in a crash and minor injuries.
Sold At:
Authorized Honda Powersports dealers nationwide from November 2021 through September 2022 for about $3,250.
Manufacturer(s):
American Honda Motor Co. Inc., Torrance, California
Recall number:
Fast Track Recall
Choose Your Recall Emails Get Notified About Each Recall via Email.
Related Recalls
Sakar International Recalls Multi-Purpose Helmets Due to Risk of Head Injury
The recalled helmets do not comply with the positional stability and retention system requirements of the U.S. CPSC federal safety standard for bicycle helmets. The helmets can fail to protect in the event of a crash, posing a risk of head injury.
American Honda Expands Recall of Honda Talon 1000 ROVs Due to Crash and Injury Hazards
The vehicle's intake funnel band screw can loosen and enter the engine through the intake funnel and cause sudden engine failure. If this occurs, it can result in a loss of control, increasing the risk of a crash or injury.
Polaris Recalls Sportsman and Scrambler 1000 S All-Terrain Vehicles Due to Fire Hazard (Recall Alert)
Electrostatic discharge outside the fuel tank can ignite fuel spills while refueling, posing a fire hazard.
Quality Bicycle Products Recalls Carbon Handlebars and Bicycles Due to Injury Hazard
The recalled handlebars can crack where the brake/shift levers are installed and cause a crash, posing an injury hazard.
Surf 9 Recalls Body Glove Tandem and ULI Inflatable Paddle Boards Due to Drowning Hazard; Sold Exclusively at Costco
The glue on the inflatable paddle boards can separate at the seams and the paddle boards can deflate unexpectedly, posing a drowning hazard.
Yamaha Recalls Viking Off-Road Side-by-Side Vehicles Due to Crash and Injury Hazards (Recall Alert)
Improper installation of the retaining bolts could result in the parking brake mechanism failing to hold the vehicle when parked allowing the vehicle to move unintentionally, posing crash and injury hazards.
Search Product Safety Reports
About Government Recalls
Recalls from around the world
About the U.S. CPSC
The U.S. Consumer Product Safety Commission (CPSC) is charged with protecting the public from unreasonable risk of injury or death associated with the use of thousands of types of consumer products. Deaths, injuries, and property damage from consumer product-related incidents cost the nation more than $1 trillion annually. CPSC's work to ensure the safety of consumer products has contributed to a decline in the rate of injuries associated with consumer products over the past 50 years.
Federal law prohibits any person from selling products subject to a Commission ordered recall or a voluntary recall undertaken in consultation with the CPSC.
For lifesaving information:
Visit CPSC.gov.
Sign up to receive our e-mail alerts.
Follow us on Facebook, Instagram @USCPSC and Twitter @USCPSC.
Report a dangerous product or a product-related injury on www.SaferProducts.gov.
Call CPSC's Hotline at 800-638-2772 (TTY 301-595-7054).
Contact a media specialist.


text:
U teoriji verovatnoće i statistici, binomna raspodela sa parametrima -{n}- i -{p}- je diskretna raspodela verovatnoće broja uspeha u sekvenci od -{n}- nezavisnih eksperimenata, svaki od kojih daje odgovor na da-ne pitanje, i svaki ima svoj bulov rezultat - uspeh/da/tačno/jedan (sa verovatnoćoḿ -{p}-) ili neuspeh/ne/lažno/nula (sa verovatnoćom q = 1 − -{p}-). Pojedinačni uspeh/neuspeh eksperimenta se takođe naziva Bernulijev pokušaj ili Bernulijev eksperiment, a sekvenca ishoda se naziva Bernulijev proces; za pojedinačni pokušaj, i.e., -{n}- = 1, binomna distribucija je Bernulijeva raspodela. Binomna distribucija je osnova za popularni binomni test statističkog značaja.

Binomna distribucija se često koristi za modelovanje broja uspeha u uzorku veličine -{n}- koji je izvučen sa zamenom iz populacije veličine -{N}-. Ako se uzorkovanje vrši bez zamene, izvlačenja nisu nezavisna, pa je rezultirajuća raspodela hipergeometrijska, a ne binomna. Međutim, za -{N}- mnogo veće od -{n}-, binomna distribucija ostaje dobra aproksimacija i široko se koristi.

Specifikacija

Funkcija verovatnoće

Generalno, ako randomna promenljiva X sledi binomnu distribuciju sa parametrima -{n}- ∈ -{ℕ}- i -{p}- ∈ [0,1], piše se X ~ -{B(n, p)}-. Verovatnoća da se dobije tačno -{k}- uspeha u -{n}- pokušaja je data funkcijom verovatnoće:

za -{k}- = 0, 1, 2, ..., n, gde je

binomni koeficijent, po kome je raspodela dobila ime. Formula se može razumeti na sledeći način. -{k}- uspeha se javlja sa verovatnoćom -{pk}- i -{n − k}- neuspeha se javlja sa verovatnoćom -{(1 − p)n − k}-. Međutim, -{k}- uspeha se može javiti bilo gde među -{n}- pokušaja, i postoji  različitih načina raspodeljivanja -{k}- uspeha u nizu od -{n}- pokušaja.

Pri stvaranju referentnih tabela za verovatnoću binomne distribucije, obično se tabela popunjava do -{n}-/2 vrednosti. To je zato što se za -{k > n/2}-, verovatnoća može izračunati njenim komplementom kao

Gledajući izraz -{f(k, n, p)}- kao funkciju od -{k}-, postoji -{k}- vrednosti koje je maksimiziraju. Stoga se -{k}- vrednost može naći izračunavajući

i upoređujući tu vrednost sa 1. Uvek postoji ceo broj M koji zadovoljava

-{f(k, n, p)}- je monotono rastući za -{k < M}- i monotono opadajući za -{k > M}-, uz izuzetak slučaja gde je -{(n + 1)p}- ceo broj. U tom slučaju, postoje dve vrednosti za koje je -{f}- maksimalno: -{(n + 1)p}- i -{(n + 1)p − 1}-. -{M}- je najverovatniji ishod (mada još uvek može da bude sveukupno malo verovatan) Bernulijevih pokušaja i naziva se modus.

Funkcija kumulativne verovatnoće

Funkcija kumulativne verovatnoće se može izraziti kao:

gde je  "pod" ispod -{k}-, i.e. najveći ceo broj manji od ili jedna sa -{k}-.

On se može predstaviti u vidu regulisane nekompletne beta funkcije, na sledeći način:

Neki granični slučajevi zatvorenog oblika za funkciju kumulativne distribucije dati su u nastavku.

Primer

Pretpostavka je da se pristranim bacanjem novčića dobija glava sa verovatnoćom 0,3. Pitanje je: koja je verovatnoća postizanja 0, 1, ..., 6 glava posle šest bacanja?

Očekivanje

Ako je -{X ~ B(n, p)}-, drugim rečima, X je binomno distribuirana randomna promenljiva, pri čemu je -{n}- ukupan broj eksperimenata, a -{p}- je verovatnoća svakog eksperimenta da proizvede uspešan rezultat, onda je očekivana vrednost X:

Na primer, ako je -{n}- = 100, i -{p}- = 1/4, onda je prosečan broj uspešnih rezultata 25.

Proof: Srednja vrednost, μ, se direktno izračunava po definiciji

i binomnoj teoremi:

Srednja vrednost se može izvesti iz jednačine  gde su sve randomne promenljive  obuhvaćene Bernulijevom raspodelom sa  ( ako -{i}--ti eksperiment uspe, dok je inače ). Dobija se:

Varijansa

Varijansa je:

Dokaz: Neka je  gde su sve  nezavisne randomne promenljive Bernulijeve raspodele. Kako je , dobija se:

Reference

Literatura

Spoljašnje veze

 -{Interactive graphic: Univariate Distribution Relationships}-
 -{Binomial distribution formula calculator}-
 -{Difference of two binomial variables: X-Y}-
 -{Querying the binomial probability distribution in WolframAlpha}-

Дискретне расподеле
Фамилија експоненцијалних расподела

redpajama_set_name: RedPajamaWikipedia


text:
Q: How can I write a unit test for a react component that calls reduxjs's mapStateToProps? I'm trying to write unit tests for a container component called AsyncApp but I get the following error "mapStateToProps must return an object. Instead received undefined."
This is my set-up.
Root.js
import configureStore from '../configureStore';
import React, { Component } from 'react';
import { Provider } from 'react-redux';
import AsyncApp from './AsyncApp';

const store = configureStore();

export default class Root extends Component {
  render() {
    return (
      <Provider store={store}>
        <AsyncApp />
      </Provider>
    );
  }
}

configureStore.js
import { createStore, applyMiddleware } from 'redux';
import thunkMiddleware from 'redux-thunk';
import createLogger from 'redux-logger';
import rootReducer from './reducers';

const loggerMiddleware = createLogger();

const createStoreWithMiddleware = applyMiddleware(
  thunkMiddleware
  //loggerMiddleware
)(createStore);

export default function configureStore(initialState) {
  return createStoreWithMiddleware(rootReducer, initialState);
}

AsyncApp.js
import React, { Component, PropTypes } from 'react';
import { connect } from 'react-redux';
import { foo } from '../actions';
import FooComponent from '../components/FooComponent';

class AsyncApp extends Component {
  constructor(props) {
    super(props);
    this.onFoo= this.onFoo.bind(this);
    this.state = {}; // <--- adding this doesn't fix the issue
  }

  onFoo(count) {
    this.props.dispatch(foo(count));
  }

  render () {
    const {total} = this.props;

    return (
      <div>
        <FooComponent onFoo={this.onFoo} total={total}/>
      </div>
    );
  }
}

function mapStateToProps(state) {
  return state;
}

export default connect(mapStateToProps)(AsyncApp);

I'm passing store directly to AsyncApp in my test to avoid getting the following Runtime Error : Could not find "store" in either the context or props of "Connect(AsyncApp)". Either wrap the root component in a <Provider>, or explicitly pass "store" as a prop to "Connect(AsyncApp)".
The test isn't complete yet because I can't get past the mapStateToProps error message.
AsyncApp-test.js
jest.dontMock('../../containers/AsyncApp');
jest.dontMock('redux');
jest.dontMock('react-redux');
jest.dontMock('redux-thunk');
jest.dontMock('../../configureStore');

import React from 'react';
import ReactDOM from 'react-dom';
import TestUtils from 'react-addons-test-utils';
const configureStore = require( '../../configureStore');
const AsyncApp = require('../../containers/AsyncApp');

const store = configureStore();

//const asyncApp = TestUtils.renderIntoDocument(
  //<AsyncApp store={store} />
//);

const shallowRenderer = TestUtils.createRenderer();
shallowRenderer.render(<AsyncApp store={store}/>);

I want to eventually test that AsyncApp contains a FooComponent, and that a foo action is dispatched when onFoo is called.
Is what I am trying to do achievable? Am I going about this the right way?

A: The suggestion I've seen in a few places is to test the non-connected component, as opposed to the connected version.  So, verify that when you pass in specific props to your component you get the expected rendered output, and verify that when you pass in a state with a certain shape your mapStateToProps() returns the expected pieces.  Then you can expect that they should both work correctly when put together.


March 21 is the World Down Syndrome Day. One way to celebrate the day is by wearing different coloured socks, to honour differences and support equal values and rights of all people.
Let's talk about Down Syndrome! The WDSD is celebrated March 21 because Down's syndrome means having three copies of chromosome No. 21. The chromosomes look like small crosses, just like two socks that pair with the heels against each other.
By wearing #LotsOfSocks we at Etac will partake in the celebrations and ask all our employees to do the same. Together we will give the day a splash of colour!
To wear lots of socks is easy. All you have to do is wear two different socks on March 21. That way, you too can participate in acknowledging inequalities and highlight all people's equal value.


We want you to know that TBA Group respects the information we hold on you and that we take the security of your information very seriously.
We have recently published a new Privacy Notice to give you more information on the data we hold on you, what we do with that data, who we share your data with and your new rights under GDPR.
Alternatively we can send you a copy if you ask us too.


text:
Q: How to find the word before a match I have the following log file. I need to know if there are any fault / suspect condition using a shell script.
I need to find fault and check the previous word, if it is greater than 0 then there is a work for the DBA.
Checking pubs2: Logical pagesize is 4096 bytes
DBCC CHECKSTORAGE for database 'pubs2' sequence 17 completed at Oct 21 2015  3:17PM. 4 faults and 0 suspect conditions were located. 0 checks were aborted. You should investigate the recorded faults, and plan a course of action that will correct them.

I already tried the following commands in Linux/Bash shell and it is working well.
FLTCNT=`cat $MAILLOG | grep -oP '\S+(?=\s+faults and)'`
SPTCNT=`cat $MAILLOG | grep -oP '\S+(?=\s+suspect)'`

if [ $FLTCNT -gt 0 ] || [ $SPTCNT -gt 0 ] ; then
    FAILED="Y"
#   echo "Fault / suspect conditions found"
    cat $MAILLOG >> $ERRLOG
fi

But when I execute the same in an AIX server I am getting error
grep: illegal option -- o
grep: illegal option -- P
usage: grep [-r] [-R] [-H] [-L] [-E|-F] [-c|-l|-q] [-insvxbhwyu] [-p[parasep]] -e pattern_list...
    [-f pattern_file...] [file...]
usage: grep [-E|-F] [-c|-l|-q] [-insvxbhwyu] [-p[parasep]] [-e pattern_list...]
    -f pattern_file... [file...]

A: Assuming you want to do something if any of the two strings X faults and Y suspect for any positive integer values X and Y not equal to 0 exists in the file $MAILLOG.
if grep -qwE '([1-9]|[0-9]{2,}) (faults|suspect)' "$MAILLOG"; then
    # do something
fi

The pattern ([1-9]|[0-9]{2,}) would match either a single digit greater than zero, or any number with two or more digits.
The pattern (faults|suspect) would match either the string faults or suspect.  Would you want to include checks there too, just use (faults|suspect|checks).
The -q option to grep turns off any output non-error output that the utility would otherwise produce (we're only interested in the exit status of grep, i.e. whether it was able to match the pattern or not).
The -w option make grep perform a "word search".  In this case, it means that it will find 10 faults rather than the substring 0 faults as the zero in 10 does not start a new "word", but the 1 does. It also means that the string 2 faultsmen (however unlikely that string is) would not trigger a match.
The -E is needed to support extended regular expressions using alternation (|).

A: You could try using grep:
grep -c "your word to match" /log/file

Eg:
$ grep -c "upgraded" /var/log/pacman.log
244


text:
<?xml version="1.0" encoding="utf-8"?>
<resources>
    <string name="app_name">Keyboard Tracker</string>
    <string name="is_keyboard_open">Is the keyboard open?</string>
    <string name="keyboard_closed">Keyboard is closed</string>
    <string name="keyboard_open">Keyboard is open</string>
</resources>

redpajama_set_name: RedPajamaGithub


text:
Q: Test multiple axios calls inside react component using jest and testing-library There are multiple axios call in my react component. I am trying to test responses wrt URL. But how to check on URL's.
Below is the code:
import mockAxios from "jest-mock-axios";
// other test cases inside describe
mockAxios.get.mockImplementation((url) => {
  console.log("url", url);
  switch (url) {
    case "//companies/123/activity":
      return Promise.resolve({ data: [] });
    case "//activity/events":
      return Promise.resolve({ data: [] });
    case "//companies/123/activity/users":
      return Promise.resolve({ data: [] });
    default:
      return Promise.reject(new Error("not found"));
  }
});

But not getting url, the arg is empty.


text:
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Jamaican Man Who Supplied Flight Attendant with 60 Pounds of Cocaine to Smuggle at LAX Sentenced to Nearly 14 Years in Prison
By DOJ Press
FILE PHOTO: Signage is seen at the United States Department of Justice headquarters in Washington, D.C.
LOS ANGELES – A Jamaican national was sentenced today to 165 months in federal prison for supplying a JetBlue flight attendant with nearly 60 pounds of cocaine that she attempted to smuggle onboard a flight at Los Angeles International Airport.
Gaston Brown, 42, of Clarendon, Jamaica, was sentenced by United States District Judge Christina A. Snyder.
A federal grand jury indicted Brown in January 2017, charging him with two counts of conspiracy to possess and distribute cocaine, one count of unlawful use of a means of identification, one count of use of a counterfeit access device, and one count of aggravated identity theft.
At the conclusion of a four-day trial in February 2018, a jury found Brown guilty of all charges.
On six occasions between October 2015 and March 2016, Brown paid JetBlue flight attendant Marsha Gay Reynolds, 35, of Jamaica, New York, to transport cocaine and drug money across the country. Brown paid her to carry the drugs and cash in suitcases through "known crewmember" checkpoints at LAX and New York's John F. Kennedy International Airport. As a known crewmember, Reynolds was subjected to much lighter screening at airport security checkpoints and would be able to transport the cash and cocaine without being stopped.
To evade detection by law enforcement, Brown, an illegal immigrant and convicted felon, used identities he had stolen from two mentally disabled men so he could meet Reynolds in the "sterile" area of the airport, past security. While in the airport's sterile area, he retook possession of the drug- and cash-laden suitcases, and traveled across the country with them, undetected.
On March 18, 2016, Brown supplied approximately 27 kilograms (59.5 pounds) of cocaine to Reynolds. She then attempted to board a JetBlue flight in Terminal 4 at LAX. After showing her official badge and identification to the Transport Security Administration officer on duty at the known crewmember checkpoint, Reynolds was randomly selected for additional screening. Reynolds was then escorted to a secondary screening area. Upon arriving at this checkpoint, Reynolds dropped her luggage, removed her shoes, and fled the area, running down an upward-traveling escalator and away from TSA officers.
Reynolds eventually surrendered herself to the Drug Enforcement Administration at John F. Kennedy International Airport in New York on March 23, 2016. She remained in custody while her case was pending and ultimately pleaded guilty in December 2016 to one count of conspiracy to possess and to distribute cocaine. In April 2018, she was given credit for time served and was released. Brown was indicted for the charges in this case while in federal custody for attempting to illegally re-enter the United States in April 2016 following deportation.
This investigation was conducted by the Los Angeles International Airport Criminal Enterprise Task Force (LAACETF), an inter-agency task force based at LAX. The Task Force, which includes representatives of the FBI, the DEA, U.S. Customs and Border Protection, the Transportation Security Administration, the Los Angeles International Airport Police Department, the Los Angeles Police Department, and the Los Angeles County Sheriff's Department. The LAACETF provides a coordinated law enforcement effort to target airport/airline internal criminal enterprises that use the aviation system to transport large amounts of illicit drugs throughout the United States and various international destinations.
Assistant United States Attorney Suria M. Bahadue of the General Crimes Section prosecuted this case.
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Dear Shareholder - Update from Portfolio Manager
Dale Nicholls - Portfolio Manager
Dear Shareholders and potential investors,
Following my last update in July, when I addressed the sharp sell-off we saw in the Chinese equities market, I wanted to once again take the opportunity to share my perspectives on the market, along with an update on changes I've subsequently made to the portfolio.
As you will be aware, well-publicised concerns over increasing regulation were a major factor in the Chinese markets' decline over the summer. Naturally, this remains a key concern for investors. In trying to understand and analyse the government's recent actions, I believe it is important to recognise some key points. First, the 'hand' of the government, coupled with regulatory direction and implementation, is core to the investment landscape in any market. This is particularly true in China.
Therefore, it is important to be aware of trends and the general direction of policy. We have clearly had periods of tightening in the past, for example, government-imposed restrictions around online gaming in 2018. In terms of future policy direction, it is important to be cognisant of the long-term goals laid out in policy documents like the Five-Year Plan when assessing how the regulatory landscape could change and impact an industry's growth profile.
Second, many of these crackdowns are addressing problems that confront countries globally. Big tech and related challenges around anti-trust and data security and privacy are examples, as are the challenges around income inequality. While in many cases we can trace the path of regulation, unlike in most other countries, Beijing's implementation can be swift, which often roils markets.
The property sector has been under the spotlight of both policymakers and investors for some time. Reigning in property speculation is a crucial aspect of President Xi's vision of a more equal society – think back to 2017 when he commented that houses are 'for living in, not for speculation'.
While it is likely that we will see some developers default, I think that the systemic risk remains low. Although comparisons to the situation in the US around the financial crisis of 2008 can be drawn, they are very different given the nature of the companies involved and the general control Beijing has over the economy. In terms of Evergrande itself, it remains an evolving situation. My base case is a government led restructuring, with a focus on project completion and asset disposals to meet social obligations.
Broadly speaking, while I do not expect President Xi's drive towards a healthier, less speculative property sector to be reversed anytime soon, one should not be surprised to see some policy fine tuning in the near-term as both property and land sales continue to slow down. We are already seeing signs of accelerating mortgage approvals in some cities. Importantly, on the back of a period of tightening, there is significant scope to loosen policy.
From a portfolio perspective, an underweight real estate exposure, coupled with an overweight position in the healthcare space has benefited overall performance. Within healthcare, an overweight position in Wuxi AppTec Group supported returns. While I believe that we need to be concerned over pricing pressures in the generic drug sector, the continued emphasis from the government on developing the innovative drug sector remains very much intact. Wuxi is a key facilitator in this area. Furthermore, I believe the prospects for China establishing itself as a global hub for innovation in drug development will gain traction.
One of the bigger shifts in the portfolio has been a reduction in holdings in the consumer discretionary sector, mostly because of valuation levels, and an increase in holdings in materials and industrials. The thesis around industry consolidation in areas like building materials remains very much in place. However, I have trimmed some positions given rising concerns over the residential property slowdown and potential knock-on effects to the industry from the troubled developers in the market.
In the financial space, I remain positive on the outlook for life insurance on rising penetration over the mid-term. Near-term fundamentals generally remain tepid, but I believe this is more than factored into what remain very attractive valuations. Whilst I remain underweight the banking sector, I have built a position in the Postal Savings Bank of China, which I believe is undervalued given its strong, growing position in retail banking and wealth management helping to drive superior returns relative to the sector. We are encouraged by the clear focus of the new management team, including a strong emphasis on growing its green financing.
After the significant recent correction in technology related names, I feel that the risk/ reward payoff is now tipping much more in our favour in these companies. While there is still risk of new regulation, as we think about what could come next, there is a good chance that we are near or close to a "peak" in terms of news flow. As discussed earlier, the government has ambitious long-term goals in areas of economic development and innovation. Clearly these will be difficult to achieve without a vibrant private sector. At the same time, valuations for many companies have moved to historical lows and look even more compelling when we compare to global peers.
As is often the case with broad-based corrections, some stocks with lesser regulatory risk have also been sold off, presenting opportunities. This includes some smaller companies that could actually be beneficiaries since most of the new regulation focuses on larger companies. As I have added to some long positions, and closed some of the short positions, net gearing for the portfolio has increased, and at the time of writing is around 123%.
Despite the market correction, I also continue to feel positive about the outlook for the unlisted portion of the portfolio and the progress being made in the respective businesses. This includes new unlisted holdings including Tuhu Car, the number one brand for independent auto aftermarket product and services in China; Cutia Therapeutics, an emerging leaders in the dermatology and medical aesthetics space in China and Beijing Beisen, a first-class HRM (human resources management) software company and a clear market leader due to its superior cloud and integrated solutions for talent management.
It clearly has been a volatile period, but we have seen times like this before, and most likely will see them again. While the combination of the risks I have outlined is negatively impacting sentiment towards China currently, history teaches us that these are usually the periods that offer the most attractive opportunities. Corporate earnings for the market are forecast to grow over 15% for the next twelve months, with the Company's portfolio comfortably above this level. Meanwhile, the market overall is trading on a price earning multiple that is attractive relative to history and relative to other stock markets globally.
As always, the key thing will be individual companies' ability to deliver on their earnings potential over time. Our growing team continues to be active on the ground, engaging with companies and understanding how they are navigating any shifts in the operating landscape, naturally including regulatory change. This in-depth analysis gives us conviction to act and capitalise, in terms of both adding to existing holdings and seeking new ones - both in the listed and unlisted areas of the market.
Take care and all the best,
Dale Nicholls
Portfolio Manager - Fidelity China Special Situations PLC
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Горско-еврейская литература — еврейско-татская литература горских евреев на горско-еврейском (джуури, джухури) языке.

История
Горско-еврейская литература богата народным фольклором. Наиболее популярными рассказчиками народного фольклора в начале XX века были Мардахай Овшолум (1850—1925),
 Шаул Симанду (1856—1939), Хизгил Дадашев (1860—1945) и Айболо из Тарки.

Раввин Иешаяху Рабинович был в числе первых кто в 1904 г. создал литературные произведения на горско-еврейском языке для драматического кружка в городе Дербенте.

В 1920-х гг. печатались драматические произведения (Яков Агарунов, (джуури:«Падшох, рабби ва ошир») — «Царь, раввин и богач»; Герцль Горский (1904 или 1906-1937), (джуури:«Бахар дас баба-дадай») — «Плоды рук отца и матери»; П. Щербатов, (джуури:«Кук савдогар-революционер») — «Сын купца — революционер»; драматические произведения поэта и новеллиста Юно Семенова (1899—1961). Были известны его пьесы (джуури:«Амалданэ илчи») — «Сообразительный сват», 1924, (джуури:«Дю алатфуруххо») — «Два старьевщика», 1924 и «Махсюм» (1927).

Позже знаток горско-еврейского фольклора, Хизгил Авшалумов создает фольклорнога образа острослова (джуури:Шими Дербенди) — Шими из Дербента (горско-еврейский аналог Гершеле Острополера).

Ряд поэтов XX века создают свои произведения на горско-еврейском языке, такие как Сергей Изгияев, создаёт (джуури:Иму гъэлхэнд шолуминим) — «Мы защитники мира» (1952), (джуури:Фикиргьой шогьир) — «Думы поэта» (1966), (джуури:Муьгьбет ве гьисмет) — «Судьба и любовь» (1972) и ряд других работ. Шимшун Сафонов, в 1968 г. создал поэтический сборник (джуури:Парза, ма'ни ма) — «Лети, мой стих».
 Поэтесса Зоя Семендуева выпустила сборник (джуури:Войгей дуьл) — «Веление сердца». В 2007 году вышла её книга (джуури:''Духдер эн дуь бебе) — «Дочь двух отцов».

В конце XX века ряд литераторов пишут только на русском языке, такие как поэт Лазарь Амиров (1936—2007), новеллист Феликс Бахшиев (1937), литературовед и новеллист Манашир Азизов (1936—2011) и Асаф Мушаилов. Асаф Мушаилов, в 2017 г. выпустил книгу «Стихи Поэмы Рассказы».

Литература
 Агарунов Я. М., Татско (еврейско)-русский словарь. М., 1997.
 Анисимов И. Ш., Кавказские евреи — горцы. — М.: «Наука», 2002
 Анисимов И. Ш., Кавказские евреи — горцы, газ. «Рассвет», No 18. — СПб., 30 апреля 1881
 Берг., Кавказские евреи, Кавказ, #250, 1895
 Грюнберг А. Л. Язык североазербайджанских татов. Л., 1963.
 Грюнберг А. Л. Давыдова Л. Х. Татский язык, в кн: Основы иранского языкознания. Новоиранские языки: западная группа, прикаспийские языки. М., 1982.
 Еврейские языки и диалекты//Краткая еврейская энциклопедия — Иерусалим: общество по исследованию еврейских общин, 1982. — Том 2, колонки 417.
 Миллер В. Ф. Материалы для изучения еврейско-татского языка. СПб., 1892.
 Миллер В. Ф. Очерки морфологии еврейско-татского наречия. СПб., 1892.

Горско-еврейские авторы
 Авшалумов, Хизгил Давидович (1913—2001)
 Агарунов, Яков Михайлович (1907—1992)
 Атнилов, Даниил Атнилович (1915—1968)
 Бахшиев, Миши Юсупович (1910—1972)
 Семёнов, Юно Шаулович (1899—1961)
 Гаврилов, Борис Гаврилович (1908—1990)
 Гаврилов, Михаил Борисович (1926—2014)
 Дадашев, Манувах Мардахаевич (1913—1943)
 Изгияев, Сергей Давидович (1922—1972)
 Семендуева, Зоя Юноевна (1929—2020)

Примечания

Литература на горско-еврейском языке
Литература Дагестана
Литература народов России
Литература по языкам