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Record funding for childcare will drive down costs for parents from today (Jan 2nd) – Seery Kearney
Senator Seery Kearney, Fine Gael's Seanad Spokesperson for Children, said: "Budget 2023 introduces major reforms to the National Childcare Scheme (NCS) from January 2023, which will substantially improve the affordability of early learning and childcare for families.
Fine Gael putting more money back in people's pockets with tax changes from tomorrow (1st Jan) – Carroll McNeill
Income tax changes announced in Budget 2023 that take effect from tomorrow will put more money back in people's pockets to help them to better deal with the cost of living crisis, Minister of State at the Department of Finance, with responsibility for Financial Services, Credit Unions and Insurance, Jennifer Carroll McNeill has stated.
Follow French lead and install solar panels in car parks – McGahon
Installing solar panels on car parks across the country would increase Ireland's renewable energy supply, a Fine Gael Senator has said.
New period poverty campaign needed to encourage local authorities to spread awareness – Higgins
A new campaign on period poverty aimed at local authorities is much needed to spread awareness, a Fine Gael TD has said.
Standard minimum length should apply to multi-journey bus tickets - McGahon
A standard minimum length should be applied to bus tickets on Public Service Obligation (PSO) and commercial services, a Fine Gael Senator has said.
Young adult travel card must be extended to 25-year-olds – Byrne
The Young Adult Card (YAC), which provides an average 50% public transport fare discount to those aged between 19 and 23, should be extended to 25-year-olds, a Fine Gael Senator has said.
36% reduction in burglaries nationwide since 2019 - Ward
Targeted enforcement and high visibility policing has led to a 36% reduction in burglaries and property-related crime, according to Fine Gael Senator Barry Ward.
Fresh focus on children's disabilities will be key in combatting lack of therapy services – Currie & Carrigy
A revived approach to children's disabilities will be crucial in increasing therapy services, according to two Fine Gael Senators.
Supports remain available for those at risk of domestic violence over Christmas – Carroll MacNeill
New refuges will be made available in different parts of the country to support victims of domestic violence over the coming two years. In the interim, crucial supports remain available for victims of Domestic Violence, particularly over the Christmas period, according to Fine Gael TD Jennifer Carroll MacNeill.
Fine Gael Councillor first to benefit from maternity leave entitlement for Councillors
Fine Gael Councillor and Cathaoirleach of Laois County Council, Thomasina Connell, will be the first Councillor in Ireland to benefit from the new measures set out in the Local Government (Maternity Protection and Other Measures for Elected Members in Local Authorities) Bill 2022.
Speech of the Taoiseach, Leo Varadkar TD
Parents to save thousands of euros in childcare fees next month thanks to Budget 2023 - Seery Kearney
Parents are set to save thousands of euros per year with a substantial reduction in childcare fees in January when Budget measures take effect, Fine Gael Senator Mary Seery Kearney has said.
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100 Hipster Baby Names That Are Effortlessly Cool
Sa'iyda Shabazz
This list is the ultimate collection of hipster baby names, including hipster baby names for boys, girls, and gender-neutral options.
Flannel- and beanie-wearing folks who sip craft beer and eat artisanal cheeses. Whether you're all for it, or they grind your gears, there is one thing that hipsters are actually quite good at - naming their babies. It sounds absolutely nutty, but it actually makes a lot of sense. Hipsters get it right when it comes to choosing innovative baby names.
More: 75 Native American Names for Your Little One
Hipsters are responsible for bringing back a lot of names that had kind of been lost. If you're wondering why names that were popular when your grandparents were born are suddenly cool again, you know who to thank. But it's not just old-fashioned names that hipsters have brought back and made cool. Its names of obscure or mainstream literary figures, too. Names that are a little off the beaten path but sound effortlessly chic are definitely credited to hipsters, as well. (We've also got a complete lift of hippie names here if that's more your speed.)
There is no shortage of hipster baby name lists out there, the names are quite popular in their own way. But if you're looking for a varied list of hipster baby names, this list is the ultimate collection of hipster baby names.
Don't have time to read now? Pin it for later:
Hipster Baby Names for Girls
Anais - Likely because of writer Anais Nin, this is certainly a hipster name.
Bea - A shortened version of Beatrice, this nickname stands alone just as well.
Beatrix - Similar to Bea and Beatrice, this is also the name of popular children's book author Beatrix Potter.
Calliope - You may know it from 'Grey's Anatomy', but this Greek name is quite hipster.
Clementine - This super hipster name reminds us of the song, "oh my darling Clementine!"
Daisy - This popular flower is definitely a hipster baby name.
Edie - Sounds a bit like a Nana, but it's also the name of 1960's model Edie Sedgwick.
Flora - This nature name also sounds like someone's grandma.
Hazel - A little old fashioned, but Emily Blunt and John Krasinski have a daughter named Hazel.
Ione - Pronounced eye-own-ee, it's the name of 80s actress Ione Skye from 'Say Anything'.
Isla - This name, pronounced eye-la, is the name of actress Isla Fisher.
Ivy - If you're looking for something hipster, plant names are always a good choice.
June - This monthly name is hipster due to its popularity a long time ago.
Juniper - This plant name is cute and fun, and totally hipster.
Leonora - This diminutive of Eleanor is prime for hipster name recognition.
Lux - This lavish sounding name is growing among hipsters.
Mabel - Another one of those names for your grandma but also your baby hipster names.
Mamie - Meryl Streep's daughter, Mamie Gummer, can be credited with an interest in the name.
Millie - Looking for something a little different than Amelia? Go with Millie instead.
Minerva - Taken from the Roman goddess of wisdom, this name is great for a comeback.
Ophelia - A tragic Shakespearean heroine, Ophelia is quite literary.
Pearl - This birthstone name has rockstar vibes, it was the name of Janis Joplin's final album.
Poppy - This floral name is a little sweet and a little spunky.
Prudence - Taken from the popular Beatles song, you can shorten this to Pru or Prue.
Ramona - Meaning "wise protector," this is another old name that's now cool.
Rhiannon - Hipsters love Stevie Nicks, and this is one of the most popular Fleetwood Mac songs.
Ruby - This gem name is full of sass and cool.
Sadie - This name is a super hip nickname for Sarah that's become popular in its own right.
Stella - Meaning "star," this name always shines bright.
Tallulah - Like actress Tallulah Bankhead, this hipster name is a little dramatic but very cool.
Violet - Floral names are very hipster, as Jennifer Garner and Ben Affleck know.
Zelda - Gamers aren't the only ones who love this name.
Zola- This African name may be familiar from 'Grey's Anatomy'.
Gender-Neutral Hipster Baby Names
Arlo - This name hasn't been popular in 100 years, but it's time for a comeback.
Auden - Taken from poet W.H. Auden, this gender-neutral name has been growing in popularity steadily.
Bear - This one has slowly been gaining popularity, celebrities like Alicia Silverstone, Kate Winslet and Liam Payne have given the name to their kiddos!
Beckett - This name is super popular with hipsters.
Blair - Like Blair Waldorf or Blair Underwood, this name is clearly chic and gender-neutral.
Dexter - Names with an "x" are apparently hipster, and this one is a popular TV and movie character name.
Duke - While it's largely considered a name for boys, Duke can totally be gender-neutral!
Edison - Though it was popular in the 1800s, it's come back in the last 10 years.
Elliot - This name is popular with boys, but growing in popularity with girls.
Fallon - Commonly a girl's name, this name is actually pretty gender-neutral.
Frances - A variant of the name Francis, this name is truly gender-neutral.
Holden - The name became popular thanks to 'The Catcher in the Rye'.
Hopper - This gender-neutral name is more commonly a last name that's not being used as a first name.
Hudson - This name has been growing in popularity over time.
Jagger - Last name of popular musician Mick Jagger, Ashlee Simpson named her daughter Jagger.
James - This common boys name was used by Blake Lively and Ryan Reynolds for their daughter.
Jude - Like the popular Beatles song, this name is very common among hipsters.
Kai - This name has Japanese and Hawaiian roots and a steady growth.
Kingston - This is a common hipster name, Gwen Stefani named her son Kingston.
Lennon - Last name of singer John Lennon, it's becoming a popular gender-neutral name option.
Lincoln - Kristen Bell and Dax Shepard named their daughter Lincoln .
Luca - Hilary Duff named her son this Italian name.
Miller - Hipsters like to make last names first names, and this one is a great one.
Nico - A common Italian nickname, this is a truly hip gender-neutral name.
Orion - This starry first name is certainly climbing up the name charts.
Piper - Some credit the popularity of this name to the show 'Charmed'.
Poe - Taken from poet Edgar Allan Poe, this name is a little spooky and a lot cool.
Remy - Not just the character from 'Ratatouille', Remy is one of those effortlessly cool nickname names.
Ryder - This name means "knight," how hip!
Sullivan - Commonly a last name, this gender-neutral name is ready for its spotlight.
Tate - Happy hipster babies are cheerful, which is what this name means.
Walden - Another poetic name, and the famous pond in New England, very hipster.
Wren - These sweet little songbirds make for perfect hipster names.
Zephyr - Meaning "west wind," this is popular with hipster boys, but it's a great gender-neutral option.
Hipster Baby Names for Boys
Alden - This name means old friend.
Atticus - This Greek name has become synonymous with the character from 'To Kill A Mockingbird' .
August - This name is quite popular with celebrities and regular folks alike!
Byron - Last name of poet Lord Byron, it's really transformed into a popular first name.
Calvin - Meaning "bald one," this name peaked in popularity in 1920.
Cormac - First name of popular author Cormac McCarthy, this name is growing in popularity.
Dashiell - This fancy sounding Scottish name can also be shortened to Dash.
Elvis - Looking for something cool, this is pretty effortlessly cool.
Fitzgerald - think of 'Great Gatsby' author F. Scott Fitzgerald, or JFK, John Fitzgerald Kennedy.
Gus - This grandpa name can be a nickname, but it stands well on its own.
Huck - A shortened form of Huckleberry, from the titular Mark Twain novel.
Ike - This nickname has emerged as a great first name option.
Jack - A classic, timeless name that hasn't gone out of style, but definitely evolved.
Leopold - We have Queen Victoria to thank for the popularity of this Germanic hipster name.
Lionel - This name hasn't been popular since the 1920s, unless you count Lionel Ritchie.
Magnus - This name means "greatest," as in greatest hipster boy baby name.
Matteo - Try this instead of the name Matthew, it's a lot cooler.
Milo - This boy's name is quite popular with hipster parents.
Monty - Looking for something with a vintage feel? Monty sounds like a grandpa's nickname.
Orson - Like actor Orson Welles, there's a certain Old Hollywood vintage to this name.
Oscar - With Norse and Gaelic origins, this old man name is quite hipster.
Otis - Much like singer Otis Redding, this name is effortlessly chill and cool.
Roman - Popular among celebrities like Cate Blanchett and Debra Messing, this name is so hip.
Roscoe - Another one of those cool grandpa names.
Rufus - This name is super hip, like singer Rufus Wainwright.
Sanford - This last name was actually a popular first name in the early 1900s.
Sebastian - Because of indie band Belle and Sebastian, this feels more hipster.
Stellan - Scandinavian names are very cool, and this one is no exception.
Thor - Like the hottest Avenger, this name is strong, aka the god of thunder.
Van - You don't have to be a brown eyed girl to name your baby Van.
Waldo - You don't need to look hard for this hipster name.
Watson - This name hasn't been popular in the last 20 years, but it's so sophisticated.
Zeke - A little bit country, a lot of hipster, this nickname is strong on its own.
Looking for more baby names and inspiration? Check out our Baby Name Center.
Sa'iyda Shabazz is a writer and mother to one. She can often be found in her apartment writing into the wee hours and trying to read a book in peace.
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redpajama_set_name: RedPajamaCommonCrawl


text:
\section{Introduction}
Transformers~\cite{NIPS2017_3f5ee243} have achieved great success in the domains of natural language processing (NLP)~\cite{devlin-2019-bert} and computer vision~\cite{dosovitskiy2021an}. These models benefit from the self-attention mechanism, which computes correlations between all pairs of tokens in an input sequence. Self-attention enables transformers to capture long-range context, which is important in both language and vision tasks.

\begin{figure}[!t]
\vskip 0.2in
\begin{center}
\centerline{\includegraphics[width=1.1\columnwidth]{compare_scheme}}
\caption{Comparing transformers with (a) standard self-attention~\cite{NIPS2017_3f5ee243}, (b-c) efficient attention mechanisms with localized~\cite{yang-2018} or other sparsity patterns~\cite{LiJXZCWY19} that lose the full-attention capacity, and (d) area attention~\cite{pmlr-v97-li19e} that maintains an extra memory formed by average pooling with a predefined set of pool sizes. (e) Our ContextPool learns to pool with adaptive weighting and support size for each token in-place, before computing full attention.}
\label{fig:compare_scheme}
\end{center}
\vskip -0.3in
\end{figure}

However, each attention layer uses pairwise relationships between individual tokens (\emph{e.g.},~text characters and image pixels), which implies a fixed granularity for attention. This ignores the context around each token, which can vary substantially in scale in the vision and language domains,~\emph{e.g.},~from characters to words and from phrases to sentence. Therefore, self-attention with fixed granularity can be fundamentally limited for modeling complex distribution of contextual dependencies, and several layers of self-attention might be needed to make up for this fixed granularity.

Recent vision transformers such as Swin transformer~\cite{liu2021Swin} and PVT~\cite{Wang_2021_ICCV} adopt a hierarchical architecture to compute self-attention at various scales. However, such attention scale or granularity is predetermined rather than learned. Similarly, Li~\emph{et al.}~\yrcite{pmlr-v97-li19e} proposed to use a predefined set of pooling sizes to form a multi-scale `area memory', which accounts for varying context range but in fixed architecture. In BP-Transformer~\cite{1911-04070}, a fine-to-coarse attention is computed from multi-scale attention spans via automatic binary partitioning, but the resulting local span sequences might still hurt the capacity of \emph{full} attention.

In this paper we propose \emph{ContextPool}, a drop-in and low-cost module for both the transformer and convolutional networks (ConvNets) to enhance their capacity to model long-range context with dynamic scales, and hence to facilitate efficient representation learning. The idea behind ContextPool is in general inspired by ConvNets, which have local receptive fields and pooling operations.
Here we similarly learn to pool neighboring features for each token at every attention layer before computing full attention in transformers. Importantly, the pooling weights and support size are input-adaptive. This allows the pooled features to encode meaningful context with dynamic scale. As a result, self-attention among pooled features can explicitly capture high-level dependencies between contexts.

We show our simple ContextPool makes attention models more expressive, achieving strong performance often with fewer layers. This leads to significantly reduced cost without much sacrifice in accuracy. On the other hand, when we can maintain the same level of compute cost, ContextPool consistently improves performance as it can model longer range of context. When compared to recent transformer models that reduce cost by sparsifying the attention matrix~\cite{yang-2018,sukhbaatar2019,child2019sparsetransformer,ainslie2020etc}, our ContextPool method preserves the full attention capability (see comparison in Fig.~\ref{fig:compare_scheme}) and can be considered orthogonal to those efficient techniques.

Experiments show that our ContextPool module significantly improves transformer models in terms of performance-cost trade-off, matching or surpassing state-of-the-art performance with less compute on several language and image benchmarks. ContextPool also outperforms recent works with adaptive context size or sparse attention, and is applicable to ConvNets for efficient representation learning. To summarize, our main \textbf{contributions} are:
\begin{itemize}
\item We introduce ContextPool to encode varying-sized context for each token in an attention layer, giving rise to self-attention with adaptive granularity to model high-level dependencies.
\item We show ContextPool-based transformers achieve competitive performance with much less compute on several language and image benchmarks, and outperform prior works with adaptive context size or sparse attention patterns.
\item ContextPool is applicable to ConvNets with strong image recognition performance, showing its promise to be a generic module for efficient representation learning.
\end{itemize}

\section{Related Work}
\textbf{Context in Transformers} is captured by the attention mechanism between all pairs of tokens from the entire input sequence. When the network goes deeper, high-level contextual dependencies emerge. However, full attention scales quadratically with the sequence length as existing attention models are trained to attend to individual tokens with a fixed granularity,~\emph{e.g.},~text characters and image pixels. Hence the vanilla Transformer~\cite{NIPS2017_3f5ee243} is prohibitive for learning long sequences such as long documents or high-resolution images (modeled as long sequences of image patches).

Recent works build on the hierarchical architecture to improve the capability of long-range context modeling. In the vision domain for example, hierarchical transformers such as Swin transformer~\cite{liu2021Swin}, PVT~\cite{Wang_2021_ICCV} and ViL~\cite{Zhang_2021_ICCV} rely on predefined image pyramids to compute self-attention at multiple scales, and can thus model long sequences of image patches at a much higher resolution. However, both the scaling scheme and effective attention granularities remain fixed in these methods. In a similar spirit, `area attention'~\cite{pmlr-v97-li19e} computes multi-scale attention which is generic for both language and vision tasks. Specifically, attention is computed against a multi-scale memory formed by pooling the original memory with predetermined pool sizes. This not only requires larger memory but also does not adapt the context range based on content. Finally, the BP-Transformer~\cite{1911-04070} computes attention using multi-scale attention spans that encode fine-to-coarse contexts, but it imposes a sparsity prior on the attention mechanism, which is adaptive, but which might hurt its capacity.

\textbf{Efficient Transformers} mostly use sparsity or low-rank assumptions on the attention matrix to reduce cost. For sparse attention methods, one can sparsify the attention matrix with predefined patterns like~local window~\cite{yang-2018,sukhbaatar2019,child2019sparsetransformer}, blockwise~\cite{blockwise}, log-sparse~\cite{LiJXZCWY19} or axial~\cite{ho2019axial} patterns and their combinations~\cite{Beltagy2020Longformer,ainslie2020etc,zaheer2020bigbird}. The sparsity patterns can also be learned as in~\cite{Kitaev2020Reformer,TACL2405,Tay2020SparseSA}. These sparse attention methods, despite their sub-quadratic cost, often have a reduced model capacity because each token can only attend to a subset of tokens. Generally, sparse attention needs more layers to model full contextual dependencies in a long sequence~\cite{child2019sparsetransformer}. Another family of efficient transformers approximate the attention matrix using low-rank projections~\cite{wang2020linformer} or feature maps of particular kernels~\cite{katharopoulos20}. Such low-rank methods preserve the full attention capability with low computational cost, but suffer from the lossy approximations of potentially full-rank attention. We depart from the above-mentioned methods, aiming for efficient, full attention without sparse or low-rank approximations. Nevertheless, our ContextPool module can be embedded within the internals of several of these models.

There are some recent attempts to accelerate transformers by directly reducing the number of tokens to process in attention layers. Ryoo~\emph{et al.}~\yrcite{ryoo2021tokenlearner} proposed to `tokenize' the input images by aggregating their feature maps into a few tokens, while DynamicViT~\cite{rao2021dynamicvit} relies on an extra neural network to prune tokens for a fully trained ViT~\cite{dosovitskiy2021an}. We provide a novel perspective for parameter-efficient self-attention given any amount of tokens. By learning to pool the token features with adaptive weighting and pool size, we obtain more expressive tokens from fewer layers.

\textbf{Context in ConvNets} is efficiently captured by convolutions, which summarize local neighborhoods with shared weights and when combined with pooling, can model long-term dependencies. Recent works indicate that ConvNets benefit from using different kernel sizes at different convolutional layers~\cite{Pintea21}. Therefore, many methods choose to learn adaptive kernel size to account for data-dependent context or receptive field. Concretely, they scale kernels by dilation and learn dilation factors over shifted Delta-dirac functions~\cite{8237351}, scalable Gaussian functions~\cite{Shelhamer19} or Gaussian derivative filters~\cite{Pintea21,tomen21a}. Another method of receptive field learning in ConvNets is based on learning pooling functions with adaptive pooling regions~\cite{NIPS2011_6c1da886,6248076}. Our ContextPool method is also applicable to ConvNets. By learning dynamic pooling weights and support size, it is shown to be competitive with existing methods while maintaining low computational cost.

\begin{figure*}[!t]
\vskip 0.1in
\begin{center}
\centerline{\includegraphics[width=\linewidth]{schematic}}
\vskip -0.05in
\caption{(a) Motivation: the proposed ContextPool seeks to achieve adaptive attention granularity through adaptive context pooling around each token and then computing context-wise attention. This helps to capture high-level dependencies and is useful to model ambiguous pronoun ``\emph{it}'' by associating with neighboring phrases rather than single words, or to model interactions between varying-sized object parts. (b) For adaptive ContextPool, we learn the pooling weights and support size dynamically for each token. (c) Our ContextPool module is applicable to both transformers and ConvNets for efficient feature learning. For transformers, the ContextPool module is placed after each attention block, whose output token features are pooled to the same number of features for use in the next attention block. While for ConvNets, ContextPool replaces the conventional pooling function (please refer to supplementary materials for details).}
\label{fig:schematic}
\end{center}
\vskip -0.2in
\end{figure*}

\section{ContextPool for Transformers}

\subsection{Standard Transformers}

A standard transformer model~\cite{NIPS2017_3f5ee243} is a chain of self-attention modules (self-attention plus feed-forward layers). The input of each self-attention layer is a feature matrix $\bm{X} \in \mathbb{R}^{n \times d}$ from the preceding layer. $\bm{X}$ is a sequence of $n$ tokens =$\{\bm{x}_1,\dots,\bm{x}_n\}$ each of dimension $d$. The attention layer operates on all the token features in $\bm{X}$. Specifically, each token $\bm{x}_i$ is first transformed to the query $\bm{q}_i=\bm{W}^q \bm{x}_i$, key $\bm{k}_i=\bm{W}^k \bm{x}_i$ and value $\bm{v}_i=\bm{W}^v \bm{x}_i$ with learned projection matrices $\{\bm{W}^q, \bm{W}^k, \bm{W}^v\} \in \mathbb{R}^{d \times d}$. Then the attention score of one query $\bm{q}$ attending to all the keys $\{\bm{k}_i\}$ stored in a memory is given by:
\begin{equation}
a_i = \frac{\exp({\bm{q}^T\bm{k}_i})}{\sum_{j=1}^n \exp({\bm{q}^T\bm{k}_j})}.
\label{eq1}
\end{equation}
The final output $\bm{o}_q$ from querying the memory with $\bm{q}$ is obtained by taking a weighted average of all the values $\{\bm{v}_i\}$ in memory:
\begin{equation}
\bm{o}_q = \sum_{i=1}^n a_i \bm{v}_i.
\label{eq2}
\end{equation}
In practice, multi-head self-attention is used in transformers, where multiple projections are learned to compute attention within different heads. The outputs are then concatenated and projected into refined token features.

\textbf{Drawback} The above self-attention mechanism assumes a fixed granularity over which to construct the query and key vectors for individual tokens. However, such a fixed granularity may be sub-optimal for modeling context with different scales. Consider neural machine translation with word-based tokens -- translating numerals from one language to another requires little context, but translating ambiguous pronouns (\emph{e.g.},~``\emph{it}'') requires long range-contextual cues from neighboring tokens.
One might argue that this difficulty can be resolved by using deeper models, where self-attention in deeper layers can capture the interactions between single tokens and bake them into deep feature representations. This can progressively correct for the fixed attention granularity at lower layers, but requires more computation that may be avoidable with an adaptive strategy.

\subsection{Adaptive Context Pooling}

\textbf{Motivation} We motivate our method using Fig.~\ref{fig:schematic}(a). In language modelling, if we can piece words together to form phrases, we can gradually capture the phrasal patterns and useful context information. This helps to disambiguate the pronoun ``\emph{it}'' by linking it to the phrase ``\emph{The couch}''. Similarly, in image understanding, pooling similar image patches can enable the model to learn semantics of a bird's body parts. To account for the special role of context in obtaining adaptive attention granularity, we introduce an explicit way of learning context-aware token features. We do this by learning to pool neighboring features for each token (\emph{ContextPool}). Self-attention between such pooled features can thus be context-aware and model high-level dependencies, without requiring multiple self-attention layers.

Therefore, our ContextPool method needs an input-adaptive pooling function. Below, we describe how to learn that with adaptive pooling weights and pooling size. Specifically, given the input token feature matrix $\bm{X} \in \mathbb{R}^{n \times d}$, we pool for each token $\bm{x}_i \in \bm{X}$ with learned weights $\bm{w}  \in \mathbb{R}^{n \times 1}$ and a Gaussian mask $\bm{g}^i  \in \mathbb{R}^{n \times 1}$ (acting as a soft, local pooling window), generating a contextual feature matrix $\bm{Y} \in \mathbb{R}^{n \times d}$ of the same size of $\bm{X}$ (see Fig.~\ref{fig:schematic}(b)).

\textbf{Adaptive pooling weights} differ from the uniform ones in the popular average pooling function. We found it helpful to reweight the neighboring token features $\{\bm{x}_j\}$ during pooling based on their contextual support to $\bm{x}_i$. One widely used approach of measuring such support is based on nonlocal feature similarity as in~\cite{8578911}:
\begin{equation}
w_j = \frac{\exp(\theta(\bm{x}_i)^T \phi(\bm{x}_j))}{\sum_{j=1}^n \exp(\theta(\bm{x}_i)^T \phi(\bm{x}_j))},
\label{eq3}
\end{equation}
where $\theta(\bm{x}_i)=\bm{W}^{\theta} \bm{x}_i$ and $\phi(\bm{x}_j)= \bm{W}^{\phi} \bm{x}_j$ are embeddings with learnable projections $\{\bm{W}^{\theta},\bm{W}^{\phi}\} \in \mathbb{R}^{d \times d}$.

We dub such learned pooling weights $\bm{w}$ as nonlocal weights (\textbf{NL weights}). The intuition behind NL weights is that similar features in the context are likely to correspond to semantically related entities. Therefore, nonlocal similarity pooling in form of $\sum_{i=1}^n w_i \bm{x}_i$ can provide contextual information to increase (or decrease) the probability of a semantic region or segment. Note we only introduce NL weights as a comparing baseline.

One limitation of NL weights is that each weight $w_j$ in Eq.~(\ref{eq3}) only depends on a feature pair $(\bm{x}_i,\bm{x}_j)$, overlooking the potential contributions from other features to $\bm{x}_i$. Here we turn to learning $w_j$ by a mapping function $m(\cdot)$ conditioned on all the token features $\{\bm{x}_i\}$ in $\bm{X}$. In fact, we predict the pooling weights $\bm{w}=m(\bm{X})$ all at once, where $m(\cdot)$ is implemented as two convolutional layers. Hence the prediction of $\bm{w}$ is collaborative and more efficient than NL weights prediction.

\textbf{Adaptive pooling size} Pooling with adaptive weights, however, does not take into account the location relationships between tokens. Here we introduce a locality prior to bias pooling towards the local context around considered token. Note that learning the pooling weights alone might also be able to find local patterns in the learned weights. However, the locality prior can simplify learning by allowing factorized and independent predictions of pooling weights and scope. Our experiments support this hypothesis with favorable results. The locality prior also shares a similar high-level idea with the effective receptive field~\cite{NIPS2016_c8067ad1}, which is shown to have a Gaussian distribution.

We learn a Gaussian mask for each token to implement soft, localized pooling with \emph{adaptive} pooling size rather than a hand-picked one. Specifically, we learn the mapping function $m(\cdot)$ to predict both the pooling weights $\bm{w}\in \mathbb{R}^{n \times 1}$ and sizes $\bm{s}\in \mathbb{R}^{n \times 1}$ for $n$ input tokens conditioned on their features $\bm{X}$,~\emph{i.e.},~$\{\bm{w},\bm{s}\}=m(\bm{X})$. We implement $m(\cdot)$ again by two convolutional layers, but with the channel size set to 2 now. This enables generating the vectors of $\bm{w}$ and $\bm{s}$ altogether, which are normalized by a softmax function for ease of training. Given the normalized pooling size $s_{i} \in [0,1]$, we then transform it to the standard deviation $\sigma_{i}=rn \cdot s_{i}$ of a Gaussian mask $\bm{g}^i \sim \mathcal{N}(i,\sigma_{i}^2)$. Here $r$ is a scalar empirically set as 0.1.

By multiplying the learned pooling weights $\bm{w}$ with the Gaussian mask $\bm{g}^i$ for token $\bm{x}_i$, we arrive at our final ContextPool function:
\begin{equation}
\bm{y}_i = f_{ave}(\bm{X} \odot \gamma(\bm{w}) \odot \gamma(\bm{g}^i)) = \sum_{j=1}^n \bm{x}_{j} \cdot w_{j} \cdot g^i_{j},
\label{eq4}
\end{equation}
where $\bm{y}_i \in \bm{Y}$ denotes the ContextPooled features, $f_{ave}$ denotes average pooling function, $\gamma(\cdot)$ is a broadcasting function for element-wise multiplication $\odot$. We set the normalization factor as $C(\bm{X}) = \sum_{j} w_{j} \cdot g^i_{j}$.

As shown in Fig.~\ref{fig:schematic}(c), our ContextPool module can be placed after different attention blocks. After each attention block, we take its outputs as input token features and pool them to the same number of features for use in the next attention block. During training, we jointly learn the main model and ContextPool parameters. We also show the applicability of our ContextPool method to ConvNets in supplementary materials.

\section{Results}

We evaluate the proposed ContextPool (dubbed ``CP'' as a prefix) module mainly in the transformer architecture to show how strengthened context modeling can benefit self-attention in a parameter-efficient way. We validate such benefits on both language and vision tasks that require a good context modeling capability. Supplementary materials also show that our ContextPool can be seamlessly integrated into ConvNets in place of the conventional pooling function. ContextPool leads to strong results on standard image classification benchmarks, being competitive or even better than those ConvNets with adaptive kernel size or receptive field. This comes at low computational overhead, showing the potential of ContextPool to be a generic module for efficient representation learning.

\subsection{Tasks, Datasets and Implementation}

\textbf{Neural Machine Translation} For language tasks, we first experiment on the token-level Neural Machine Translation (NMT) task. We use both the WMT 2014 English-to-German (EN-DE) dataset with about 4.5 million English-German sentence pairs, and the and English-French (EN-FR) dataset with about 36 million English-French sentence pairs. A token is a byte pair or a word piece as in~\cite{NIPS2017_3f5ee243}. We compare with different methods all using three transformer architectures as defined in~\cite{pmlr-v97-li19e}: Small (2 layers), Base and Big (6 layers) models. For our method, we insert ContextPool after every attention layer. Following~\cite{pmlr-v97-li19e}, we train for 250k iterations for Small and Base models, and for 600k iterations with a smaller batch size for the Big model due to the memory constraint. We use Adam optimizer with the same learning rate schedule in~\cite{NIPS2017_3f5ee243}.

\textbf{Autoregressive Language Modeling} We also evaluate ContextPool on the autoregressive language modeling task at character level. Compared to the token-level task, character-level task is harder due to much longer sequences, which would hypothetically benefit more from stronger context modeling. We use enwik8 and text8 datasets, each with 100M characters and 90M/5M/5M for train/dev/test as in~\cite{MattMahoney09}. For testing, we follow~\cite{Beltagy2020Longformer} to split the dataset into overlapping sequences of length 32k with step size 512, and then calculate the Bits Per Character (BPC) of predicting 512 characters from previous 32k.

We use the same 12-layer model architecture with Longformer~\cite{Beltagy2020Longformer}. We train our models in 3 stages with increasing sequence lengths (2048, 4096, 8192) and different batch sizes (32, 32, 16). All models are trained for a total of 530k steps with linear learning rate warmup. We also use dropout rates 0.2 and weight decays 0.01.

\textbf{Image Classification} We benchmark different transformer models on the widely used ImageNet-1K classification dataset~\cite{DenDon09Imagenet}. There are 1.28M training and 50k validation images from 1k classes. The top-1 accuracy on a single crop
is reported. We consider the regular training setting in~\cite{touvron21a} where no external training data are used. The input image resolution is $224^2$ by default. For higher resolutions like $384^2$, we fine-tune the $224^2$ trained models. We train for 300 epochs with the AdamW optimizer, using a cosine decay learning rate scheduler and linear warm-up (20 epochs). When fine-tuning on higher resolution images, we tune for 30 epochs with a similar training recipe as in~\cite{liu2021Swin}. We have batch size 1024, initial learning rate 0.001, weight decay 0.05, and the max norm of gradient clipping 1. Stronger data augmentation is found to benefit our ContextPool method. Therefore we use a larger degree of augmentation with the augmentation techniques in~\cite{touvron21a} such as RandAugment~\cite{NEURIPS2020_d85b63ef}, making our pooled token features more robust.

\subsection{Ablations and Comparisons}

\textbf{Ablation on adaptive pooling weights and size} We start with ablation studies on these two core components of our ContextPool method and compare against their alternatives. For this purpose, both the NMT and image classification tasks are considered for a comprehensive comparison. For NMT, we choose the English-German (EN-DE) translation task using the Base model. While for image classification, the ViT-B/16 model~\cite{dosovitskiy2021an} (the ``Base'' variant with $16\times16$ input patch size) is used.

\begin{table*}[!t]

\begin{minipage}{.5\textwidth}
\caption{Ablations on token-level translation (EN-DE task) using the Base model. Speed (steps / s) is measured on a V100 GPU. CP denotes the use of our full ContextPool module ($\bm{w} \odot \bm{g}^i$). The middle and bottom cells compare with alternative weightings and locality priors respectively for context pooling.}
\label{tb:ablation_NMT}
\begin{center}
\begin{small}
\resizebox{\linewidth}{!}{
\begin{tabular}{lccc}
\toprule
Method & Memory (G)& Speed & BLEU $\uparrow$ \\
\midrule
Base & 17.2 & 1.20 & 28.16\\
CP-Base ($\bm{w} \odot \bm{g}^i$)& 17.6 & 1.12 & \textbf{28.91}\\ \midrule
Unnormalized weights $\odot \,\bm{g}^i$ & 17.6 & 1.13 & 28.79\\
Uniform weights $\odot\, \bm{g}^i$  & 17.4 & 1.16 & 28.52\\
NL weights $\odot\, \bm{g}^i$ & 21.3 & 0.84 & 28.66\\ \midrule
No locality prior $\odot\, \bm{w}$ & 17.4 & 1.15 & 28.31\\
Fixed window $\odot\, \bm{w}$& 17.4 & 1.15 & 28.55\\
Adaptive window $\odot\, \bm{w}$& 17.6 & 1.12 & 28.74\\
Random sparse $\odot\, \bm{w}$& 17.4 & 1.15 & 28.14\\
\bottomrule
\end{tabular}
}
\end{small}
\end{center}
\vskip -0.2in
\end{minipage}
\hspace{0.1in}
\begin{minipage}{.5\textwidth}
\caption{Ablations on ImageNet-1K classification. Top1 is top-1 accuracy. Throughput (images / s) is measured on a V100 GPU. CP denotes the use of our full ContextPool module ($\bm{w} \odot \bm{g}^i$). The middle and bottom cells compare with alternative weightings and locality priors respectively for context pooling.}
\label{tb:ablation_imagenet}
\begin{center}
\begin{small}
\resizebox{\linewidth}{!}{
\begin{tabular}{lccc}
\toprule
Method & FLOPs (G) & Throughput & Top1 $\uparrow$ \\
\midrule
ViT-B/16 & 55.4 & 85.9 & 77.9\\
CP-ViT-B/16 ($\bm{w} \odot \bm{g}^i$) & 56.7 & 84.1 & \textbf{79.9}\\ \midrule
Unnormalized weights $\odot \, \bm{g}^i$ & 56.6 & 84.2 & 79.7\\
Uniform weights $\odot \, \bm{g}^i$ & 56.1 & 84.8 & 78.9\\
NL weights $\odot \, \bm{g}^i$ & 68.8 & 69.2 & 79.4\\ \midrule
No locality prior $\odot \, \bm{w}$& 56.0 & 85.1 & 78.3\\
Fixed window $\odot \, \bm{w}$& 56.0 & 85.1 & 78.9\\
Adaptive window $\odot \, \bm{w}$& 56.7 & 84.1 & 79.6\\
Random sparse $\odot \, \bm{w}$& 56.0 & 85.1 & 78.1\\
\bottomrule
\end{tabular}
}
\end{small}
\end{center}
\vskip -0.2in
\end{minipage}
\vskip -0.2in
\end{table*}

Tables~\ref{tb:ablation_NMT} and~\ref{tb:ablation_imagenet} summarize the results. We observe that our ContextPool method can consistently improve the baseline transformers at only marginal overhead (in memory, FLOPs and speed), due to the efficiency of adaptive pooling functions implemented by convolutions. For the learning of pooling weights, we first compare with those un-normalized weights without using softmax (middle cell). We obtained slightly worse results for both tasks using un-normalized weights, which confirms the need of normalization for effective weighting (note the pooling size predictions were always softmax normalized).
One straightforward alternative to our learned weighting is the use of uniform weights,~\emph{i.e.},~to perform average pooling. By doing so, we save the learning cost for the weights but suffer from apparent performance loss. We can also choose to learn NL weights as in Eq.~(\ref{eq3}), which is equivalent to learning extra, single-head self-attention weights in transformers and is thus much more costly than our lightweight convolutional method. Further, NL weights are found to be less competitive than ours due to the lack of feature interactions in pairwise weights computation.

\begin{table*}[!t]
\caption{Ablation study on the design choice of ContextPool (CP) module. For transformers, we choose the same NMT and image classification tasks as in Tables~\ref{tb:ablation_NMT} and~\ref{tb:ablation_imagenet}, with identical task settings and baseline models. We also include the ConvNet experiments (details in supplementary materials) for more comprehensive ablations.}
\label{tb:CP_design}
\begin{center}
\begin{small}
\resizebox{\textwidth}{!}{
\begin{tabular}{cccccccccc}
\toprule
\multirow{2}{*}{Method} & \multicolumn{3}{c}{\textbf{Transformer} (EN-DE translation)} & \multicolumn{3}{c}{\textbf{Transformer} (ImageNet classification)} & \multicolumn{3}{c}{\textbf{ConvNet} (CIFAR-10 classification)} \\
\cmidrule(lr){2-4}
\cmidrule(lr){5-7}
\cmidrule(lr){8-10}
 & Memory (G)& Speed & BLEU $\uparrow$ & FLOPs (G) & Throughput & Top1 $\uparrow$ & FLOPs (G) & Size & Accuracy $\uparrow$ \\
\midrule
Baseline & 17.2 & 1.20 & 28.16 & 55.4 & 85.9 & 77.9 & 3.7 & 0.66M & 92.9 \\
+ CNN-based CP (default) & 17.6 & 1.12 & \textbf{28.91} & 56.7 & 84.1 & \textbf{79.9} & 3.9 & 0.68M& \textbf{93.4} \\
+ MLP-based CP & 17.3 & 1.17 & 28.33 & 56.5 & 85.2 & 78.7 & 3.8 & 0.67M& 93.1 \\
+ Self-attention-based CP & 21.3 & 0.84 & 28.66 & 68.8 & 69.2 & 79.4 & 4.4 & 0.67M& 93.2 \\
\bottomrule
\end{tabular}
}
\end{small}
\end{center}
\vskip -0.1in
\end{table*}

Tables~\ref{tb:ablation_NMT} and~\ref{tb:ablation_imagenet} (bottom cell) compare several baselines to replace our learned Gaussian mask that imposes a soft locality prior for pooling. When we remove the locality prior entirely, we save compute again but observe a big drop in performance for both tasks. This suggests that context pooling indeed benefits from a local ``receptive field'' (similar to the findings in~\cite{NIPS2016_c8067ad1}). It also suggests the difficulty of disentangling the local prior from the pooling weights by learning the latter alone in an unfactorized way. The ``Fixed window'' baseline is one simple remedy to this issue by associating a fixed local window to the pooling function, where the window size is hand-picked on validation data. We see immediate help from this baseline (relative to ``no locality prior''). On the other hand, we find pooling at random sparse locations will slightly hurt performance. Finally, learning adaptive local windows performs close to our method with adaptive soft Gaussian masks, but the benefits of the latter still hold with consistent gains.

\textbf{Ablation on the design choice of ContextPool module} Recall that our default ContextPool module is implemented as a convolutional mapping function $m(\bm{X})$, which maps the input feature matrix $\bm{X}$ into arrays of pooling weights and size. Table~\ref{tb:CP_design} compares such a CNN-based design choice against alternatives like fully-connected MLP and self-attention layers. Here we conduct the comparing experiments on both transformers and ConvNets (detailed in supplementary materials) for a more comprehensive ablation. Note for transformers, we still benchmark on the same tasks as in Tables~\ref{tb:ablation_NMT} and~\ref{tb:ablation_imagenet}, with identical task settings and baseline models.

The MLP-based ContextPool module in Table~\ref{tb:CP_design} can be considered as the simplest form of $m(\cdot)$, which maps each feature vector $\bm{x}_i \in \bm{X}$ to its corresponding pooling weights. We can see that MLP is more compute-efficient than our convolutional module but worse in performance. The reason is that such MLP module operates individually for $\bm{x}_i$ without considering feature interactions when predicting their pooling weights, while convolutional layers leverage neighboring features to do so. Note we can use a giant MLP that predicts for all $\{\bm{x}_i\}$ together, which becomes collaborative but at a much higher cost.

Alternatively, we can implement $m(\cdot)$ using a (single-head) self-attention layer as in Eq.~(\ref{eq3}). However, as mentioned before, such mapping function $m(\cdot)$ is not only costly with quadratic computation, but also limited in modeling feature interactions. As shown in Table~\ref{tb:CP_design}, the inferiority also translates to the ConvNet framework. Note we can improve by modeling richer feature interactions with more than one attention layers, but this will further increase the cost.

\begin{figure}[!t]
\begin{center}
\centerline{\includegraphics[width=0.7\columnwidth]{visualization}}
\vskip -0.1in
\caption{Visualizations of the pooling weights and size (in the form of soft Gaussian mask) predicted by our ContextPool module on example ImageNet images. We observe that the pooling weights are learned to aggregate diverse information from different locations or object parts, while the pooling size is learned to capture either local or global image context depending on the input.}
\label{fig:visualization}
\end{center}
\vskip -0.3in
\end{figure}

\begin{table*}[!t]
\caption{The BLEU scores for token-level translation on the WMT 2014 EN-DE and EN-FR datasets. We compare our CP-attention with standard attention~\cite{NIPS2017_3f5ee243}, local attention~\cite{yang-2018} and area attention~\cite{pmlr-v97-li19e}.}
\vskip 0.1in
\label{tb:NMT}
\begin{center}
\begin{small}
\begin{tabular}{ccccccccc}
\toprule
\multirow{2}{*}{Model} & \multicolumn{2}{c}{Standard attention} & \multicolumn{2}{c}{Local attention} & \multicolumn{2}{c}{Area attention} & \multicolumn{2}{c}{CP-attention (ours)}\\
\cmidrule(lr){2-3}
\cmidrule(lr){4-5}
\cmidrule(lr){6-7}
\cmidrule(lr){8-9}
 & EN-DE & EN-FR & EN-DE & EN-FR & EN-DE & EN-FR & EN-DE & EN-FR\\
\midrule
Small & 22.55 & 31.93 & 22.71  & 32.48  & 23.20 & 32.93 &  \textbf{23.67} & \textbf{33.24}  \\
Base & 28.16 & 38.97 & 28.32  &  39.04  & 28.52 & 39.19 & \textbf{28.91}  &  \textbf{39.36} \\
Big & 29.26 & 41.00 & 29.31  &  41.17 & 29.77 & 41.46 &  \textbf{30.11} &  \textbf{41.59} \\
\bottomrule
\end{tabular}
\end{small}
\end{center}
\vskip -0.1in
\end{table*}

\subsection{Visualizations and Analysis}

Now we visualize what have been learned in our pooling weights and pooling sizes (in the form of soft Gaussian mask). Since visualization is easier on images with spatial grids, we take the ViT-B/16 model and visualize the predictions from our ContextPool module after the second attention layer.

We are able to observe from Fig.~\ref{fig:visualization} that: 1) The pooling weights are learned to aggregate diverse information, and seem to go beyond feature similarity (the main intuition of NL weights). The last image gives one example where the pooling weights highlight some dissimilar regions around the window and ceiling, which can instead accumulate evidence for the target class of ``room''. 2) The learned pooling size is indeed input dependent, capturing the local or global context adaptively. Fig.~\ref{fig:Poolsize_dist} further shows the distributions of pooling size in different layers. Interestingly, the predicted pooling size remains diverse within each layer, but in general tends to increase at higher layers to capture long-range dependencies.

\begin{figure}[!t]
\begin{center}
\centerline{\includegraphics[width=\columnwidth]{Poolsize_dist}}
\vskip -0.1in
\caption{Distributions of the predicted pooling size by our ContextPool module in different attention layers (ViT-B/16).}
\label{fig:Poolsize_dist}
\end{center}
\vskip -0.3in
\end{figure}

\subsection{Comparing to SOTAs on Language Tasks}

Table~\ref{tb:NMT} evaluates our ContextPool-based attention model on the token-level NMT task using both EN-DE and EN-FR datasets. Comparison is made against standard attention and other variants that model context differently. Three transformer architectures are adopted as in~\cite{pmlr-v97-li19e}.

It is observed that local attention only achieves marginal gains over standard attention, mainly because the locality is added to the attention mechanism which hurts the full attention capacity. Area attention preserves full attention by allowing queries to attend to the whole memory. The memory is a multi-scale one to encode context of varying scales. Despite the strong BLEU scores from area attention, it is not flexible enough to model content-dependent context due to the use of fixed set of pooling sizes when constructing the multi-scale memory. Our ContextPool is able to meaningfully outperform area attention across datasets and model sizes, thanks to its adaptiveness during context pooling.

\begin{figure}[!t]
\begin{center}
\centerline{\includegraphics[width=0.85\columnwidth]{NMT}}
\caption{Performance-cost comparisons on token-level translation (EN-DE task) using the Base model. The number of layers ranges from $L=6$ to 10.}
\label{fig:NMT}
\end{center}
\vskip -0.3in
\end{figure}

Fig.~\ref{fig:NMT} further compares the above methods in terms of computation and memory complexities. Given the default number of layers $L=6$, our CP-attention not only outperforms others at the same $L$, but also strikes a better trade-off between performance and cost. For instance, our CP-attention ($L=6$) obtains a higher BLEU score 28.91 at a noticeably faster speed and lower memory than area attention, since the latter needs to maintain a multi-scale memory online. More importantly, we are able to utilize our saved compute in the form of additional layers (increasing $L$ to 7). This way, we further improve the model capacity and BLEU score, but our speed and memory remain comparable to those of area attention with only $L=6$ layers. When we continue to train a deeper model with CP, we found significantly boosted parameter efficiency over the one without CP. Interestingly, our CP-attention with $L=8$ layers obtains a similar BLEU score with the 10-layer vanilla attention model (without CP), leading to $27\%$ faster speed and $16\%$ less memory used. On the other hand, when we train shallower models, the performance gap (with vs. without CP) becomes larger,~\emph{e.g.},~$\Delta$BLEU$=$1.21 when $L=$4. This again demonstrates our improved model expressiveness.

\begin{table}[t]
\caption{BPC ($\downarrow$) and model size on enwik8 and text8 for autoregressive language modeling. The number of layers is included in the parenthesis. CP denotes the use of our ContextPool module.}
\label{tb:autoregressive}
\begin{center}
\begin{small}
\begin{tabular}{cccccc}
\toprule
\multirow{2}{*}{Model} & \multirow{2}{*}{\#Param} & \multicolumn{2}{c}{text8} & \multicolumn{2}{c}{enwik8}\\
\cmidrule(lr){3-4}
\cmidrule(lr){5-6}
 & & Dev & Test & Dev & Test\\
\midrule
T12 & 44M & - & 1.18  & -  & 1.11\\
Transformer-XL & 41M & - & -  & -  & 1.06\\
Adaptive local & 38M & 1.05 & 1.11  & 1.04  & 1.02\\
BP-Transformer & 38M & - & 1.11  & -  & 1.02\\
Longformer & 41M & 1.04 & 1.10  & 1.02  & 1.00\\
Reformer & - & - & -  & -  & 1.05\\ \midrule
CP-Transformer (12) & 39M & 1.04 & 1.09  & 1.02  & 0.99\\
CP-Transformer (14) & 44M & \textbf{1.02} & \textbf{1.07}  & \textbf{1.01}  & \textbf{0.97}\\
CP-Transformer (11) & \textbf{36M} & 1.05 & 1.11  & 1.03  & 1.01\\ \midrule
CP-Adaptive local & 39M & 1.05 & 1.10  & 1.03  & 1.01\\
CP-Longformer & 43M & 1.03 & 1.09  & 1.02  & 0.99\\
\bottomrule
\end{tabular}
\end{small}
\end{center}
\vskip -0.2in
\end{table}

Finally, we evaluate on the challenging task of character-level autoregressive language modeling (see Table~\ref{tb:autoregressive}). BPC results are reported on the Dev/Test sets of enwik8 and text8 datasets. We compare with the baseline models of T12~\cite{RfouCCGJ19} and Transformer-XL~\cite{dai-2019-transformer}, as well as four representative methods of sparse attention. Among them, Adaptive local~\cite{sukhbaatar2019} and BP-Transformer~\cite{1911-04070} use the local window as a sparsity prior, but with a learned window size and multi-scale windows respectively. Longformer~\cite{Beltagy2020Longformer} uses a combined sparsity pattern (global+local window), while Reformer~\cite{Kitaev2020Reformer} chooses to learn the patterns.

The above sparse attention methods differ from our ContextPool method in their loss of full attention capacity despite the improved efficiency. Our method on the other hand, computes full attention over ContextPooled token features. Note our context pooling function does have a locality prior, similar to existing sparsity priors based on local window. But the critical difference is that our locality prior is only applied to feature pooling, not to the following full attention process.

Table~\ref{tb:autoregressive} confirms the benefits of full attention models. Our CP module when applied to the standard 12-layer transformer, makes a strong baseline CP-Transformer (12) that has a small model size (39M parameters) but consistently outperforms the compared sparse attention methods. We are able to further lower the model size to 36M when inserting CP to a 11-layer model without sacrificing the performance much, due to the boosted model expressiveness. When the saved parameters are re-invested in constructing a deeper model (14 layers) that has comparable model size of 44M, we attain new state-of-the-art performance on both enwik8 and text8.

The bottom cell of Table~\ref{tb:autoregressive} examines if our ContextPool is complementary to sparse attention. The answer is positive given our consistent gains over two sparse attention baselines. Intuitively, sparse attention would benefit more from our expressive token features that are context-aware.

\subsection{Comparing to SOTAs on Image Classification}
Table~\ref{tb:imagenet} evaluates our CP method on ImageNet classification and compares with the state-of-the-art methods ViT~\cite{dosovitskiy2021an}, DeiT~\cite{touvron21a} and Swin-T~\cite{liu2021Swin}. It is shown that when CP is simply applied to the 12-layer ViT-B/16 model, performance gains are achieved at low overhead. When we plug CP into a smaller CP-ViT-B/16 model with 10 layers, this model can perform even comparably to ViT-L/16 despite being much more efficient. We further show CP is applicable to the Swin transformer that computes multi-scale attention with an image pyramid. Our CP method proves helpful for the two Swin-B models using different input image resolutions, and achieves a strong top1 accuracy of 85.6\%.

\begin{table}[t]
\caption{ImageNet-1K top1 classification accuracy. Throughput (images/s) is measured on a V100 GPU. CP denotes the use of our ContextPool module. The number of attention layers is included in the parenthesis.}
\label{tb:imagenet}
\begin{center}
\begin{small}
\resizebox{1.05\linewidth}{!}{
\begin{tabular}{cccccc}
\toprule
Method & image & \#Param & FLOPS & image/s & Top1 \\
\midrule
ViT-B/16 & $384^2$ & 86M & 55.4G & 85.9 & 77.9\\
ViT-L/16 & $384^2$ & 307M& 190.7G & 27.3 & 76.5\\\midrule
DeiT-S & $224^2$ & 22M & 4.6G & 940.4 & 79.8\\
DeiT-B & $224^2$ & 86M & 17.5G & 292.3 & 81.8\\
DeiT-B & $384^2$ & 86M & 55.4G & 85.9 & 83.1\\ \midrule
Swin-S & $224^2$ & 50M & 8.7G & 436.9 & 83.0\\
Swin-B & $224^2$ & 88M & 15.4G & 278.1 & 83.5\\
Swin-B & $384^2$ & 88M & 47.0G & 84.7 & 84.5\\ \midrule
CP-ViT-B/16 (12) & $384^2$ & 88M & 57.2G & 85.1 & 79.2\\
CP-ViT-B/16 (10) & $384^2$ & 75M & 48.7G & 96.1 & 76.8\\
CP-Swin-B & $224^2$ & 89M & 16.8G & 272.3 & 84.3\\
CP-Swin-B & $384^2$ & 89M & 48.9G & 81.4 & \textbf{85.6}\\
\bottomrule
\end{tabular}
}
\end{small}
\end{center}
\vskip -0.2in
\end{table}

\section{Conclusions and Future Work}
In this paper we have shown how adaptive pooling of features for a location based on context can improve the results for a transformer model, both by reducing the number of layers needed to achieve similar accuracy and by improving accuracy of models with the same number of layers. For future work we hope to apply this technique more broadly to other domains, such as speech recognition that have multi-level contextual dependencies that span different, dynamic extents. It is our hope that adaptive pooling can benefit other such domains. In addition, a common dynamic pooling mechanism across Convnets and transformers can help to simplify hybrid architectures that adapt to context, opening up new efficient design choices.

\section*{Acknowledgements}
The authors want to thank Shih-Yu Sun, Hesam Najafi Shoushtari, Kelsey Ho and many others at Apple for helpful discussions during the course of this project. We also thank the ICML reviewers for providing useful feedback.

redpajama_set_name: RedPajamaArXiv


With the restoration of the Mask, the Sidereals are once more properly visible to the Pattern Spiders – and thus once more fully a part of the great tapestry of creation, no longer protected (or victimized) by Arcane Fate. As a side benefit, with the Sidereals direct connection to the Loom and authority unobscured, a great many of the checks that the Pattern Spiders used to track and confirm Sidereal Astrology are no longer necessary – and the art has returned to it's original power.
Masque of Destiny, one Endurance, one Paradox Die per week. The user may gain a mundane identity (complete with acquaintances, paperwork, and so on) appropriate to the college and may manage all of that identities routine demands competently. The user gets +3 dice on rolls made to convince others of the validity of this identity. Those interacting with the user suffer a -3 internal penalty or -2 Defense Value (as appropriate) to disbelieve the identity. These effects fail against anyone else who is attuned to the same college.
Hand of Destiny, one Endurance per Bonus Die. The user may expend ranks in a College buying extra dice on an Astrology Success Check. A character needs to have at least one current rank in a college to produce astrological effects based on it, but need not buy extra dice with this resplendency to do so.
While Resplendencies still generally do not work against targets who are Outside of Fate or in areas where the Loom of Fate holds no sway, they are generally* NOT "Shaping Effects"; they function by manipulating how the strands of the Loom interact, and are entirely in accordance with the Laws of Creation. To a large extent, a college rank represents special attention from the Pattern Spiders of a particular constellation. While they may or may not be worth their experience point cost, it's worth noting that the Pattern Spiders are perfectly capable of paying more attention to those they favor – potentially awarding college ranks beyond the user's purchased ranks if the user pleases them, fixes causality problems smoothly, or simply offers up a lot of prayers and sacrifices. They are also capable of deciding to ignore the user – stripping away bonus ranks, or even reducing the user's effective ranks below their base level for the duration of a story.
*Effects which target creatures that are Outside of Fate, such as the Arrows of Reason, ARE Shaping Effects, since they're forcing Creation's laws to apply to outsiders who have their own realities.
But what about Pattern Bite? How can I LIVE without an occasional bout of supernatural poison?
Well, OK; Optionally you CAN overspend ranks from your colleges. Each rank you don't have, but spend anyway, gives you one point of Paradox. Otherwise this works normally.
With this revision Colleges are now considerably more useful; users don't need to spend days creating a Resplendent Destiny – which counts against the number of astrological effects you can have on yourself at any one time – to power their effects.
Since the Pattern Spiders are now sure who everyone is, an astrologer can no longer simply spam destinies (unless the War Protocols are in effect – but Autochthon and the Maidens shut them down at the end of the Primordial War). Each astrologer is thus limited to a maximum of (Essence) destinies at any one time and no single individual may be affected by more than three destinies at a time. No prayer roll is necessary (or helpful); now that the Pattern Spiders can see who you are, a Sidereal Exaltations inherent link with the Loom of Fate suffices. Each active destiny an astrologer sustains will set a new essence-star – faint or brighter according to the user's essence – burning in the heavens, allowing any other astrologer who can perceive the essence-stars of the Loom to attempt to read that influence.
An Active Astrology check requires three hours of effort. If a Sidereal is operating the Loom directly, he or she can use (Dexterity + Craft Fate). Those operating remotely may use either (Charisma + Performance) to persuade the Pattern Spiders to do most of the work or (Intelligence + Occult) to figure it out for themselves. Specialities, Excellencies, Will, and Channeled Virtues apply normally, but Stunting is ineffective; warping fate while trying impose structure on it does not work well. An astrologer may also obtain bonus dice by investing three days/seven days/months in planning and casting horoscopes for +1/+2/+3 dice or by getting the support of several/many/great numbers of relevant Gods and Exalts for +1/+2/+3 dice.
Success are spent buying the effect and the area/number of targets to be affected – although both are limited by the astrologers permanent essence. Effects cost a number of successes equal to the essence level at which they become available, the area to be affected costs one point less than that. Astrological effects continue as long as they are maintained. Unfortunately, the great Tapestry of Creation has an incredibly intricate web of connections – making it wise to reserve some successes to add a few details and guidelines as to how the desired effect comes about. In addition, directly affecting minor essence users costs +1 success, affecting major ones (Gods, Exalts, Etc) costs +2. Creatures which are resistant to Fate (Raksha) or bound to other loom-equivalents (the Calender of Setesh, Autochthon, Etc) cost +3 successes. Those which are entirely outside of fate cannot be directly affected at all.
It can take some time for a destiny to take full effect; if you wish to rush that, it's best to include a few extra successes for that purpose. Attempting to directly and negatively affect an essence-user requires an essence-roll off, with the target winning ties. Most importantly, the effect of the Destiny the user creates must be related to the college used.
ordinary person, identify trends, obtain general supplies, arrange trivial coincidences, arrange an apprenticeship.
college used by +/-1 to the tn or +/-2 dice, alter a virtue by +/-1 when used for rolls within the specialty, or roll to regain will as if awakening once per day when you succeed in something related to the specialty. Such modifiers do not stack.
notable actions, cause serious delays, set up minor threats, arrange a secure spot for a regular meeting, meet with an expert, trace someone's connections or finances, obtain rare supplies, arrange minor events, orchestrate good or bad reviews, arrange notable coincidences, schedule a "must-attend" event, get help with a notable project, arrange for fog, arrange for children for a couple, manipulate organizational procedures, turn loose trivial magical creatures, arrange for a competent bodyguard for someone.
4 Village Background-3, Destiny-5, stall an activity entirely, set up notable threats, induce major attitudes, meet with a grand master, awakened thaumaturge, or similar, start a small heresy or investigation, acquire questionable materials, plant a body, get widely quoted, arrange really unlikely coincidences, have relatives drop by, arrange a serious storm, turn loose minor magical creatures, get someone kidnapped, make major legal problems vanish, suppress major news, frame someone for a serious crime, get reasonably detailed warnings of upcoming events.
the college used by +/-2 to the tn or +/-4 dice, alter a virtue by +/-2 when used for rolls within the specialty, or roll to regain will as if awakening twice per day when you succeed in something related to the specialty. Such modifiers do not stack.
6 City Background-4, arrange a mortal's death, induce deep beliefs, set up major threats, cause someone to receive special orders, get covert operations performed, arrange fabulously unlikely events, start serious investigations, make someone a top public enemy, get detailed prophecies of mundane events, access very secure facilities.
secured locations, obtain or destroy very famous mundane things, obtain grossly illegal items, arrange incredibly unlikely events, gain blackmail material on pretty much anyone, get help with a grandiose project, arbitrarily manipulate legal proceedings, make complex falsehoods into stuff that "everyone knows", set up major threats.
arrange astoundingly unlikely coincidences, have major gods drop by to visit, arrange laws or precedents for your personal benefit, add or delete notable things from creation, arrange major natural disasters, increase or decrease the level of chaos in regions of Rakshastan.
9 Continent Cause grandiose disruptions or conflicts, obtain utterly forbidden items, arrange unbelievable coincidences (he was hit by a falling star!), bind things into Creation, erase all memory and traces of an event, set up grandiose threats (unbind the Kukla, etc).Alternatively, modify rolls in a given "specialty" related to the college used by +/-3 to the TN OR +/-6 dice, alter a virtue by +/-3 when used for rolls within the specialty, or roll to regain will as if awakening three times per day when you succeed in something related to the specialty Such modifiers do not stack.
greater manse guardian, or even an Exalt if they're suitable).
Backgrounds may be either added or neutralized, sometimes with greater or lesser ease – although they cannot exceed rank five even with modifiers until the astrologer is at Essence 10 and they cannot be reduced or enhanced. Eligible backgrounds include Allies, Artifact (if providing Raksha artifacts use Raksha totals; those are easy), Backing, Command (-1 level of effect to provide, +1 to suppress), Connections, Cult (+1 level of effect to provide, -1 level of effect to suppress), Destiny (exceptionally easy, has it's own entry on the chart above), Familiar (+2 levels to provide unless upgraded versions are used, cannot be suppressed), Followers, Henchmen, Manse (-2 levels to provide unless only granting access, cannot be suppressed), Library, Mentor (-2 levels of effect when providing Exalted or Celestial Mentors, only mortal mentors can be suppressed), Resources (-1 level of effect to provide, +1 level of effect to suppress), Spies, and Patron (-1 level of effect when providing Exalted or Celestial Patrons, only mortal patrons may be suppressed). Ineligible backgrounds include Abyssal Command, Acquaintances, Arsenal (this may be suppressed, but arranging artifacts is hard enough), Breeding, Celestial Manse, Charms, Class, Esoteric Knowledge, Face, Familiar (Demonic or Autochthonian), Family, Eidolon, Esoteric Knowledge, Hearts Blood, Infamy, Infernal Manse, Influence (this may be suppressed), Inheritance, Liege, Past Life, Salary, Savant, Sifu (this may be suppressed), Solar Bond (although an existing bond can be temporarily strengthened), Taboo, Tattoo Artifact, Underworld Manse (eligible if working with the Calender of Setesh), Unwoven Coadjutor, and Whispers.
If a non-sidereal happens to have some Pattern Spider Allies, then he or she can take ranks in the relevant college or colleges, and use "Sidereal" astrology within those limits – although their rolls are at difficulty three instead of difficulty 1.
Optionally, an astrologer may opt to give up a permanent point of essence to make his or her current astrological destinies equally permanent – although their effect and scale is reduced to a maximum of (Original Essence/2). If you're a high-essence Sidereal, and you really wish to bless the household of your most direct descendants forever or some such, go right ahead. This sort of thing usually only happens when someone is getting ready to die, but so what?
What about Censure you say? Well, Censure comes up when your fiddling around with Fate really upsets other Sidereals or the Celestial Bureaucracies. It's wise to check on who you may be upsetting before working any large-scale effects or meddling with important people.


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About QCSD
Assessment & Testing Information
Georgia Pre-Kindergarten Program
21st CCLC
Technology Trouble Ticket
Digital Home Resources
Welcome to Quitman County Schools! The Quitman County community highly values the important role education plays in building a thriving community and strongly support the school system's pursuit of excellence. We strive to recruit and retain the finest teachers in the profession. We further believe that involved parents and a supportive community are key elements in our quest to become a world-class school system.
Our Mission: Our mission is to ensure the academic success of each child, each day, with no excuses.
Our Vision: Quitman County students will be able to compete in a global society while serving our thriving local community.
Goal 1:
To increase the number of students meeting the Lexile stretch band 3% yearly by the end of FY2021 in grades K-12. (See District Improvement Plan for Strategies)
To increase the number of teachers retained in the district by 10% by the end of the FY 2021. (See District Improvement Plan for Strategies)
To increase state mandated Georgia Milestone Math scores by 3% yearly by the end of FY 2021. (See District Improvement Plan for Strategies)
To increase each school's Star Climate Rating by 1 star yearly by the end of FY 2021. (See District Improvement Plan for Strategies)
Adopted September 30, 2018, Quitman County Board of Education
The Quitman County School System provides quality educational programs for students. All schools in the system meet the Standards for Public Schools in Georgia, and are accredited by the Southern Association of Colleges and Schools and the Georgia Accrediting Commission.
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Is your dating app profile really you? Would you even want it to be?
Logo for next generation dating app
App terms of use
Time Zone Converter – Time Difference Calculator
100% Free Online Dating in Fuse, IB
The Founder Of Hinge Has Some Thoughts On Your Dating Profile
Smile, don't swipe.
'Revealed' Deleted Scene: Jason Derulo's Kind Of On Tinder
How To Not Suck At Dating Apps
To that end, you should know that many advertisers pay us a referral fee if you purchase products after clicking links or calling phone numbers on our website. We sometimes offer premium or additional placements on our website and in our marketing materials to our advertising partners. Partners may influence their position on our website, including the order in which they appear on the page. For example, when company ranking is subjective meaning two companies are very close our advertising partners may be ranked higher. If you have any specific questions while considering which product or service you may buy, feel free to reach out to us anytime. If you choose to click on the links on our site, we may receive compensation. If you don't click the links on our site or use the phone numbers listed on our site we will not be compensated. Ultimately the choice is yours. The analyses and opinions on our site are our own and our editors and staff writers are instructed to maintain editorial integrity. Our brand, ConsumersAdvocate.
By Andrew Cottrill. November 21, Then their time was up and the next uncomfortable but terribly real face would flash up on the screen. VHS repairman.
And because most dating sites ask users to give consent for their data to be used for research purposes, this online courting has played out like.
The first episode of the season follows book reviewer Maggie played by Cristin Milioti as she braves the New York dating scene and an unexpected pregnancy with the fierce support of what seems to be the only constant in her life: her doorman, Guzmin real name Guzim. The story is based on the essay of writer Julie Margaret Hogben, who currently lives in Los Angeles with her twelve-year-old daughter, Isabel. In a new interview with The New York Times , Hogben reveals that in actuality, the father of her child proposed to her after she revealed her pregnancy to him; she declined his proposal.
She also explains that, unlike in the episode, she never debated whether or not to go through with the pregnancy. Today, Hogben is still single, despite her daughter's earnest attempts to sign her up for dating apps. Episode two recounts an interview that leads to both journalist Julie played by Catherine Keener and subject Joshua Dev Patel opening up about their romantic pasts, tracing not one but two gut-wrenching tales of lost love.
In reality, there was no job interview, whirlwind love-at-first-sight, or trip to the zoo for Joshua and his love interest. Nor was there an infidelity that caused their separation. The real Joshua, Hinge founder Justin McLeod, had actually met the love of his life, Kate, in college , where they dated off and on until graduation. Kate, however, was engaged to another man at the time of McLeod's interview with Copaken, and had not spoken to him in years.
The real-life Copaken was indeed heartbroken by the boy who never showed at her Paris flat, but their upstate rendezvous was imagined. It was Copaken who found him online by accident while doing research for a book she was writing, and they did meet up, albeit for lunch on a bench in Central Park. Copaken is now in a happy relationship with a new man, who she met on Bumble, not Hinge, contrary to what her character reveals in the season finale.
However, Copaken's interview inspired McLeod to win Kate back in the wistful way the episode depicts.
What are you doing this weekend? The stunt, which is being sponsored by a " commitment-free dating app " and filmed as part of an upcoming Discovery Channel series featuring Hughes, gives the year-old rocketeer a chance to outdo himself following a successful-yet-bumpy DIY launch he completed in March Hughes had to deploy two parachutes to save himself from smashing into the desert nose-first, finally walking away with just a sore back.
Fuse is a Berlin-based startup launching the first dating app with real-life dynamics at its heart. We're here to shake up the online dating industry by matching.
The baby-faced chief executive had designed Hinge, which was a new dating app. My question was an obvious throwaway. Justin looked stricken. No one, he said, had ever asked him that in an interview. I hit Stop. Off the record, he looked relieved to unburden himself.
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Please be aware that internet transmissions are never completely private or secure and that any message or information you send using the App may be read or intercepted by others. If you experience any problems with the App, including if you think it is faulty, please contact your Authorising Entity.
We, Fuse Aware Limited of The Old Church, 48 Verulam Road, St Albans, that the content in the App or Documentation is accurate, complete or up-to-date.
This privacy policy "Policy" outlines which information we collect when you "You" or "Your" access and use the website, mobile application the "App" , products and services provided by us which, together, are referred to as the "Services" , and how we protect and use it. The Policy also wishes to make sure that You are aware of the options available to You when You access and use our Services.
Please take a moment to familiarize Yourself with the Policy before using Our Services. Feel free to email Us at help fuse. If You do not agree to this Policy or Terms of Service, please do not access or otherwise use Our Services or any information contained herein. We may not know if We collect or maintain Personal Information from persons under 21 years-of-age. It is important to remember that We only collect what We require and to use such Personal Information for the purposes stipulated in this Policy and Terms and Conditions where you are bind as a party.
Your Personal Information we collect including as follows:. Personal Information can be given by You directly for example when You register Yourself as the user of the App or automatically collected when You use the App. When You use the Services through the App on a device, we will collect and use information about you in generally similar ways and for similar purposes as when you use the Fuse website. In addition, We may collect information about your location in real time.
We may use the location information collected from your device to enhance Your use of the Fuse services by providing You with relevant content and contextual advertising. Please note that turning off location sharing may affect certain features of our services. Though We may identify your geographic location, we do not receive your Personal Information.
SKOUT is the developer of a location-based social networking and dating application and website. SKOUT was one of the first dating and mobile people discovery applications to emphasize generalized user location. SKOUT uses a cellphone's global positioning system to help users to find other users within a general radius of one another.
Catholic answers forum dating services. Fuse dating app for girls. Best free dating site reviews uk. No matchmaking for destiny walkthrough. Widowhood and.
This past weekend Kate and I stated our vows in front of our friends and family in our favorite mountain town. Thanks to all who have supported us along the way. Skip navigation! Story from Entertainment. This story begins with Copaken, a writer and producer, writing a profile on Hinge founder Justin McLeod , the founder of Hinge, for Cafe. McLeod was 30 at the time. Hinge, which is based on the idea of meeting partners through friends, had yet to become the dating app behemoth it is today.
Nearing the end of the interview, Copaken asks a question that transforms the piece from a straightforward interview into a meditation on bold chances and lost love. Copaken asks if McLeod had ever been in love. McLeod proceeds to tell her a tale of his former love, Katie Stern, now engaged to someone else. After this moment of intimacy, Copaken tells McLeod an equally wrenching love story.
Neither story ends there; the interview inspires McLeod to change his life. In the Modern Love column, Copaken describes what happens after.
An award-winning team of journalists, designers, and videographers who tell brand stories through Fast Company's distinctive lens. Leaders who are shaping the future of business in creative ways. New workplaces, new food sources, new medicine–even an entirely new economic system.
Related Pages. MeetMe. App Page. Skout: Meet. Chat. Friend. Just For Fun. SKOUT APP. Entertainment Website. The Meet Group. Internet Company.
Follow Us. Since launching in London in , Hinge has become the fastest growing dating app in the UK – with a meet-up arranged through its interface every four seconds. The key to its success? Justin first met Kate while passed out in a stairwell at a private liberal arts college in upstate New York. He had a barely-under-control substance abuse problem. She had never so much as received a detention.
FUSE is one component of an all-inclusive mobile device audit. FUSE provides an overview of a collection of apps, showing the potential data flows within that collection. FUSE combines information from the static analysis of multiple apps to give security analysts the ability to quickly see where collusion or other undesirable interaction may occur—interactions that no single-app analysis can reveal.
For those keeping score at home, those are a lot of firsts: first dating app, first man on my screen, first blind date, first love at first sight. I was.
So Ms. Copaken shares her own story as a cautionary tale: When she and her former boyfriend reconnected 20 years later, it was too late. Both were married; her ex happily so, while she eventually got a divorce. Copaken convinces Mr. McLeod to go find his former love. He does, and ultimately wins her back. You can watch a clip below, and stream the full episode here.
Below is my conversation with Ms. The interview has been edited for length and clarity. Daniel Jones: Readers are curious about Modern Love and who writes for it and why. So let me ask you: Why explore things that are private or that expose the privacy of others in such a public sphere? And catch up on all things Modern Love.
I do think, though, that technology can come to the rescue if someone is feeling lonely and in need of meaningful companionship — any day of the year. Hence, the most effective way to cope with the spirit of festivity might be to get on the dating apps bandwagon. One thing is for sure: algorithms that match you up with your significant other are more popular than ever.
Meeting Planner App for iOS. Find the Countdown App for Android Use our Time Zone data in your apps. Time and Date AS –
FUSE is the next generation dating app. Fuse is all about finding your perfect match through revealing your true personality instead of just showing some shallow "i look awesome" selfies. The app will bring people together that really do belong together. Our initial target group is young , urban, well educated and has a great taste in music, art and design. This is why they love simplicity, quality and 'real things'. We want a logo, that consists of 2 parts a a pictogram b a word mark It should be possible to use each of them either separately or combined.
Tinder perfectly shows how this works.
FUSE is the next generation dating app. Fuse is all about finding your perfect match through revealing your true personality instead of just showing some shallow "i look awesome" selfies. The app will bring people together that really do belong together. Our initial target group is young , urban, well educated and has a great taste in music, art and design.
This is why they love simplicity, quality and 'real things'.
love from decades before, and another about Justin McLeod, the founder of the dating app Hinge, whom she was interviewing for an article.
To support our nonprofit science journalism, please make a tax-deductible gift today. Are you carefully weighing every factor that makes someone a good romantic match? Not according to a study of more than 1 million interactions on a dating website published this week in the Proceedings of the National Academy of Sciences. Instead, the results indicate that you are probably looking for "deal breakers," harshly eliminating those who do not live up to your standards.
Not long ago, dating produced no data at all. People met their romantic partners through the recommendations of friends, family, or even at real-world locations known as "bars. But that's changing. Those 30 million people have generated billions of pieces of data. And because most dating sites ask users to give consent for their data to be used for research purposes, this online courting has played out like an enormous social science experiment, recording people's moment-by-moment interactions and judgments.
A team led by Elizabeth Bruch, a sociologist at the University of Michigan, Ann Arbor, tapped into this torrent of dating data. Because of a nondisclosure agreement, the researchers can't reveal the exact source of their subjects, describing it only as an "established, marriage-oriented, subscription-based dating site" from which they randomly selected people, all based in New York City.
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Still a young, organization. Administrative staff is open to new ideas, programming, and policy. Flexible scheduling allows me to go to appointments or events for my kid without using all of my vacation days. Able to do paperwork at home if you're not feeling well and don't want to use a vacation day. Staff is like family.


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Welcome To NAACP
St John The Baptist Parish, Louisiana
"VOICE FOR THE VOICELESS"
Proven Leadership bring Results
Branch 6077 Officers:
LaTunya D. Creecy, Parliamentarian
Michelle Garrison, Acting Secretary
David Weathersby II, Assistant Secretary
Larry Sorapuru II, Treasurer
Derron Cook, 2nd Vice President
Contact Us Via Email:
sjbpnaacp@gmail.com
NATIONAL ASSOCIATION FOR THE ADVANCEMENT OF THE COLORED PEOPLE
The National Association for the Advancement of Colored People (NAACP) was established in St John the Baptist Parish in 1940 and led by Edward Hall (February 1905- May 19, 2001) at age of 35 years old. Edward Hall grew up in Reserve, La. The National Association for the Advancement of Colored People has existed for 111 years to date, and its origins trace back to 1909-New York.
The former Presidents of the NAACP branch 6077 of St John the Baptist Parish are: Edward Hall, George Clark, Dale Wolfe, Michael Carter, Leonard Triplett, Robert Taylor II. Presently, Shondrell Perrilloux has been elected the first Black woman President of St John the Baptist Parish Branch.
As President, Shondrell Perrilloux is most passionate about the economic advancement of the disadvantaged and under privileged persons. Shondrell Perrilloux unique skills and abilities qualify her to lead the newly revised NAACP in St John the Baptist Parish. Among her gifts, is to be a "Voice for the Voiceless." She intends to create a Legal Defense Fund to assist people with competent legal representation to ensure civil rights are upheld. The Legal Defense Fund would be one of the very few within Louisiana NAACP chapters.
Branch 6077 has a rich history within St John Baptist Parish. Edward Hall and Thurgood Marshall removed racial barriers at the local registrar of voters. The action set a precedent that spread throughout the south. In 1944, the lawsuit filed by Edward Hall and Thurgood Marshall in the 5th Circuit Court of Appeal paved the way for black voting rights.
Today, the NAACP advocates for equality in six core areas: economic sustainability, high quality healthcare, equal treatment in the criminal justice system; voting rights/political representation and expanding youth and young adult engagement. The NAACP is not just for African Americans its for all persons of color that deal with any inequality. The goal is to provide equal opportunities for people in the community regardless of race. As of February 15, 2020 reactivation, the branch has more than 75 active members.
Branch 6077 has a primary focus to hold local government accountable to ensure that the residents of St John the Baptist Parish are given equal and fair economic opportunities. We intend to uphold the core mission of the NAACP.


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Le mont Abbott est une montagne de  d'altitude située à  de la base italienne Mario-Zucchelli. C'est le point culminant des contreforts du Nord () une ligne de collines côtières, de la Terre Victoria qui domine la baie Terra Nova en Antarctique.

Cette chaîne de collines a été cartographiée par l'équipe nord de l'expédition Terra Nova et le mont a été nommé en l'honneur du maître George P. Abbott de la Royal Navy membre de cette expédition britannique menée par Robert Falcon Scott de 1910 à 1913.

La montagne est un cône de scories basanitiques du « groupe volcanique de McMurdo » (province dite « de Melbourne »), daté de 0,628 ± 0,015 million d'années.

Notes et références

Annexes

Articles connexes
 Liste des volcans d'Antarctique

Volcan en Antarctique
Sommet de la chaîne Transantarctique
Mont Abbott

redpajama_set_name: RedPajamaWikipedia


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University of South Wales Home
Ruth Northway public CV
Ruth Northway
Professor, Faculty of Life Sciences and Education
Supervised Work (11)
Health inequalities through an ethical lens
Northway, R., 9 Aug 2019.
Helping a person with autism to overcome her fear of needles
Edwards, J. & Northway, R., 1 Dec 2011, In: Primary Health Care. 21, 10, p. 26 - 29 3 p.
Learning Disabilities 100%
Autistic Disorder 80%
autism 78%
learning disability 76%
Needles 72%
Hospital passports, patient safety and person centred care: a review of documents currently used for people with intellectual disabilities in the United Kingdom
Northway, R., Rees, S., Davies, M. & Williams, S., Dec 2017, In: Journal of Clinical Nursing. 26, 23-24, p. 5160 - 5168 9 p., 14065.
Patient Safety 88%
How Corona Virus highlights the everyday challenges of people with learning disabilities
Northway, R., Aug 2020, The Conversation.
How do people with intellectual disabilities view abuse and abusers?
Northway, R., Melsome, M., Flood, S., Bennett, D., Howarth, J. & Thomas, B., 2013, In: Journal of Intellectual Disabilities. 17, 4, p. 361-75 15 p.
abuse 66%
Identity, difference and diversity
Northway, R., 1 Jun 2017, In: Journal of Intellectual Disabilities. 21, 2, p. 101-102 2 p.
'I'm a researcher!' Working together to gain ethical approval for a participatory research study
Ham, M., Jones, N., Mansell, I., Northway, R., Price, L. & Walker, G., 1 Dec 2004, In: Journal of Intellectual Disabilities. 8, 4, p. 397-407 11 p.
Research Personnel 100%
Disabled Persons 80%
Research Ethics Committees 75%
Improving diabetes care for people with intellectual disabilities: A qualitative study exploring the perceptions and experiences of professionals in diabetes and intellectual disability services
Brown, M., Taggart, L., Karatzias, T., Truesdale, M., Walley, R., Northway, R., Macrae, S., Carey, M. & Davies, M., 1 May 2017, In: Journal of Intellectual Disability Research. 61, 5, p. 435-449 15 p.
Intellectual Disability 100%
Diabetes 93%
Qualitative Study 86%
Health Catchment Area 15%
Improving equality of healthcare for people with learning disabilities
Northway, R. & Dix, A., 18 Mar 2019, Nursing Times, 115, 4, p. 27 - 31 5 p.
Premature Mortality 47%
Inclusion of learning disability research in nursing journals: a review of 5 international journals 2013 - 2017
Marsden, D., Northway, R., Ross, S. & Statham, D., 7 Aug 2019.
Research output: Contribution to conference › Poster › peer-review
Integration and inclusion: Illusion or progress in services for disabled people?
Northway, R., Jun 1997, In: Social Policy and Administration. 31, 2, p. 157-172 16 p.
Disabled People 100%
society 53%
services 44%
Introduction to the Sociological Context of Nursing
Northway, R., 2015, Essentials of Nursing Practice. Delves-Yates, C., Elcock, K., Hall, C., Jenkins, R., Northway, R., Trenoweth, S. & Wright, W. (eds.). SAGE Publications, p. 651 - 662 12 p.
Keeping safe and providing support: A participatory survey about abuse and people with intellectual disabilities
Northway, R., Bennett, D., Melsome, M., Flood, S., Howarth, J. & Jones, R., Sep 2013, In: Journal of Policy and Practice in Intellectual Disabilities. 10, 3, p. 236-244 9 p.
Knowledge, time and change
Laying the foundations for health
Northway, R., 31 Mar 2017, In: Journal of Intellectual Disabilities. 21, 1, p. 3-4
Leadership and patient care outcomes
Northway, R., 16 Jun 2017, Leadership and change for the Health Professional. Curtis, E. A. & Cullen, J. (eds.). London: Open University Press
Learning Disability and Dementia - A Review of Recent Literature
JENKINS, ROBERT., Northway, R., Llewellyn, P., Davies, R. & Sardi, I., 1 Jan 2008, University of Glamorgan.
Research output: Book/Report › Commissioned report › peer-review
Dementia 67%
Down Syndrome 42%
Learning Disability Educational Framework for Healthcare Staff in Wales
Northway, R., Morgan, R., Oloidi, E. O., Price, K. & Churcher, C., Apr 2020, Improvement Cymru. 41 p.
Research output: Book/Report › Commissioned report
Learning Disability Health Communication Tool
Northway, R., Rees, S. & Oloidi, E. O., Apr 2020
learning disability 100%
online survey 47%
Learning Disability Nursing - Developing Professional Practice
Northway, R. & Hopes, P., 1 Sep 2022, 1st ed. St Albans: Critical Publishing. 313 p.
Listening, learning and acting: working with people who use services to plan and undertake research
Northway, R., 1 Mar 2016, In: Journal of Clinical Nursing. 25, 5-6, p. 573-574
Looking back to move forward: Reflections and projections on Journal of Intellectual Disabilities
Gates, B. & Northway, R., 1 Mar 2016, In: Journal of Intellectual Disabilities. 20, 1, p. 3-4 2 p.
Anniversaries and Special Events 100%
Meaning of life for adolescents with a physical disability in Korea: Commentary
Northway, R., 15 Jul 2003, In: Journal of Advanced Nursing. 43, 2, p. 155-157 3 p.
Meeting the health needs of children and young people with severe and profound learning disabilities: the contribution of the school nurse and community learning disability nurse.
Spargo, D. & Northway, R., 1 Oct 2011, In: SLD Experience. 61, p. 19 - 24 5 p.
Meeting the health needs of older people with intellectual disabilities: exploring the experiences of residential social care staff
Northway, R., Holland-Hart, D. & Jenkins, R., 31 Aug 2016, In: Health and Social Care in the Community. 25, 3, p. 923 - 931
staff 54%
Methods that bridge quality and quantity
Northway, R., Jul 2013, In: Nurse Researcher. 20, 6, p. 4-5 2 p.
Moving models: Leading through constant change
Northway, R., 11 Apr 2019, Leadership for Intellectual Disability Service: Motivating Change and Improvement. Sheerin, F. & Curtis, E. A. (eds.). Taylor & Francis, p. 23-44 22 p.
Effective Leadership 43%
Community-based 40%
Factors 24%
Must do better : the lessons to be learned from Winterbourne View
Jenkins, R. & Northway, R., 1 Oct 2012, In: Learning Disability Practice. 15, 8, p. 26 - 28 2 p.
abuse 100%
event 50%
Networking in practice
Northway, R., Jun 2017, Nursing in Intellectual Disabilities: Irish and International Perspectives. Keenan, P. & Doody, O. (eds.). Dublin, Ireland: Nursing Network Intellectual Disability, Ireland, p. 57 - 67 11 p. Chapter 8
Nursing 100%
Professional Practice 49%
Nursing care at end of life: a UK based survey of the deaths of people living in care settings for people with Intellectual Disability
Todd, S., Northway, R., Morgan, R., Hopes, P., Shearn, J., Worth, E., Bernal, J. & Unit, K., 3 Jul 2018, (E-pub ahead of print) In: Journal of Research in Nursing. 17 p., 1744987118780919.
Nursing Care 93%
Participative Approaches to Research
Northway, R., 1 Jan 2008, Churchill Livingstone. 11 p.
Participatory research, people with intellectual disabilities and ethical approval: Making reasonable adjustments to enable participation
Northway, R., Howarth, J. & Evans, L., 27 Jan 2015, In: Journal of Clinical Nursing. 24, p. 573-581 9 p.
Participatory Research. Part 1: Key features and underlying philosophy
Northway, R., 2017, In: International Journal of Therapy and Rehabilitation. 24, 10, p. 443- 448 6 p.
balance of power 100%
research process 83%
Community Participation 82%
commitment 57%
participation 46%
Northway, R., 1 Apr 2010, In: International Journal of Therapy and Rehabilitation. 17, 4, p. 174 - 179 5 p.
Participatory research. Part 2: Practical considerations
Northway, R., 8 Nov 2017, In: International Journal of Therapy and Rehabilitation. 24, 11, p. 488-493
Northway, R., 1 May 2010, In: International Journal of Therapy and Rehabilitation. 17, 5, p. 226 - 231 5 p.
Patient, service user, family and carer perspectives
Northway, R. & Emerson, K., 2015, Essential of Nursing Practice. Delves-Yates, C., Elcock, K., Hall, C., Jenkins, R., Northway, R., Trenoweth, S. & Wright, W. (eds.). SAGE Publications, p. 185- 197 12 p.
Perspectives: The health of people with intellectual disabilities: realising the future potential of of all nurses and nursing to reducing health disparities
Northway, R., 17 Dec 2017, In: Journal of Research in Nursing. 22, 8, p. 637 - 640 4 p.
Planning for impact from question to dissemination (Symposium)
Northway, R., Rees, S. & Oloidi, E. O., 6 Sep 2022.
Research output: Contribution to conference › Other › peer-review
'Policies don't protect people, It's how they are implemented': Policy and practice in protecting people with learning disabilities from abuse
Northway, R., Davies, R., Mansell, I. & Jenkins, R., Feb 2007, In: Social Policy and Adminstration. 41, 1, p. 86-104 19 p.
disability 87%
Poverty as a practice issue for learning disability nurses
Northway, R., 1 Jan 2001, In: British Journal of Nursing. 10, 18, p. 1186-1192 7 p.
Poverty 87%
Mental Health 12%
Health 7%
Promoting collaboration within community health care nursing
Northway, R. & Walker, G., 1 Dec 1999, In: Journal of Community Nursing. 13, 4, p. XI-XII
Community Health Nursing 100%
Community Health Services 78%
Gills 77%
Nursing 52%
Quality of life as a concept for developing learning disability nursing practice?
Northway, R. & Jenkins, R., 1 Jan 2003, In: Journal of Clinical Nursing. 12, 1, p. 57-66 10 p.
Reflections on labelling and counting
Northway, R., 1 Mar 2018, In: Journal of Intellectual Disabilities. 22, 1, p. 3-4 2 p.
Relationships in an inclusive research team
Brown, F. & Northway, R., 6 Aug 2019.
Researching learning disability nursing
Northway, R., 1 Jan 2009, Learning Disability Nursing Practice. p. 155-184
Researching policy and practice to safeguard people with intellectual disabilities from abuse: Some methodological challenges
Northway, R., Jenkins, R., Jones, V., Howarth, J. & Hodges, Z., 10 Sep 2013, In: Journal of Policy and Practice in Intellectual Disabilities. 10, 3, p. 188-195 8 p.
Research teaching in learning disability nursing: Exploring the views of student and registered learning disability nurses
Northway, R., Parker, M., James, N., Davies, L., Johnson, K. & Wilson, S., 1 Dec 2015, In: Nurse Education Today. 35, 12, p. 1155-1160 6 p.
teaching research 86%
Review: Equality and access to general healthcare for people with learning disabilities: Reality or rhetoric?
Northway, R., 9 Jul 2010, In: Journal of Research in Nursing. 15, 4, p. 363-364 2 p.
Northway, R., 29 Nov 2016, In: Journal of Intellectual Disabilities. 20, 4, p. 311-312
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