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Avtandil Socks
Avtandil Socks

Attention



If a complete request has not been sent to the server, theclient MUST send the next packet with both ignore bit (0x02) and EOM bit (0x01)set in the status to cancel the request. An example of this behavior is if oneor more packets have been sent but the last packet with EOM bit (0x01) set instatus has not been sent. Setting the ignore and EOM bits terminates thecurrent request, and the server MUST ignore the current request. When theignore and EOM bits are set, the server does not send an attentionacknowledgment, but instead returns a table response witha single DONE token (section 2.2.7.6) that has astatus of DONE_ERROR to indicate that the incoming request was ignored. Formore details about the packet header status code, see section 2.2.3.1.2.




attention


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ADHD is one of the most common neurodevelopmental disorders of childhood. It is usually first diagnosed in childhood and often lasts into adulthood. Children with ADHD may have trouble paying attention, controlling impulsive behaviors (may act without thinking about what the result will be), or be overly active.


Attention-deficit/hyperactivity disorder (ADHD) is marked by an ongoing pattern of inattention and/or hyperactivity-impulsivity that interferes with functioning or development. People with ADHD experience an ongoing pattern of the following types of symptoms:


ADHD symptoms can appear as early as between the ages of 3 and 6 and can continue through adolescence and adulthood. Symptoms of ADHD can be mistaken for emotional or disciplinary problems or missed entirely in children who primarily have symptoms of inattention, leading to a delay in diagnosis. Adults with undiagnosed ADHD may have a history of poor academic performance, problems at work, or difficult or failed relationships.


ADHD symptoms can change over time as a person ages. In young children with ADHD, hyperactivity-impulsivity is the most predominant symptom. As a child reaches elementary school, the symptom of inattention may become more prominent and cause the child to struggle academically. In adolescence, hyperactivity seems to lessen and symptoms may more likely include feelings of restlessness or fidgeting, but inattention and impulsivity may remain. Many adolescents with ADHD also struggle with relationships and antisocial behaviors. Inattention, restlessness, and impulsivity tend to persist into adulthood.


ADHD is more common in males than females, and females with ADHD are more likely to primarily have inattention symptoms. People with ADHD often have other conditions, such as learning disabilities, anxiety disorder, conduct disorder, depression, and substance use disorder.


Non-stimulants. A few other ADHD medications are non-stimulants. These medications take longer to start working than stimulants, but can also improve focus, attention, and impulsivity in a person with ADHD. Doctors may prescribe a non-stimulant: when a person has bothersome side effects from stimulants, when a stimulant was not effective, or in combination with a stimulant to increase effectiveness.


This brochure provides information about attention-deficit/hyperactivity disorder (ADHD) in children and teens including symptoms, how it is diagnosed, causes, treatment options, and helpful resources.


Mindfulness is defined as paying attention in the present moment. We investigate the hypothesis that mindfulness training may alter or enhance specific aspects of attention. We examined three functionally and neuroanatomically distinct but overlapping attentional subsystems: alerting, orienting, and conflict monitoring. Functioning of each subsystem was indexed by performance on the Attention Network Test. Two types of mindfulness training (MT) programs were examined, and behavioral testing was conducted on participants before (Time 1) and after (Time 2) training. One training group consisted of individuals naive to mindfulness techniques who participated in an 8-week mindfulness-based stress reduction (MBSR) course that emphasized the development of concentrative meditation skills. The other training group consisted of individuals experienced in concentrative meditation techniques who participated in a 1-month intensive mindfulness retreat. Performance of these groups was compared with that of control participants who were meditation naive and received no MT. At Time 1, the participants in the retreat group demonstrated improved conflict monitoring performance relative to those in the MBSR and control groups. At Time 2, the participants in the MBSR course demonstrated significantly improved orienting in comparison with the control and retreat participants. In contrast, the participants in the retreat group demonstrated altered performance on the alerting component, with improvements in exogenous stimulus detection in comparison with the control and MBSR participants. The groups did not differ in conflict monitoring performance at Time 2. These results suggest that mindfulness training may improve attention-related behavioral responses by enhancing functioning of specific subcomponents of attention. Whereas participation in the MBSR course improved the ability to endogenously orient attention, retreat participation appeared to allow for the development and emergence of receptive attentional skills, which improved exogenous alerting-related process.


A critical and apparent disadvantage of this fixed-length context vector design is incapability of remembering long sentences. Often it has forgotten the first part once it completes processing the whole input. The attention mechanism was born (Bahdanau et al., 2015) to resolve this problem.


With the help of the attention, the dependencies between source and target sequences are not restricted by the in-between distance anymore! Given the big improvement by attention in machine translation, it soon got extended into the computer vision field (Xu et al. 2015) and people started exploring various other forms of attention mechanisms (Luong, et al., 2015; Britz et al., 2017; Vaswani, et al., 2017).


Self-attention, also known as intra-attention, is an attention mechanism relating different positions of a single sequence in order to compute a representation of the same sequence. It has been shown to be very useful in machine reading, abstractive summarization, or image description generation.


The long short-term memory network paper used self-attention to do machine reading. In the example below, the self-attention mechanism enables us to learn the correlation between the current words and the previous part of the sentence.


In the show, attend and tell paper, attention mechanism is applied to images to generate captions. The image is first encoded by a CNN to extract features. Then a LSTM decoder consumes the convolution features to produce descriptive words one by one, where the weights are learned through attention. The visualization of the attention weights clearly demonstrates which regions of the image the model is paying attention to so as to output a certain word.


When reading from the memory at time t, an attention vector of size $N$, $\mathbfw_t$ controls how much attention to assign to different memory locations (matrix rows). The read vector $\mathbfr_t$ is a sum weighted by attention intensity:


The content-addressing creates attention vectors based on the similarity between the key vector $\mathbfk_t$ extracted by the controller from the input and memory rows. The content-based attention scores are computed as cosine similarity and then normalized by softmax. In addition, NTM adds a strength multiplier $\beta_t$ to amplify or attenuate the focus of the distribution.


The location-based addressing sums up the values at different positions in the attention vector, weighted by a weighting distribution over allowable integer shifts. It is equivalent to a 1-d convolution with a kernel $\mathbfs_t(.)$, a function of the position offset. There are multiple ways to define this distribution. See Fig. 11. for inspiration.


The complete process of generating the attention vector $\mathbfw_t$ at time step t is illustrated in Fig. 12. All the parameters produced by the controller are unique for each head. If there are multiple read and write heads in parallel, the controller would output multiple sets.


In problems like sorting or travelling salesman, both input and output are sequential data. Unfortunately, they cannot be easily solved by classic seq-2-seq or NMT models, given that the discrete categories of output elements are not determined in advance, but depends on the variable input size. The Pointer Net (Ptr-Net; Vinyals, et al. 2015) is proposed to resolve this type of problems: When the output elements correspond to positions in an input sequence. Rather than using attention to blend hidden units of an encoder into a context vector (See Fig. 8), the Pointer Net applies attention over the input elements to pick one as the output at each decoder step.


The Ptr-Net outputs a sequence of integer indices, $\boldsymbolc = (c_1, \dots, c_m)$ given a sequence of input vectors $\boldsymbolx = (x_1, \dots, x_n)$ and $1 \leq c_i \leq n$. The model still embraces an encoder-decoder framework. The encoder and decoder hidden states are denoted as $(\boldsymbolh_1, \dots, \boldsymbolh_n)$ and $(\boldsymbols_1, \dots, \boldsymbols_m)$, respectively. Note that $\mathbfs_i$ is the output gate after cell activation in the decoder. The Ptr-Net applies additive attention between states and then normalizes it by softmax to model the output conditional probability:


The attention mechanism is simplified, as Ptr-Net does not blend the encoder states into the output with attention weights. In this way, the output only responds to the positions but not the input content.


The major component in the transformer is the unit of multi-head self-attention mechanism. The transformer views the encoded representation of the input as a set of key-value pairs, $(\mathbfK, \mathbfV)$, both of dimension $n$ (input sequence length); in the context of NMT, both the keys and values are the encoder hidden states. In the decoder, the previous output is compressed into a query ($\mathbfQ$ of dimension $m$) and the next output is produced by mapping this query and the set of keys and values.


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