source: CRF

title: THE GOLDILOCKS PRINCIPLE: READING CHILDREN’S BOOKS WITH EXPLICIT MEMORY REPRESENTATIONS

authors:
Felix Hill
Antoine Bordes
Sumit Chopra
Jason Weston

heading: 1 INTRODUCTION

text:
Humans do not interpret language in isolation. The context in which words and sentences are understood, whether a conversation, book chapter or road sign, plays an important role in human comprehension (Altmann & Steedman, 1988; Binder & Desai, 2011). In this work, we investigate how well statistical models can exploit such wider contexts to make predictions about natural language.
Our analysis is based on a new benchmark dataset (The Children’s Book Test or CBT) designed to test the role of memory and context in language processing and understanding. The test requires predictions about different types of missing words in children’s books, given both nearby words and a wider context from the book. Humans taking the test predict all types of word with similar levels of accuracy. However, they rely on the wider context to make accurate predictions about named entities or nouns, whereas it is unimportant when predicting higher-frequency verbs or prepositions.
As we show, state-of-the-art language modelling architectures, Recurrent Neural Networks (RNNs) with Long-Short Term Memory (LSTMs), perform differently to humans on this task. They are excellent predictors of prepositions and verbs, but lag far behind humans when predicting nouns or named entities. This is because their predictions are based almost exclusively on local contexts. In contrast, Memory Networks (Weston et al., 2015b) are one of a class of ‘contextual models’ that can interpret language at a given point in text conditioned directly on both local information and explicit representation of the wider context. On the CBT, Memory Networks designed in a particular way can exploit this information to achieve markedly better prediction of named-entities and nouns than conventional language models. This is important for applications that require coherent semantic processing and/or language generation, since nouns and entities typically encode much of the important semantic information in language.
∗The majority of this work was done while FH was at Facebook AI Research, and was completed at his current affiliation, University of Cambridge, Computer Laboratory, Cambridge, UK.
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However, not all contextual models reach this level of performance. We find the way in which wider context is represented in memory to be critical. If memories are encoded from a small window around important words in the context, they are more useful to the network than those encoding either single words or entire sentences; an effect we have nicknamed the Goldilocks Principle after the well-known English fairytale (Hassall, 1904). In the case of Memory Networks, we also find that self-supervised training of the memory access mechanism yields a clear performance boost when predicting named entities, a class of word that has typically posed problems for neural language models. Indeed, we train a Memory Network with these design features to beat the best reported performance on the CNN QA test of entity prediction from news articles (Hermann et al., 2015).

heading: 2 THE CHILDREN’S BOOK TEST

text:
The experiments in this paper are based on a new resource, the Children’s Book Test, designed to measure directly how well language models can exploit wider linguistic context. The CBT is built from books that are freely available thanks to Project Gutenberg.1 Using children’s books guarantees a clear narrative structure, which can make the role of context more salient. After allocating books to either training, validation or test sets, we formed example ‘questions’ (denoted x) from chapters in the book by enumerating 21 consecutive sentences.
In each question, the first 20 sentences form the context (denoted S), and a word (denoted a) is removed from the 21st sentence, which becomes the query (denoted q). Models must identify the answer word a among a selection of 10 candidate answers (denoted C) appearing in the context sentences and the query. Thus, for a question answer pair (x, a): x = (q, S, C); S is an ordered list of sentences; q is a sentence (an ordered list q = q1, . . . ql of words) containing a missing word symbol; C is a bag of unique words such that a ∈ C, its cardinality |C| is 10 and every candidate word w ∈ C is such that w ∈ q ∪ S. An example question is given in Figure 1.
For finer-grained analyses, we evaluated four classes of question by removing distinct types of word: Named Entities, (Common) Nouns, Verbs and Prepositions (based on output from the POS tagger and named-entity-recogniser in the Stanford Core NLP Toolkit (Manning et al., 2014)). For a given question class, the nine incorrect candidates are selected at random from words in the context having the same type as the answer. The exact number of questions in the training, validation and test sets is shown in Table 1. Full details of the candidate selection algorithm (e.g. how candidates are selected if there are insufficient words of a given type in the context) can be found with the dataset.2
1https://www.gutenberg.org/ 2The dataset can be downloaded from http://fb.ai/babi/.
Classical language modelling evaluations are based on average perplexity across all words in a text. They therefore place proportionally more emphasis on accurate prediction of frequent words such as prepositions and articles than the less frequent words that transmit the bulk of the meaning in language (Baayen & Lieber, 1996). In contrast, because the CBT allows focused analyses on semantic content-bearing words, it should a better proxy for how well a language model can lend semantic coherence to applications including machine translation, dialogue and question-answering systems.

heading: 2.1 RELATED RESOURCES

text:
There are clear parallels between the CBT and the Microsoft Research Sentence Completion Challenge (MSRCC) (Zweig & Burges, 2011), which is also based on Project Gutenberg (but not children’s books, specifically). A fundamental difference is that, where examples in the MSRCC are made of a single sentence, each query in the CBT comes with a wider context. This tests the sensitivity of language models to semantic coherence beyond sentence boundaries. The CBT is also larger than the MRSCC (10,000 vs 1,040 test questions), requires models to select from more candidates on each question (10 vs 5), covers missing words of different (POS) types and contains large training and validation sets that match the form of the test set.
There are also similarities between the CBT and the CNN/Daily Mail (CNN QA) dataset recently released by Hermann et al. (2015). This task requires models to identify missing entities from bulletpoint summaries of online news articles. The CNN QA task therefore focuses more on paraphrasing parts of a text, rather than making inferences and predictions from contexts as in the CBT. It also differs in that all named entities in both questions and articles are anonymised so that models cannot apply knowledge that is not apparent from the article. We do not anonymise entities in the CBT, as we hope to incentivise models that can apply background knowledge and information from immediate and wider contexts to the language understanding problem.3 At the same time, the CBT can be used as a benchmark for general-purpose language models whose downstream application is semantically focused generation, prediction or correction. The CBT is also similar to the MCTest of machine comprehension (Richardson et al., 2013), in which children’s stories written by annotators are accompanied by four multiple-choice questions. However, it is very difficult to train statistical models only on MCTest because its training set consists of only 300 examples.

heading: 3 STUDYING MEMORY REPRESENTATION WITH MEMORY NETWORKS

text: Memory Networks (Weston et al., 2015b) have shown promising performance at various tasks such as reasoning on the bAbI tasks (Weston et al., 2015a) or language modelling (Sukhbaatar et al., 2015). Applying them on the CBT enables us to examine the impact of various ways of encoding context on their semantic processing ability over naturally occurring language.

heading: 3.1 ENCODING MEMORIES AND QUERIES

text:
Context sentences of S are encoded into memories, denoted mi, using a feature-map φ(s) mapping sequences of words s ∈ S from the context to one-hot representations in [0, 1]d, where d is typically the size of the word vocabulary. We considered several formats for storing the phrases s:
• Lexical memory: Each word occupies a separate slot in the memory (each phrase s is a single word and φ(s) has only one non-zero feature). To encode word order, time features are added as embeddings indicating the index of each memory, following Sukhbaatar et al. (2015).
3See Appendix D for a sense of how anonymisation changes the CBT.
• Window memory: Each phrase s corresponds to a window of text from the context S centred on an individual mention of a candidate c in S. Hence, memory slots are filled using windows of words {wi−(b−1)/2 . . . wi . . . wi+(b−1)/2} where wi ∈ C is an instance of one of the candidate words in the question. Note that the number of phrases s is typically greater than |C| since candidates can occur multiple times in S. The window size b is tuned on the validation set. We experimented with encoding as a standard bag-of-words, or by having one dictionary per window position, where the latter performed best.
• Sentential memory: This setting follows the original implementation of Memory Networks for the bAbI tasks where the phrases s correspond to complete sentences of S. For the CBT, this means that each question yields exactly 20 memories. We also use Positional Encoding (PE) as introduced by Sukhbaatar et al. (2015) to encode the word positions.
The order of occurrence of memories is less important for sentential and window formats than for lexical memory. So, instead of using a full embedding for each time index, we simply use a scalar value which indicates the position in the passage, ranging from 1 to the number of memories. An additional parameter (tuned on the validation set) scales the importance of this feature. As we show in Appendix C, time features only gave a marginal performance boost in those cases.
For sentential and window memory formats, queries are encoded in a similar way to the memories: as a bag-of-words representation of the whole sentence and a window of size b centred around the missing word position respectively. For the lexical memory, memories are made of the n words preceding the word to be predicted, whether these n words come from the context or from the query, and the query embedding is set to a constant vector 0.1.

heading: 3.2 END-TO-END MEMORY NETWORKS

text:
The MemN2N architecture, introduced by Sukhbaatar et al. (2015), allows for a direct training of Memory Networks through backpropagation, and consists of two main steps.
First, ‘supporting memories’, those useful to find the correct answer to the query q, are retrieved. This is done by embedding both the query and all memories into a single space of dimension p using an embedding matrix A ∈ Rp×d yielding the query embedding q = Aφ(q) and memory embeddings {ci = Aφ(si)}i=1,...n, with n the number of memories.The match between q and each memory ci in the embedding space is fed through a softmax layer giving a distribution {αi}i=1,...n of matching scores defined by αi = ec > i q/ ∑ j e
c>j q. These weights are used to return a weighted average of memories as the first supporting memory:4
mo1 = ∑
i=1...n
αimi , (1)
where {mi}i=1,...n is a set of memory embeddings obtained in the same way as the ci, but using another embedding matrix B ∈ Rp×d. A characteristic of Memory Networks is their ability to perform several hops in the memory before returning an answer. Hence the above process can be repeated K times by recursively using qk = qk−1 + mok−1 instead of the original q1 = q. There are several ways of connecting the layers corresponding to distinct hops. We chose to share the embedding matrices A and B across all layers and add a linear mapping across hops, that is qk = Hqk−1 + mok−1 with H ∈ Rp×p. For the lexical memory setting, we also applied ReLU operations to half of the units in each layer following Sukhbaatar et al. (2015).5
In a second stage, an answer distribution â is returned given K retrieved memories mo1, . . .moK and the query q:
â = softmax(UqK+1). (2)
Here, U ∈ Rd×p is a separate weight matrix that can potentially be tied with A, and â ∈ Rd is a distribution over the whole vocabulary. The predicted answer â among candidates is then simply â = argmaxw∈C â(c), with â(w) indicating the probability of word w in â. For the lexical memory
4Such a weighted average over memories can also be understood as an attention mechanism. 5For the lexical memory we use the code available at https://github.com/facebook/MemNN.
variant, â is selected not only by using the probability of each of the ten candidate words, but also of any words that follow the missing word marker in the query.
During training, â is used to minimise a standard cross-entropy loss with the true label a against all other words in the dictionary, and optimization is carried out using stochastic gradient descent (SGD). We did not train the model to discern candidates in the question directly, e.g. with a ranking loss, and this promising approach is left as future work. Extra experimental details and hyperparameters are given in Appendix A.

heading: 3.3 SELF-SUPERVISION FOR WINDOW MEMORIES

text:
After initial experiments, we observed that the capacity to execute multiple hops in accessing memories was only beneficial in the lexical memory model. We therefore also tried a simpler, single-hop Memory Network, i.e. using a single memory to answer, that exploits a stronger signal for learning memory access. A related approach was successfully applied by Bordes et al. (2015) to question answering about knowledge bases.
Memory supervision (knowing which memories to attend to) is not provided at training time but is inferred automatically using the following procedure: since we know the correct answer during training, we hypothesize the correct supporting memory to be among the window memories whose corresponding candidate is the correct answer. In the common case where more than one memory contains the correct answer, the model picks the single memory m̃ that is already scored highest by itself, i.e. scored highest by the query in the embedding space defined by A.6
We train by making gradient steps using SGD to force the model, for each example, to give a higher score to the supporting memory m̃ relative to any other memory from any other candidate. Instead of using eq (1), the model selects its top relevant memory using:
mo1 = argmax i=1,...n
c>i q . (3)
If mo1 happens to be different from m̃, then the model is updated.
At test time, the model scores each candidate not only with the score of its highest scoring memory (returned by eq (3)) but with the sum of the scores of all its corresponding windows after passing all scores through a softmax. In other words, the score of a candidate is defined by the sum of the αi (as used in eq (1)) of its corresponding windows. This relaxes the effects of the max operation and allows for all windows associated with a candidate to contribute some information about that candidate. As shown in the ablation study in Appendix C, this results in slightly better performance on the CNN QA benchmark compared to hard selection at test time.
Note that self-supervised Memory Networks do not exploit any new information beyond the training data. The approach can be understood as a way of achieving hard attention over memories, to contrast with the soft attention-style selection described in Section 3.2. Hard attention yields significant improvements in image captioning (Xu et al., 2015). However, where Xu et al. (2015) use the REINFORCE algorithm (Williams, 1992) to train through the max of eq (3), our self-supervision heuristic permits direct backpropagation.

heading: 4 BASELINE AND COMPARISON MODELS

text: In addition to memory network variants, we also applied many different types of language modelling and machine reading architectures to the CBT.

heading: 4.1 NON-LEARNING BASELINES

text:
We implemented two simple baselines based on word frequencies. For the first, we selected the most frequent candidate in the entire training corpus. In the second, for a given question we selected the most frequent candidate in its context. In both cases we broke ties with a random choice.
We also tried two more sophisticated ways to rank the candidates based on no learning from the training data. The first is the ‘sliding window’ baseline applied to the MCTest by Richardson et al.
6TF-IDF distance worked almost as well in our experiments, but a random choice over positives did not.
(2013). In this method, ten ‘windows’ of the query concatenated with each possible candidate are slid across the context word-by-word, overlapping with a different subsequence at each position. The overlap score at a given position is simply word-overlap weighted TFIDF-style based on frequencies in the context (to emphasize less frequent words). The chosen candidate corresponds to the window that achieves the maximum single overlap score for any position. Ties are broken randomly.
The second method is the word distance benchmark applied by Hermann et al. (2015). For a given instance of a candidate wi in the context, the query q is ‘superimposed’ on the context so that the missing word lines up with wi, defining a subsequence s of the context. For each word qi in q, an alignment penalty P = min(minj=1...|s|{|i − j| : sj = qi},m) is incurred. The model predicts the candidate with the instance in the context that incurs the lowest alignment penalty. We tuned the maximum single penalty m = 5 on the validation data.

heading: 4.2 N-GRAM LANGUAGE MODELS

text: We trained an n-gram language model using the KenLM toolkit (Heafield et al., 2013). We used Knesser-Ney smoothing, and a window size of 5, which performed best on the validation set. We also compare with a variant of language model with cache (Kuhn & De Mori, 1990), where we linearly interpolate the n-gram model probabilities with unigram probabilities computed on the context.

heading: 4.3 SUPERVISED EMBEDDING MODELS

text:
To directly test how much of the CBT can be resolved by good quality dense representations of words (word embeddings), we implement a supervised embedding model similar to that of (Weston et al., 2010). In these models we learn both input and output embedding matrices A, B ∈ Rp×d for each word in the vocabulary (p is still the embedding dimension and d the vocabulary size). For a given input passage q and possible answer word w, the score is computed as S(q, w) = φ(q)A>Bφ(w), with φ the feature function defined in Section 3. These models can be considered as lobotomised Memory Networks with zero hops, i.e. the attention over the memory component is removed.
We encode various parts of the question as the input passage: the entire context + query, just the query, a sub-sequence of the query defined by a window of maximum b words centred around the missing word, and a version (window + position) in which we use a different embedding matrix for encoding each position of the window. We tune the window-size d = 5 on the validation set.

heading: 4.4 RECURRENT LANGUAGE MODELS

text:
We trained probabilistic RNN language models with LSTM activation units on the training stories (5.5M words of text) using minibatch SGD to maximise the negative log-likelihood of the next word. Hyper-parameters were tuned on the validation set. The best model had both hidden layer and word embeddings of dimension 512. When answering the questions in the CBT, we allow one variant of this model (context + query) to ‘burn in’ by reading the entire context followed by the query and another version to read only the query itself (and thus have no access to the context). Unlike the canonical language-modelling task, all models have access to the query words after the missing word (i.e if k is the position of the missing word, we rank candidate c based on p(q1 . . . qk−1, c, qk+1 . . . ql) rather than simply p(q1 . . . qk−1, c)).
Mikolov & Zweig (2012) previously observed performance boosts for recurrent language models by adding the capacity to jointly learn a document-level representation. We similarly apply a contextbased recurrent model to our language-modelling tasks, but opt for the convolutional presentation of the context applied by Rush et al. (2015) for summarisation. Our Contextual LSTM (CLSTM) learns a convolutional attention over windows of the context given the objective of predicting all words in the query. We tuned the window size (w = 5) on the validation set. As with the standard LSTM, we trained the CLSTM on the running-text of the CBT training set (rather than the structured query and context format) since this proved much more effective.

heading: 4.5 HUMAN PERFORMANCE

text:
We recruited 15 native English speakers to attempt a randomly-selected 10% from each question type of the CBT, in two modes either with question only or with question+context (shown to different
annotators), giving 2000 answers in total. To our knowledge, this is the first time human performance has been quantified on a language modelling task based on different word types and context lengths.

heading: 4.6 OTHER RELATED APPROACHES

text:
The idea of conditioning language models on extra-sentential context is not new. Access to document-level features can improve both classical language models (Mikolov & Zweig, 2012) and word embeddings (Huang et al., 2012). Unlike the present work, these studies did not explore different representation strategies for the wider context or their effect on interpreting and predicting specific word types.
The original Memory Networks (Weston et al., 2015b) used self-supervised memory access and were applied to question-answering tasks in which language was generated to describe the content of knowledge bases or simulated worlds. Sukhbaatar et al. (2015) and Kumar et al. (2015) trained Memory Networks with RNN components end-to-end with soft memory access, and applied them to additional language tasks. The attention-based reading models of Hermann et al. (2015) also have many commonalities with Memory Networks, differing in word representation choices and attention procedures. Both Kumar et al. (2015) and Hermann et al. (2015) propose bidirectional RNNs as a way of representing previously read text. Our experiments in Section 5 provide a possible explanation for why this is an effective strategy for semantically-focused language processing: bidirectional RNNs naturally focus on small windows of text in similar way to window-based Memory Networks.
Other recent papers have proposed RNN-like architectures with new ways of reading, storing and updating information to improve their capacity to learn algorithmic or syntactic patterns (Joulin & Mikolov, 2015; Dyer et al., 2015; Grefenstette et al., 2015). While we do not study these models in the present work, the CBT would be ideally suited for testing this class of model on semanticallyfocused language modelling.

heading: 5 RESULTS

text:
Modelling syntactic flow In general, there is a clear difference in model performance according to the type of word to be predicted. Our main results in Table 2 show conventional language models are very good at predicting prepositions and verbs, but less good at predicting named entities and nouns. Among these language models, and in keeping with established results, RNNs with LSTMs demonstrate a small gain on n-gram models across the board, except for named entities where the cache is beneficial. In fact, LSTM models are better than humans at predicting prepositions, which suggests that there are cases in which several of the candidate prepositions are ‘correct’, but annotators prefer the less frequent one. Even more surprisingly, when only local context (the query) is available, both LSTMs and n-gram models predict verbs more accurately than humans. This may be because the models are better attuned to the distribution of verbs in children’s books, whereas
humans are unhelpfully influenced by their wider knowledge of all language styles.7 When access to the full context is available, humans do predict verbs with slightly greater accuracy than RNNs.
Capturing semantic coherence The best performing Memory Networks predict common nouns and named entities more accurately than conventional language models. Clearly, in doing so, these models rely on access to the wider context (the supervised EMBEDDING MODEL (QUERY), which is equivalent to the memory network but with no contextual memory, performs poorly in this regard). The fact that LSTMs without attention perform similarly on nouns and named entities whether or not the context is available confirms that they do not effectively exploit this context. This may be a symptom of the difficulty of storing and retaining information across large numbers of time steps that has been previously observed in recurrent networks (See e.g. Bengio et al. (1994)).
Getting memory representations ‘just right’ Not all memory networks that we trained exploited the context to achieve decent prediction of nouns and named entities. For instance, when each sentence in the context is stored as an ordered sequence of word embeddings (sentence mem + PE), performance is quite poor in general. Encoding the context as an unbroken sequence of individual words (lexical memory) works well for capturing prepositions and verbs, but is less effective with nouns and entities. In contrast, window memories centred around the candidate words are more useful than either word-level or sentence-level memories when predicting named entities and nouns.
Self-supervised memory retrieval The window-based Memory Network with self-supervision (in which a hard attention selection is made among window memories during training) outperforms all others at predicting named entities and common nouns. Examples of predictions made by this model for two CBT questions are shown in Figure 2. It is notable that this model is able to achieve the strongest performance with only a simple window-based strategy for representing questions.

heading: 5.1 NEWS ARTICLE QUESTION ANSWERING

text:
To examine how well our conclusions generalise to different machine reading tasks and language styles, we also tested the best-performing Memory Networks on the CNN QA task (Hermann et al., 2015).8 This dataset consists of 93k news articles from the CNN website, each coupled with a question derived from a bullet point summary accompanying the article, and a single-word answer. The answer is always a named entity, and all named entities in the article function as possible candidate answers.
7We did not require the human annotators warm up by reading the 98 novels in the training data, but this might have led to a fairer comparison.
8The CNN QA dataset was released after our primary experiments were completed, hence we experiment only with one of the two large datasets released with that paper.
As shown in Table 3, our window model without self-supervision achieves similar performance to the best approach proposed for the task by Hermann et al. (2015) when using an ensemble of MemNN models. Our use of an ensemble is an alternative way of replicating the application of dropout (Hinton et al., 2012) in the previous best approaches (Hermann et al., 2015) as ensemble averaging has similar effects to dropout (Wan et al., 2013). When self-supervision is added, the Memory Network greatly surpasses the state-of-the-art on this task. Finally, the last line of Table 3 (excluding co-occurrences) shows how an additional heuristic, removing from the candidate list all named entities already appearing in the bullet point summary, boosts performance even further.
Some common principles may explain the strong performance of the best performing models on this task. The attentive/impatient reading models encode the articles using bidirectional RNNs (Graves et al., 2008). For each word in the article, the combined hidden state of such an RNN naturally focuses on a window-like chunk of surrounding text, much like the window-based memory network or the CLSTM. Together, these results therefore support the principle that the most informative representations of text correspond to sub-sentential chunks. Indeed, the observation that the most informative representations for neural language models correspond to small chunks of text is also consistent with recent work on neural machine translation, in which Luong et al. (2015) demonstrated improved performance by restricting their attention mechanism to small windows of the source sentence.
Given these commonalities in how the reading models and Memory Networks represent context, the advantage of the best-performing Memory Network instead seems to stem from how it accesses or retrieves this information; in particular, the hard attention and self-supervision. Jointly learning to access and use information is a difficult optimization. Self-supervision in particular makes effective Memory Network learning more tractable.9

heading: 6 CONCLUSION

text:
We have presented the Children’s Book Test, a new semantic language modelling benchmark. The CBT measures how well models can use both local and wider contextual information to make predictions about different types of words in children’s stories. By separating the prediction of syntactic function words from more semantically informative terms, the CBT provides a robust proxy for how much language models can impact applications requiring a focus on semantic coherence.
We tested a wide range of models on the CBT, each with different ways of representing and retaining previously seen content. This enabled us to draw novel insights into the optimal strategies for representing and accessing semantic information in memory. One consistent finding was that memories that encode sub-sentential chunks (windows) of informative text seem to be most use to neural nets when interpreting and modelling language. However, our results indicate that the most useful text chunk size depends on the modeling task (e.g. semantic content vs. syntactic function words). We showed that Memory Networks that adhere to this principle can be efficiently trained using a simple self-supervision to surpass all other methods for predicting named entities on both the CBT and the CNN QA benchmark, an independent test of machine reading.
9See the appendix for an ablation study in which optional features of the memory network are removed.

heading: ACKNOWLEDGMENTS

text: The authors would like to thank H. Pentapelli and M. Paluri for helping to get the human annotations.

heading: A EXPERIMENTAL DETAILS

text:
Setting The text of questions is lowercased for all Memory Networks as well as for all nonlearning baselines. LSTMs models use the raw text (although we also tried lowercasing, which made little difference). Hyperparameters of all learning models have been set using grid search on the validation set. The main hyperparameters are embedding dimension p, learning rate λ, window size b, number of hops K, maximum memory size n (n = all means using all potential memories). All models were implemented using the Torch library (see torch.ch). For CBT, all models have been trained on all question types altogether.
Optimal hyper-parameter values on CBT:
• Embedding model (context+query): p = 300, λ = 0.01. • Embedding model (query): p = 300, λ = 0.01. • Embedding model (window): p = 300, λ = 0.005, b = 5. • Embedding model (window+position): p = 300, λ = 0.01, b = 5.
• LSTMs (query & context+query): p = 512, λ = 0.5, 1 layer, gradient clipping factor: 5, learning rate shrinking factor: 2.
• Contextual LSTMs: p = 256, λ = 0.5, 1 layer, gradient clipping factor: 10, learning rate shrinking factor: 2.
• MemNNs (lexical memory): n = 200, λ = 0.01, p = 200, K = 7. • MemNNs (window memory): n = all, b = 5, λ = 0.005, p = 100, K = 1. • MemNNs (sentential memory + PE): n = all, λ = 0.001, p = 100, K = 1. • MemNNs (window memory + self-sup.): n = all, b = 5, λ = 0.01, p = 300.
Optimal hyper-parameter values on CNN QA:
• MemNNs (window memory): n = all, b = 5, λ = 0.005, p = 100, K = 1. • MemNNs (window memory + self-sup.): n = all, b = 5, λ = 0.025, p = 300, K = 1. • MemNNs (window memory + ensemble): 7 models with b = 5. • MemNNs (window memory + self-sup. + ensemble): 11 models with b = 5.

heading: B RESULTS ON CBT VALIDATION SET

text: METHODS NAMED ENTITIES COMMON NOUNS VERBS PREPOSITIONS MAXIMUM FREQUENCY (CORPUS) 0.052 0.192 0.301 0.346 MAXIMUM FREQUENCY (CONTEXT) 0.299 0.273 0.219 0.312 SLIDING WINDOW 0.178 0.199 0.200 0.091 WORD DISTANCE MODEL 0.436 0.371 0.332 0.259 KNESER-NEY LANGUAGE MODEL 0.481 0.577 0.762 0.791 KNESER-NEY LANGUAGE MODEL + CACHE 0.500 0.612 0.755 0.693 EMBEDDING MODEL (CONTEXT+QUERY) 0.235 0.297 0.368 0.356 EMBEDDING MODEL (QUERY) 0.418 0.462 0.575 0.560 EMBEDDING MODEL (WINDOW) 0.457 0.486 0.622 0.619 EMBEDDING MODEL (WINDOW+POSITION) 0.488 0.555 0.722 0.683 LSTMS (QUERY) 0.500 0.613 0.811 0.819 LSTMS (CONTEXT+QUERY) 0.512 0.626 0.820 0.812 CONTEXTUAL LSTMS (WINDOW CONTEXT) 0.535 0.628 0.803 0.798 MEMNNS (LEXICAL MEMORY) 0.519 0.647 0.818 0.785 MEMNNS (WINDOW MEMORY) 0.542 0.591 0.693 0.704 MEMNNS (SENTENTIAL MEMORY + PE) 0.297 0.342 0.451 0.360 MEMNNS (WINDOW MEMORY + SELF-SUP.) 0.704 0.642 0.688 0.696

heading: C ABLATION STUDY ON CNN QA

text:
METHODS VALIDATION TEST MEMNNS (WINDOW MEMORY + SELF-SUP. + EXCLUD. COOCURRENCES) 0.635 0.684 MEMNNS (WINDOW MEMORY + SELF-SUP.) 0.634 0.668 MEMNNS (WINDOW MEM. + SELF-SUP.) -TIME 0.625 0.659 MEMNNS (WINDOW MEM. + SELF-SUP.) -SOFT MEMORY WEIGHTING 0.604 0.620 MEMNNS (WINDOW MEM. + SELF-SUP.) -TIME -SOFT MEMORY WEIGHTING 0.592 0.613 MEMNNS (WINDOW MEM. + SELF-SUP. + ENSEMBLE) 0.649 0.684 MEMNNS (WINDOW MEM. + SELF-SUP. + ENSEMBLE) -TIME 0.642 0.679 MEMNNS (WINDOW MEM. + SELF-SUP. + ENSEMBLE) -SOFT MEMORY WEIGHTING 0.612 0.641 MEMNNS (WINDOW MEM. + SELF-SUP. + ENSEMBLE) -TIME -SOFT MEMORY WEIGHTING 0.600 0.640
(Soft memory weighting: the softmax to select the best candidate in test as defined in Section 3.3)

heading: D EFFECTS OF ANONYMISING ENTITIES IN CBT

text:
METHODS NAMED ENTITIES COMMON NOUNS VERBS PREPOSITIONS MEMNNS (WORD MEM.) 0.431 0.562 0.798 0.764 MEMNNS (WINDOW MEM.) 0.493 0.554 0.692 0.674 MEMNNS (SENTENCE MEM.+PE) 0.318 0.305 0.502 0.326 MEMNNS (WINDOW MEM.+SUPERV.) 0.666 0.630 0.690 0.703 ANONYMIZED MEMNNS (WINDOW MEM.+SUPERV.) 0.581 0.473 0.474 0.522
To see the impact of the anonymisation of entities and words as done in CNN QA on the selfsupervised Memory Networks on the CBT, we conducted an experiment where we replaced the mentions of the ten candidates in each question by anonymised placeholders in train, validation and
test. The table above shows results on CBT test set in an anonymised setting (last row) compared to MemNNs in a non-anonymised setting (rows 2-5). Results indicate that this has a relatively low impact on named entities but a larger one on more syntactic tasks like prepositions or verbs.

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context: , 2015a) or language modelling (Sukhbaatar et al., 2015).

context: To encode word order, time features are added as embeddings indicating the index of each memory, following Sukhbaatar et al. (2015). See Appendix D for a sense of how anonymisation changes the CBT.

context: For the lexical memory setting, we also applied ReLU operations to half of the units in each layer following Sukhbaatar et al. (2015).5 In a second stage, an answer distribution â is returned given K retrieved memories mo1, .

context: Bengio et al. (1994)).

context: (∗)Results taken from Hermann et al. (2015).

abstractText: We introduce a new test of how well language models capture meaning in children’s books. Unlike standard language modelling benchmarks, it distinguishes the task of predicting syntactic function words from that of predicting lowerfrequency words, which carry greater semantic content. We compare a range of state-of-the-art models, each with a different way of encoding what has been previously read. We show that models which store explicit representations of long-term contexts outperform state-of-the-art neural language models at predicting semantic content words, although this advantage is not observed for syntactic function words. Interestingly, we find that the amount of text encoded in a single memory representation is highly influential to the performance: there is a sweet-spot, not too big and not too small, between single words and full sentences that allows the most meaningful information in a text to be effectively retained and recalled. Further, the attention over such window-based memories can be trained effectively through self-supervision. We then assess the generality of this principle by applying it to the CNN QA benchmark, which involves identifying named entities in paraphrased summaries of news articles, and achieve state-of-the-art performance.

creator: LaTeX with hyperref package


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"Xmen: The Last Stand" directed by Brett Ratner [review]

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Abstract

In short, the third film in the X-Men franchise is much like the second one: spectacular, fast-moving, and in large part consisting of a lot of sound and fury that signifies not terribly much. It’s massively entertaining, mind you; just don’t come looking for profound, life-changing narrative.


I don't think flavor would be affected, assuming your sanitation practices are sound (let us know when you're done with your "science experiment"). You might have to adjust your recipe to account for an extended hot steep of finishing hops, sort of like commercial brewers with a hot whirlpool.

I'm not sure about the temp for the buckets, but I'd imagine you might want to drop the temp a bit before transferring.

I've always wondered if this method results in hazy beers from a lack of a strong cold break.

Even on the pro-brewery level I am having to wait over night to pitch during summer temps. It's fine to do so if your sanitation procedures are top notch there's nothing really to worry about. I do agree it is not ideal but it's way better than pitching too warm.

As far as heat and plastic, general RoT is not hotter than 140 or you can start to leach out some of the plastic chemical. You should be able to get your wort cooled down to 100 degrees at least. That's what I have been doing before racking to my fermenters and cooling over night. (Actually cool mine down to 85 with a pass through my CFC on way to fermenter.)

Conventional wisdom aka dogma says that if you don't cool quickly to below 140F, you get continued conversion of DMS precursors to DMS. Now, if your malt doesn't have a lot of DMS precursors to begin with (all the but the less-modified pils malts) and you boil long and hard to get rid of the stuff to begin with, then the conversion risk becomes small.

Thats my take on it. I don't have a lot of experience with the technique to back this up.

it was me who mentioned the 'no chill' thing. As I understand it, from an article I read a couple years ago about the aussie homebrew scene, they run off into plastic jerry cans (those big plastic water/gas cans) and seal it up till it is cool enough to pitch, then pour it into fermenter. It was in BYO I think

I do know that 180+ degrees will significantly shrink/distort a Better Bottle.

I had a not-so-great "energy/resource conservation" idea once to collect the initial IC runoff water and have some hot water to clean the equipment afterwards. If Better Bottles could speak, I'd imagine it saying something along the lines of "...Look what you've done! I'm melting. I'm melting!..."

I've abandoned the Better Bottles and returned to plastic fermentation buckets--it is so much easier to clean, store, and transfer.

The following is taken from the book of Palmer, which I happen to agree with. Although, I also recommend letting your experiences and most importantly the beer flavor be your guide.

"At the end of the boil, it is important to cool the wort quickly. While it is still hot, (above 140°F) bacteria and wild yeasts are inhibited. But it is very susceptible to oxidation damage as it cools. There are also the previously mentioned sulfur compounds that evolve from the wort while it is hot. If the wort is cooled slowly, dimethyl sulfide will continue to be produced in the wort without being boiled off; causing off-flavors in the finished beer. The objective is to rapidly cool the wort to below 80°F before oxidation or contamination can occur.

Rapid cooling also forms the Cold Break. This is composed of another group of proteins that need to be thermally shocked into precipitating out of the wort. Slow cooling will not affect them. Cold break, or rather the lack of it, is the cause of Chill Haze. When a beer is chilled for drinking, these proteins partially precipitate forming a haze. As the beer warms up, the proteins re-dissolve. Only by rapid chilling from near-boiling to room temperature will the Cold Break proteins permanently precipitate and not cause Chill Haze. Chill haze is usually regarded as a cosmetic problem. You cannot taste it. However, chill haze indicates that there is an appreciable level of cold-break-type protein in the beer, which has been linked to long-term stability problems. Hazy beer tends to become stale sooner than non-hazy beer. The following are a few preferred methods for cooling the wort."

I try to chill the wort down as quickly as possible in an effort to achieve a good cold break, which aids in the mitigation of chill haze, infection and oxidation.YMMV

Hot side aeration is debatable, the DMS issue I addressed above, and I'd imagine you would get cold break whether the cooling were to happen fast or slow. Certainly my canned starter wort drops plenty of protein. Infection wouldn't be a problem if you put hot wort in a container and sealed it. So really Palmer has no irrefutable arguments against no-chill.


Q:

Python: generator seems to always give me first element?

code
class MyClass:
    def __init__(self):
        from datetime import datetime
        self.union_date = [datetime(2000,1,1), datetime(2000,1,2), datetime(2000,1,3)]

    def get_new_datetime(self):
        for date_time in self.union_date:
            yield date_time

And I call the generator function like below:
In [10]: a = MyClass()

In [11]: a.get_new_datetime()
Out[11]: <generator object MyClass.get_new_datetime at 0x7fa90bc20150>

In [12]: next(a.get_new_datetime())
Out[12]: datetime.datetime(2000, 1, 1, 0, 0)

In [13]: next(a.get_new_datetime())
Out[13]: datetime.datetime(2000, 1, 1, 0, 0)

In [14]: next(a.get_new_datetime())
Out[14]: datetime.datetime(2000, 1, 1, 0, 0)

It shows only first element... Why does it happen?

A:

You are creating a new generator each time, perhaps you meant:
In []:
g = a.get_new_datetime()
next(g)

Out[]:
datetime.datetime(2000, 1, 1, 0, 0)

In []:
next(g)

Out[]:
datetime.datetime(2000, 1, 2, 0, 0)

Etc.


The latest scattershot Trump salvo came before dawn, when he alleged that Machado had a sex tape.

And now this fall, Trump can't seem to stop talking about Alicia Machado, the Venezuelan 1996 Miss Universe, catapulted to national prominence after Hillary Clinton mentioned during the debate that Machado was shamed by Donald Trump for gaining weight — and the latest in a string of non-politicians whose interactions with Trump have either damaged his candidacy, or become massive distractions.

This summer, Trump attacked the Khans, the gold star parents who lost a son in Iraq and slammed Trump during the Democratic convention — and kept attacking.

Clinton responded on Twitter, calling Trump's early morning attacks "unhinged."

"What kind of man stays up all night to smear a woman with lies and conspiracy theories?" her account tweeted.

On Instagram, Machado wrote that it was "not the first time the candidate insists on discrediting someone or insists on demoralizing women, minorities, and people of certain religions through his hateful campaign." She called his attacks slander and false accusations meant to humiliate and intimidate her.

In the days since the debate, Machado has made herself a fixture in the media and been a popular search term on Google, skyrocketing past even Kim Kardashian.

The Trump campaign and its surrogates have responded by questioning Machado's past, including allegations that she was a getaway driver when her then-boyfriend shot a man, and that she had threatened the judge on the case, though she was not charged in either circumstance. (In response, Machado told Anderson Cooper on CNN that she was "not a saint girl.")

The Clinton campaign became aware of Machado after an Inside Edition interview in May, in which she said Trump called her "Miss Piggy." She spoke of how he embarrassed her by taking her to a gym to workout in front of cameras and reporters and called her "an eating machine." Machado said she was becoming a citizen before the election to vote against Trump.

At the time, Clinton's Twitter account shared the video of the segment along with a message: "Congratulations on becoming a U.S. citizen, Alicia. Enjoy casting that vote." Machado responded by asking Clinton to stay "with us, with Latina mothers."

Soon Machado was connected with the Florida team, and knocked on doors to register voters for the campaign in the state in August, according to a campaign aide.

But how did a Florida surrogate, who also did events in Virginia, come to be mentioned a month later at the first presidential debate, a seemingly planned moment with maximum political potency with women, immigrants, and Latinos?

A campaign source said there was interest in using Machado's unique circumstances more as the race rolled on and the Latino team elevated her story for use in the debate during preparations.

"Why pick on Machado? He got kicked around in the debate but it was this thing about this woman," the campaign source said. "I'm starting to think there's a part of him that is really bothered by women, he wants to control them and when he can't he flips out."

Leon Krauze, a Los Angeles-based Univision anchor for the national network, said he believes the legacy of the Machado incident will be that it hurt Trump more with women, than with Latinos.

"I get what Clinton was trying to do, Alicia Machado is like a beautifully crafted stone, you get the female vote, Hispanics and immigrants with one single case," he said. "But from what we’ve been hearing on our end, the misogyny aspect of it resonates most, rather than the Latino aspect. There may be an impact with Latinos, but the main damage is with women.

Ana Navarro, a CNN commentator and Republican strategist who has remained staunchly anti-Trump, agreed. She said Trump can not restrain himself from charging wildly when he feels attacked even if it's an "idiotic thing to do."

"It is not helpful to see him and his surrogates trash Alicia. Hell knows no fury like a Latina who's been called fat," Navarro said.

Navarro, like Clinton spokesperson Jennifer Palmieri speaking to reporters Friday, noted that Trump similarly attacked another woman, Fox News anchor Megyn Kelly after a primary debate.

"His insults against Alicia are fat-shaming, bullying, and speak to a total lack of chivalry," Navarro said. "I think a lot of us are shocked that a powerful man can be so vile and petty toward a woman. It's kind of gross, really."

Clinton spoke with Machado during her drive to the airport Friday, thanking her for her "courage" as the issue has been unpleasantly elevated into a war of words with Trump, said Clinton spokesperson Nick Merrill.

Segueing to the day's events after eight tweets about Machado and Trump's comments about women, Clinton's Twitter account said "While Donald continues day 5 of his Machado meltdown, we'll be in Florida talking about national service."

But you got the sense the campaign was overjoyed, that thanks to Trump, Machado was still in the news.


[Changes in phospholipids after repeated induced sublethal spinal cord ischemia in rabbits].
Degradation of membrane bound phospholipids in CNS during ischaemia begins with extreme rapidity. Sublethal ischaemia influences ischaemic tolerance in the affected neurons and is stressful enough to induce neuronal changes such as postischaemic hypoperfusion, transient suppression of protein synthesis and induction of stress (HSP) proteins. It seems, that the nature of factors responsible for ischaemic tolerance may involve the activation of multiple different systems. The aim of this study was to investigate the changes of phospholipids in gray matter regions of spinal cord following sublethal ischaemia repeated in long intervals of reperfusion. Male rabbits, weight range 2.5-3.5 kg were used in the experiment. They were divided in following groups : 1. control animals; 2. animals subjected to 25 min ischaemia; 3. animals subjected to 25 min ischaemia and 3 h of reperfusion; 4. animals subjected to sublethal (8-8-9 min) ischaemia repeated in long-lasting (8-8-24 h) intervals of reperfusion. Phospholipids were separated by thin layer chromatography, lipidic phosphorus was assessed spectrophotometrically. Sublethal ischaemia repeated in long-lasting intervals of reperfusion increased the concentration of phospholipids to control levels in all gray matter regions. The resynthesis in the dorsal horns, of PC and PE in the ventral horns and of PC in the intermediate zone. An excessive renewal of phospholipids after sublethal ischaemia repeated in longer intervals of reperfusion was most pronounced in the eh dorsal horns of the spinal cord and can be the result of many defensive cellular mechanisms.


Hillary Clinton shifted blame to New York City mayor Bill de Blasio on Tuesday for a racist comedy skit the pair performed this weekend in which they joked about “colored people’s time.”

“Well, look, it was Mayor de Blasio’s skit,” Clinton told Cosmopolitan in an interview. “He has addressed it, and I will really defer to him because it is something that he’s already talked about.”

Clinton and de Blasio have come under heavy criticism for taking part in the comedy routine, which was staged during a charity event held at the New York Hilton on Saturday.

Halfway through the set, in which de Blasio rapped and made a fool of himself, he introduced his “home girl, Hillary.”

“I just have to say thanks for the endorsement, Bill. Took you long enough,” Clinton said after taking stage. De Blasio, who managed Clinton’s 2000 Senate campaign, took some heat last year for waiting before endorsing her for president.

“Sorry Hillary, I was running on CP Time,” de Blasio said, eliciting groans from the audience. “CP Time” is a reference to “colored people’s time,” which plays off of the stereotype that black people are often late.

“Cautious politician time,” Clinton responded, according to script.

De Blasio said on Monday that critics were “missing the point” of the skit.

“It was clearly a staged show. It was a scripted show,” de Blasio said during an interview on CNN. “The whole idea was to do the counterintuitive by saying ‘cautious politician time.’ Every actor thought it was a joke on a different convention. That was the whole idea.”

It is not yet clear who wrote the skit or if the Clinton campaign reviewed it prior to the show.

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NFL to penalize use of "N" word; Mut and Merloni discuss

Feb 24, 2014|

Mut and Merloni discuss the NFL's proposed rule to penalize players for use of the "N" word.

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Transcript - Not for consumer use. Robot overlords only. Will not be accurate.

What shut our Butler and 937 WEEI your phone calls all day at 6177797937. You can text this year. On the AT&T Tex wanted 37937. Peter King has extensive Monday Morning Quarterback today and it's from man sell its. Cloudy to the 49ers -- -- speaking about. Even entertaining the idea of trading Jim Harbaugh for practically -- mean it's jam packed with stuff. And in the middle of that and it's it's tied in to an outside the lines show last night. He writes and the rest is after his big league news of the week. That the league's competition committee meets in Florida beginning on Friday. And one of the items on the agenda will be the discussion on a rule -- fag flag players for using the Edward. On the field during games. Peter writes -- unequivocally in favor rabbit and could see no reason. Why shouldn't be enacted why it wouldn't be enacted that the quick basically Twitter. Reaction from Peter King. In his column today and and it's basic that I get you wanna clean up the language your players are using you feel like that there. There's a certain time in place for certain words on the field the play. Maybe they feel like that's one of but he says I have no reason he writes I'm I'm not sure why this wouldn't be enacted. And I if your reasons first of all. This is pretty tough to. Talk about when he -- -- context of the war you have black players who. You know as recently as -- -- was flat and last year an MBA locker room it's it's locker room on the court for the celtics' Kevin Garnett was here. You have black players in the NBA you know in that league using that word is a term of endearment used that word pretty commonly. It's your read Peter King didn't you say that OK it's pretty easy to an act. Well we talking about every use of the N word on the field for NFL players is it always used derogatory and this is. This seems like a tough thing for me to just say yep go ahead and enacted no big deal where revocation might be a penalty on the field for talking your teammate. Because can be possible to do that's wise and stuff you know what is it is it's an attempt to slow down. A little bit. Can you envision. Dole. -- breaking up a pass it and in the trash talk itself right a great now you -- you stand over the guy taunting. Is a flag. In it is it is note taunting -- one word -- flag goes -- -- we got -- penalty. Well we call use of like these -- bad language yeah I like our it's fatal locker room during practice. During the week things happen on the field assistant coach gonna -- -- thousand bucks 5000 -- games that work. Now if you try to make it a rule if you say that it is one I don't believe going to be enforced. But it might make everybody aware more aware that the league is trying. And it might slow it down a little bit but I don't how can you enforce that. Really how can you enforce that I just I don't see how you can't. He should should the enforcement of it. Be worthy of affecting a game on the field the language used on a field affecting the game and again I go back to the MBA Kevin Garnett is beloved Europe Boston Kevin Garnett is one of my my favorite. Boston -- I will speak for everybody. Kevin Garnett use that word pretty liberally on and off the court when he was playing if you sat in and around. Anywhere near the floor you inevitably -- Kevin Garnett used that word. Use the N word constantly. There -- teammates he used that as a term of endearment and that to say that that is now a flag penalty and again I'm assuming they're gonna goal. Fifteen yards I don't know I have no idea but I'm assuming it's all on the lines of ponting a personal foul. If that's the case you tell me that used talking to repeat it that way to -- is worded fifteen yard penalty do affect the outcome of the game at a facility. Outside the lines last night it's and I watched it last night their mortar all day and they had. It comment. The head. Will -- whitlock in Ryan Clark where the Pittsburgh Steelers talking about this. And here's his will blogs take on the whole hobby NFL's reproach when he thinks he can it can work or not. Talking about the National Football League or in the illegal workplace -- complex. And I've talked to John -- about this and I'm I'm sure Jason whitlock has as well -- call me it's obvious and we had long discussions went through email. Whether voice -- voice from person about the use of the word he knows that I said I use it. And I now do I understand John's position on old Jason agrees largely with that. I understand the need to look at language went on the field to plate with a locker room war again. Any workplace we're going into new territories with this new ground with this I understand the need to look at I'm not sure that it can be legislated successfully. I'm not sure can be police -- that in the way it is well because again it's more complex. The use of this particular work the objection to it. The embracing a bit. Be that emotion wrapped around it is more complex than any word that I can think up in the English language. And so win -- league of owners who are very by the weight removed. From the use the word last sector -- black owners in the NFL right now. I'll -- when you have that situation. I'm not sure that they should be framing the discussion you are referred to my position on this as facet of my dear friend of 35 years Tony Kornheiser. You don't get a vote in this one. This conversation after the rain my people were a lot more intimately associated with the work. Wedlock and I go back and forth on years about -- -- you gotta stop doing this you got. That's fine we can do that in my opinion we can do that because we're talking about ownership of the word -- and -- -- context to everything. And that includes the NFL's objection to the use of the war. SI -- Got things can be tough it is kind of -- right LB had a followers can get together and vote. But the use VMware. You know all the white owners meet together in in vote on this I just. I just a little hold on the Jacksonville owners to be fair you Pakistan they've won that one not -- right so you know you've you've got. I don't know on the field right is different. When Riley Cooper. Right use then word. On the field to an African American. On the times that the -- to Levy's. I'm honest I can only assume I just assuming I'm just assuming it's probably unfair but I'm just guessing the only. Not stand Jimmy Graham is that we're coming out of his mouth as a different. -- an exit -- different sounding when the key to leave. Says it to another African American there when Riley Cooper says I mean that would probably wouldn't stand on the field right now not at all when -- at all. But I mean UC's -- hear it you know. He says KG just go to the games in front row you hear all Paris in the locker room with UN cover any Celtics gave before you heard Kevin Garnett. You use the war with team is -- this is the thing to me it's like you know when things happen. And then we sort of we we take the next step whether it's an overreaction or not. -- -- We need if we need to fix the culture in the clubhouse in the locker room. I think that is evident how people treat one another the bullying Nichols on whatever might be reaching a -- whatever might be. OK now we've taken a step further and say you can't use certain words when your plane NFL. Now that we can debate whether -- -- -- -- should ever be used I have no right used network. Put this slogan do now now gonna take Arctic words out of of the Plainfield. Human use it just in the workplace -- here on the field competitive it's like baseball -- took away at port MF. MF for right while playing baseball. Right that's garlic your version of you get big get -- for somebody or somebody's whatever some umpire also the stuff you can't use that word in the heat of competition. It just doesn't it's gonna happen and I don't know how to -- they can regulate is it would if. If on the on the football field now Michaels -- bitches that now I know what they're trying to do here I get the league wants to get the sport help I believe it's a slippery slope. That's the second thing the first thing is what you said legislating it seems impossible. It's now up to and hockey league. To decide whether or not 888 player was talking to his teammate or talking to an opponent. It's up to. You know one of -- it would be this -- -- -- this -- toward Brothers. Her up there on the field and they got a disciple was that it was not a term -- or use of the word was that really a negative views. And all bodily -- at college sports like as many B guys are texting -- -- -- assuming when I'm assuming. Unsportsmanlike conduct fifteen yards. So unofficial on the field who they have a hard enough time figure out -- to spot the ball to watch a replay is now in charge of and we're all about. Hone it how it's used and where it's Houston and everything and one of the one of the bits to a -- up I would say that the look at life when Riley Cooper. That whole thing broke. No I said this back there and got a lot of response on Twitter on tax of people agreement. Data segment two after those four individual spoke they kind of they went to the high schools and at some kids but is this segment he able means seeing -- mean Joseph green talking about it in one of the students in the high school what he feels about the war. And here we go pick them -- hotel -- recognized. Let's look. In order to -- the ER that's black and contains an -- and went with a enslaved. A black man would go team and it's kind of it it seems like he's got like. The ugly color over the in this community looks to assess -- -- -- -- said that back then right -- foot ER at the end you know as opposed to -- the -- it it. At -- some connect the army there's a different tone usually. When you use that word there I forget who was from ESPN an effort American report she's on I forget which is always on -- all the time she's talking about the word. You know bitch. We should meet my girlfriends we use it all the time but it follows a different setting not my girlfriends and somebody called me that it would have a different meaning. It would have a different meaning it was really hit home differently than when me and her girlfriends are out they use that word. And I and I agree it's about -- it's about how it's said and where it's set. It's text to bring up a very good point as it may be part. Why the NFL is doing this -- openly gay player going into league this year. The other is the the F word in the beat three letter -- -- that's the exactly part of this list to my feeling is that this is sort of the beginning. And I understand the -- I understand wanting to do -- that the backing behind it it is thoughtful. -- you don't want that his party your game. But the implicate the the implementation of it and associating penalty with it. Like what -- you talked about watching the show last night in what brought Ryan Clark talked about. Ryan Clark pretty respected member of the the Pittsburgh Steelers. And their -- eagle -- to their office and asked them to stop in their in their blocker -- And asked Ryan Clark as leader of the Pittsburgh Steelers to stop using that word is that African American and he's a -- -- leader did that work for out locker. Those caucus talking about -- where these easy calls an old man Rooney came and talked Ike Taylor -- in musical boy network -- being used in the music they played so I don't wanna. I don't want this music play in a locker room. So there was respect for him and they said that we went away from it for a while but eventually it sort of came back again and start play in the music again. No recollection he doesn't use the -- doesn't use -- and more at all but he is in groups of players that that constantly use it. Other African American players and talk with one another that use it in a locker room to use it on the field. Common new word on the field people are excited yelling at one another. -- united just I got to take that out of the game withdrawal flag for that I think it's gonna be two to harp on the deal. Yeah and it's it's John wooten who is the chairman of the Fritz Pollard alliance said the rule likely be adopted at the meetings in March quote I'd be totally shocked that the competition committee. Does locked up hold -- on what we're trying to do. We want this word to be police from the parking lot to the equipment room to the locker room. So he wanted to be. NFL to push for automatic fifteen yard penalty for first time offenders. 82 offense. The result ended rejection from a game. So that's never gonna -- that and that's never gonna happen the the right the idea that again these officials a -- -- their get it right -- -- player now. In charge of language I despite tough to believe. He just surfer means you're agree let's hear from Jason whitlock who has been he's long been. Password John wooten is he wants his word out of music he wants that a movies he Watson on the out of the NFL this was Jason whitlock yesterday -- part about tidal. -- word is a symbol of book culture -- as are openly sold. Fifty years ago the culture and the people we use the N word we're very hostile toward black people. Very violent towards black people very against black people educate themselves any ball. Not everybody did use the N word -- -- years ago with a steaming races are took action against black people. It's no different than today. The people that love the -- -- and love to use that are part of a culture that is still half out toward black people. Still anti education and still very violent toward black people. And I'm talking about the black people that are in love with his work if you examine the culture that the in the word represents. It represents the culture that is anti black. This symbol is more powerful than the confederate flag or any other symbol we have from race at them. It's still it's the most powerful of the longest standing and it's still stands today and it's embrace. By the very people pulled -- tended to destroy and that's what's added I'm glad the NFL is taking this day. -- had a strong stance on a ball no doubt -- -- the word should be taken out should be taken gatherings languish in the NB ever mentioned again. It's -- the other three were. Port on the same path as he was 61777979837. Is your phone a because of her Arctic let's give you a chance to react. To the NFL could adopt as soon as next month Jason is an act to lobbies -- much Lou -- Jason. -- lead our guys good. -- got -- -- -- -- -- a -- and I mean I had high school I played soccer in high school and I had a very good up and I was up in America and not a member earlier it and want solid dollar to ship channel -- our normal group and Edwards -- now know what the derogatory toward Daniel would reduce -- you know upgrade. It's -- you know. And kind of outlook that into -- in the mouth and never -- that -- -- again a black individual all around. It's just one of those things that -- animals with -- individuals around. Yeah there was no way I know we it was a mobile I'm -- -- black and I and our our group analytical or white friends or just have a lunch they're not kept. Big pot and then we'll -- -- to a pal and I was. An economic to -- -- -- mouth in that you're not lobby that would. And I never said it again and I'm 33 almost happened years ago. It's exit it's in the touchy subject with some individuals some individuals who don't like hearing a -- because then other people don't care. I mean it's like guys and women back in the day it was a derogatory buying by the way people. Black people but not a black people have taken it over and they music look at all kind but a lot of rumors but -- have won a light years. In an underdog Billy Graham scored you know whether it is the conversation weathered like eight other great clay and -- Edwards some out in back. They just don't like hearing a complex individual. Which I totally understand by the way a bag and it's completely understandable what I. It's it's it it's weird for -- because. If for some reason a Guy Ritchie cog veto way over the -- -- -- a story broke. It was from people it is a locker Allred she's like a black guy. He's the same language reading so some guys are are allowed to use it okay well. On the field plays the NFL gonna have a list the guys that -- those guys can use that word mean I just eats. Well that's that's one thing in -- -- to -- the face and -- lunchroom. It's one thing -- and that happened verses. And a -- officials gonna decide what context -- views on the field and by the way that's. Second only to a figuring -- -- it out of a game reviewer Charles Barkley say that you know I use that word and it continues their word. With my you know African American Brothers and friends whatever. You know of that and that sort of like that sort like the feeling was that I have friends. You know so we -- Catholic Jewish Irish Italian. You name it everything's fair fair game. We we've been friends -- middle school since high school. Now lodged a fair game sometimes things get ugly on some of these you know message boards in our fantasy football beleaguered text or whatever might beat. It it's fair game. You know because it's our group. To be comfortable say some things -- cinema but it does absolutely not. Absolutely not I have no right to did you ever use that word he noted mean to an African American but there's certain things. You know the second set Irish Italian Jewish Catholic jokes that dream my friends it's it's our group. Were close enough that nobody's offended by any of -- so to sit there and tell Charles Barkley can't use that word to another African American act to a point Agassi in -- -- whoever I want. In my in my body to my friend -- were close friends or -- all fair game Eric. Tell me what I can and cannot say don't -- my team a language I use with him and other places were on the field he's my teammate that's a language we use. Injuries in Northampton up with a Motley uniting through seven Andrew. -- been written later some of these points but recovered but. -- Michaels and coming into the leak they can't just and the airport. And I hope the conflict and to lay -- on computer -- not let them and drop but. You can't just in the airport for him. -- and are you gonna make it worse but this way don't get an entire offensive line this. Just covering without bombs and also would be and content you go. Let's -- the issue the NFL that one would -- and legal responsibility. If we want to eliminate the behavior. And listen to -- So that they can say well this Obama field. The bacon trying to eliminate it from the locker room and in all the text messages the only. Word that was sort of in determinant. If you could see what I mean is the handler spoke about your mother your sister you know but as violent and. So if they need to eliminate any kind of interpretation. And say that it all legal. They can protect themselves. Legally without actually. Admitting any responsibility. Utica -- is gonna be lauded as the couple words here under. Well I think to Michael Graham that could mean the league is the most. You know the most important factor but they can't come into the locker room and say -- We messed up you that can't be called me that she can't -- -- that. But they can't say well on the field -- -- -- and name calling. And that's about it. -- thanks your call as always it's. You guys are one react this'll get more your phone calls next 617779798378. TT -- minus 37. 937. -- in -- Sports Radio W media Butler got a 37 WEEI your call 61777979837. Can text us. On the AT&T text line at 37937. Getting your thoughts. On the note today and Peter -- Monday Morning Quarterback in the daily news story yesterday. Saying the National Football League is well one words one person's mind -- -- that -- expected. Two passing a rule that would ban the N word. From the field of play. And also wouldn't goes on to say the NFL. Is gonna push for an automatic fifteen yard penalty for first time offenders you're second offense would result. In an injection from Mac gaming getting your thoughts is that plausible -- and I basically. Agreeing that it's going to be really hard to enforce on the field. How do you do that during -- it feel to play between even teammates. Charlie's on a cellphone he's up -- not Lou -- Charlie. I got they have verbal you got a Charlie what's in your mind I think you did you major -- what -- pathetic excuse legal opinions on the of that. Or at all. -- owners don't really have a choice -- they don't vote to support. They're essentially voting to condone it. And you cannot put yourself in the situation owner. We -- million vote and don't use that word they really don't have any choice at all -- issues. And that I think he got that have been arguing for this because. -- you all agree that they like their -- network content on the field. Then you're pathetic thing is that correct reason you can make a judgment call. And we hit it indictment out of there and it took to make that word illegal so they don't happen it doesn't call it -- here. It I don't I think you know every company including the BEI. All of these against uses the words. To protect result means you are Ratner creating a culture of crap that they they don't have any choice at Embarq corporation. Well all they don't -- choice because agree with -- they going to. You really do think that -- gonna call penalties on us. Well if I get it hit eight the first block it or -- they come along that I mean Iraq. And then they're gonna happen. You know we we really at the do we really have to -- oh we we really really do at the start thinking about trying to separate the workplace and sports. Of course you can -- like K you got through like eight we can't keep compel it we can't do was keep comparing. Corporate America to the playing field and the -- salaries so that we athletes talk. With a billion dollar corporation sold -- that you know not let running out of fuel -- -- making million dollars corporate me billions of dollars now. Bartlett has been ruled that but he. Aren't well a guy -- a football field used yen where what happens if I do. She gets fired. Or to -- Go and dozens of house of the senate I don't know it's a fifteen yard penalty mega fired now that the -- but the -- all we wanna compare them stop. Stop trying to compare them stop trying to compare the salaries. Up to compare everything you worked in corporate America -- play pick up basketball game. Or softball game and it weakens the used the same language. On this football field although the -- court with your buddies -- you're doing your office to your boss why why -- we doing this. It's completely different. I understand why they want to do and I think the. Implementation of it is kind of crazy fifteen yard penalty to effect an actual game. When you -- again you have we listened to two teenagers last segment say. We uses the term of endearment it's not like it used to be there -- younger players coming up they used that language with their friends with their teammates on the field. So. You're asking officials to disseminate know which ones which. -- and you go to affect the game because of that I almost see it I'd be more on board as you say gonna start fining players. And you're gonna fall you're gonna this be a fine. But affecting -- of deciding your product on the field. Would language and some of your players believe it's okay to use and don't think it is the taken away Jason whitlock or mean Joseph green a rather spank. That's going to be fifty Europe helping your rejection that try have a hard time grasping -- -- -- Peter King says I see no reason why they should not be enacted it. That's my biggest reason you're gonna have games affected because of players language using language they think is okay that they use every day. That they use in the locker room. That's gonna be used on the field with their own teammates potentially. That's going to be a. A punishable what fifty -- it goes back to it goes back to the intelligent enough. You know old fool who went to use language certainly in. K I don't six years and use the F word. OK one I'm doing my job. When I get off my job a month on a -- my cosmic that's what comes out. -- idea comes out. I say things which you know what I'm intelligent enough to know that I can't do it in certain places. And yes. And the players so what is there a moment not to maybe a press conference that drop bombs and bombs and your name. That's probably not place or like you know when you're doing a commercial. That's probably not the time of the place -- doable when you're competing on the field. Language comes out language reactionary it just happens. That's you know you gotta be Smart enough knowing your audience knowing your job went to say when not through those sort of things. And I mean that would get -- here are different words. When it comes out of the N word you know it's like you get the group of players all African Americans are playing together and use it left and right. All of a sudden it's also be used anymore. They're gonna keep -- it in a comfortable with and they're setting and their players and using it on the field and -- some -- gonna say not -- It got just what your teammates just laid out somebody and they come over and a teammate there's dropped the F dropped the and bomb probably with an am F bomb to throw a flag. This strategy gradually his own team and that's how he expresses it. That's happen that's that's that's the prom you've uncovered here is that you Tom what teammates who feel like it's OK to use that language news and practice use it. In the locker room just in a car Jeff. -- -- -- -- -- In the interest of for the quarter I will say that I am a black American African American. And I disagree. With both your points I thought Charlie made a lot of good point you are squalor. And the point. -- will want to order articulate well -- ready 88. How can opener to white guys vote on it. -- that's you know none of -- being black and hold on. The speed of black players well. In 1964. Mean. Congress passed the civil rights bill at all congressman -- why. And -- in port. Any legislation. That black Americans have that we never performed today was crap I don't like people. But I didn't get the paint our relation to the thirteenth amendment the fourteenth demanded the 64 favourite built 65 automatic -- affirmative action soul. Let's Arctic caught out warrant you don't like guys -- -- it because it. Average -- is a little bit different I mean I'm pretty sure African Americans backed them all for not getting rid of slavery. -- every African American NFL all for getting word use that alert will be penalized for. What sometimes you don't know what that -- and let let let go back to those killed 32 orders. Out of 27 album I'm sure there are Jewish descent. In that it was ever a single word between -- -- K where it and I won't say it. -- be legislated in the outcome now like a ton of bricks on eight -- -- such ignorant I I don't personally you to where I hear brand. Well somewhat boy you do a lot of my -- I'm on my camera you live by it I don't like it and it that I have to stop and it continued to penalize the guys and they're giving it won't yacht penalties -- the stock. It will stop any -- And I I think that it apps to -- actually start somewhere it is good that. You know I'll take a stand in there and then hopefully. We will follow -- I don't think it's as prevalent in hockey actually never here at all. And I don't think it such that they're probably baseball I don't call profit figure things on the stand by in basketball while it needs to stop. And I think that sports are 8 o'clock about society so I. Like certain compared to what people the sport by my doctors and I understand your point here might -- Let me ask you this -- the -- Escude beat the use of the N word it's a new NFL. -- -- sports tickets used more. By white people or African Americans. -- I think we care more prominent black people quiet. Who knows because. I can no matter what are your college say I never edited are my black friend to him. But like you ask that you called upon to -- -- -- -- a new equipment they have. You heard that. -- didn't want to insult you I. I know why people are using it to you on the ground war but. Oprah at least it's it's you buy -- more no question. All that some naked that would you agree that okay. The N word used in sports I mean we've heard different opinions and that's not a change uses a term in the government. You're -- that some African American athletes of Charles Barkley said I use whoever walked. Shouldn't there be some. Bellwether Pia and education whether BA in camp we're gonna have a spokesman -- round and discussed the use of this word. With other African American players and we're gonna would have conference's ill in every single like you and -- -- talks to players about certain things we're gonna sit down talk about it. And tried to discuss it discuss it with our own as far as how why we're using it how we're using and how we're gonna be the ones to stop using. No no they shouldn't be because it. -- -- -- that you knew your history. Educate them that's what I'm saying let them know why Christian music. Jeff thanks for your call which have a lot of calls backed up and wanna give it to me I listen -- to build green make their point two ends mean Joseph green and Jason -- the way they talk about it. Jason -- -- in the locker room. And -- go to the the African American players and say why do you guys used to actually use it to his credit whitlock is written about it a lot he'd be -- -- to -- uses that thinks that it's completely. Ass backwards that pop up player a person of his prominence daisy but it's such an influence a young black Americans continues to use that word music. And he -- CNET -- movies he hates and so to his credit he has written about it. In terms or a lot from that'll be done that but he has written about I. I can always be too with so much I'm like -- the radio is not in -- for these teams like got to go -- what other players say hello Ryan Clark. Quote and it's gonna be really tough to legislate this rule to find a way to penalize everybody who uses this word the safety we spent twelve years in the NFL. And it's not going to be white players using it towards black players. Ryan Clark says most of the time you hear it it's black players using the war. So that's got to play in the league is black himself saying it's not the white guys say this word it's. It's black players using it either were a little autos and -- always scheming that's on sale without -- -- opponents are -- it's an awful -- right. And once the patriots is no question about it. But we're trying to take it out of the league. When it's used it's predominately used. By African American. It so it's. It's I don't know that doesn't mean something something else needs to be done. We -- gonna fine and penalize African Americans for use that word improperly. You know on -- if if if rule like I was born round run around saying it okay -- you can deal with that -- and they're gonna deal with that. Riley Cooper they're -- deal with it. But we are trying to take out of the game when -- -- African Americans that are Leo and Brian Cox not the only one that says. So. Are attracted a game for the with a educate them by the using my they should not use our. It's it's a weird spot to be in the league I get the premise behind it but I grew Ryan Clark. Trying to Ted Ted instituted markets Seattle I'm mark. And good afternoon guys more than instinct I think is they are out in public relations L. They've -- or outsourcing NFL right now by the Jonathan Martin Michael spam concussions. And pick for public. Bit they're all that negativity that sent Michael and that's -- -- -- that brought the girl. And I think the NFL is gonna get bored about OK which slurs are going to be good and which slurs are going to be bad in Tibet so broke they can't go out. It's one of those you know are all like microscope picked up and work flying around all -- replace some sort of our national TV that are gonna feel two. I mean it right now they're also the public relations nightmare. Washington Redskins nickname which is considered there's -- a better they're kind of in a no win situation here. Well he doubled at the microphone department date that words come up on microphones and NFL films they they believe that knowledgeably about all these other wars. That part is not new. There it's like Andrew North Hampton said that you know they're preparing to -- multiple words. And Michael sand gets in league it's horrible legislation thing and got to protect themselves as a league. Your thoughts on this continue at 6177797937. Coming up next on the daily diamond. David Ortiz has a new best friend. Japanese comedian. -- -- -- -- all I pronounce that right ducky I'm getting here the whole areas audio the two of them together next you don't wanna miss.


Knowledge, attitudes and practices concerning HIV/AIDS among Iranian at-risk sub-populations.
This study in 2003 looked at knowledge, attitudes and behaviours concerning HIV among 3 high-risk groups (201 cross-border truck drivers, 50 female sex workers and 754 youths) in 4 cities in the Islamic Republic of Iran. The level of knowledge about HIV was low on average, especially among individuals with high-risk behaviours. Truck drivers and female sex workers had higher knowledge about sexually transmitted infections than youths but their knowledge came primarily from personal experience rather than public awareness programmes. Truck drivers had a more positive attitude to temporary marriage and pre- and extramarital sex than youths. Condom use was low in all groups. Better targeted education of high-risk groups is needed.


Sencha Touch 2.4.2 still needs the workaround for the iPad. Take care though: the code posted above is missing a comma after 'point = touch.point'. Add it otherwise you might be leaking moveDistance as a global variable!


Novel Notch alleles reveal a Deltex-dependent pathway repressing neural fate.
The Notch receptor triggers a wide range of cell fate choices in higher organisms. In Drosophila, segregation of neural from epidermal lineages results from competition among equivalent cells. These cells express achaete/scute genes, which confer neural potential. During lateral inhibition, a single neural precursor is selected, and neighboring cells are forced to adopt an epidermal fate. Lateral inhibition relies on proteolytic cleavage of Notch induced by the ligand Delta and translocation of the Notch intracellular domain (NICD) to the nuclei of inhibited cells. The activated NICD, interacting with Suppressor of Hairless [Su(H)], stimulates genes of the E(spl) complex, which in turn repress the proneural genes achaete/scute. Here, we describe new alleles of Notch that specifically display loss of microchaetae sensory precursors. This phenotype arises from a repression of neural fate, by a Notch signaling distinct from that involved in lateral inhibition. We show that the loss of sensory organs associated with this phenotype results from a constitutive activation of a Deltex-dependent Notch-signaling event. These novel Notch alleles encode truncated receptors lacking the carboxy terminus of the NICD, which is the binding site for the repressor Dishevelled (Dsh). Dsh is known to be involved in crosstalk between Wingless and Notch pathways. Our results reveal an antineural activity of Notch distinct from lateral inhibition mediated by Su(H). This activity, mediated by Deltex (Dx), represses neural fate and is antagonized by elements of the Wingless (Wg)-signaling cascade to allow alternative cell fate choices.