text:
\section{Introduction}
Semantic parsing aims at mapping natural language to structured meaning representations. This enables a machine to understand unstructured text better which is central to many tasks requiring natural language understanding such as question answering \cite{berant2013semantic,pasupat2015compositional}, robot navigation \cite{macmahon2006walk,artzi2013weakly}, database querying \cite{zelle1996learning} etc. For question answering, natural language question is converted to formal semantics which facilitates interaction with a knowledge base (such as FreeBase \cite{bollacker2008freebase}) for retrieving concise answers \cite{furbach2010logic}. Such representations can be used to specify instructions to robots \cite{artzi2013weakly} or conversational agents \cite{artzi2011bootstrapping} for executing desired action(s) in an environment. Similarly, natural language queries are transformed into executable database programming language instructions (such as SQL) to retrieve or generate correct results in a database \cite{sun_etal_2018_semantic,zhong2017seq2sql}.

A variety of logical forms and meaning representations have been proposed for text. These include graph-based formalisms \cite{banarescu2013abstract,abend2013universal,oepen2014semeval,kollar2018alexa} where text is represented as a typed graph. The entities and action events are represented as nodes with labeled edges depicting relations between them. Semantic dependency tree \cite{oepen2014semeval} is a directed graph depicting the syntactic structure of a sentence in the form of modifier relations between its words. AMR (Abstract Meaning Representation) graphs \cite{banarescu2013abstract} use variables to annotate nodes following neo-Davidsonian style \cite{davidson1969individuation}. Lambda Dependency-based Compositional Semantics ($\lambda$-DCS) \cite{liang2013lambda} was proposed as a formal language adapting Dependency-Based Compositional Semantics \cite{liang2013learning} borrowing the expressiveness of lambda calculus \cite{barendregt1984lambda} but aiming to remove explicit use of variables.

In this work, we focus on first-order logic (FOL) \cite{smullyan2012first} as the language formalism for text. FOL represents entities and actions in natural language through quantified variables and consists of functions (called predicates) which take variables as arguments. The predicates attach semantics to variables and express relations between objects \cite{blackburn2005representation}. For instance, a simple sentence - ``a man is eating" can be represented through FOL as

\begin{center}
    $\exists A (\exists B (man(A) \ \land \ eat(B) \  \land \ agent(B,A)))$
\end{center}

Advanced natural language concepts as in sentence ``the man and woman are seated facing each other" can be expressed as

\begin{center}
    $\exists A (\exists B(\exists D (\exists C(man(A)\ \land \ woman(B) \ \land \ seat(D) \ \land \  \textcolor{blue}{subset\_of(A,C)} \ \land \ \textcolor{blue}{subset\_of(B,C)} \ \land \ theme(D,C) \ \land \ \textcolor{red}{not(\exists E(other(E)} \ \land \ \textcolor{orange}{not(\exists}  \textcolor{orange}{F(face(F)} \ \land \ \textcolor{orange}{theme(F,E)} \ \land \ \textcolor{orange}{agent(F,C)))} \ \textcolor{red}{))}))))$
\end{center}

where ``man" and ``woman" are \textcolor{blue}{represented together} through shared variable \textcolor{blue}{C} and ``facing each other" is represented by \textcolor{red}{negating} the existence of a thing $\textcolor{red}{E}$ for which \textcolor{orange}{C is not facing E} holds true.

The success of learning based neural approaches in NLP tasks like machine translation \cite{cho2014learning,sutskever2014sequence,vaswani2017attention}, paraphrase generation \cite{prakash2016neural,gupta2018deep}, dialog modeling \cite{vinyals2015neural,kottur2017exploring}, machine comprehension \cite{wang2017conditional}, logical inference \cite{kim2019semantic} has motivated their use for semantic parsing \cite{kovcisky2016semantic,buys2017robust,cheng2017learning,liu2018discourse,li2018seq2seq} as well. Many such works use the encoder-decoder framework to model it as a sequence transduction task. Since they were designed for solving specific tasks like question answering, such methods \cite{jia2016data,dong2016language} have mainly focussed on confined logical formalism for specific domains such as flight reservation, restaurant booking, etc \cite{wang2015building} capturing limited vocabulary and semantic concepts.

In this paper, we aim at developing a general-purpose open-domain neural first-order logic parser for natural language sentences to examine the capabilities of such models. We train our model by obtaining a large corpus of text-FOL pairs for sentences in SNLI Dataset \cite{bowman2015large} through C\&C parser \cite{clark2007wide} and Boxer \cite{bos2008wide} (discussed later in detail).\footnote{https://github.com/valeriobasile/candcapi} Apart from meaning depiction, parsing sentences to FOL would enable neural models to capture complex relationships between entities resulting in richer embeddings which might be useful in several other NLP tasks. Such an examination would help understand challenges in generating FOL through neural approaches owing to complexities in its representation. Since it is one of the first such exploration for FOL, we treat the popular sequence to sequence model coupled with attention mechanism \cite{bahdanau2014neural} as our baseline. We propose to disentangle the prediction of different types of FOL syntactic entities (unary and binary predicates, variables etc) while parsing sentences and show improvements through performing category type prediction as an auxiliary task. We further show major improvements by explicitly constraining the decoder to align variables across unary and binary predicates. This restricts the model to maintain consistency while expressing standalone entity attributes and relations between them.

Our contributions can be enumerated as: 1) We explore and develop an open domain neural semantic parser to parse natural language sentences to FOL using Seq2Seq framework; 2) We propose disentangled FOL entity type prediction along with FOL parsing under multi-task learning and FOL variable alignment through decoder alignment mechanism. We perform extensive ablation studies to establish the improvements registered; 3) We also aim to release our code, models and large scale dataset used comprising of sentence-FOL mappings to aid further research in FOL based NLP.

\section{Background}
\label{section:background}

\textbf{Text to FOL Conversion : }In this section, we give a brief overview of syntactic-semantic analysis pipeline used for obtaining the mappings data through Boxer \cite{bos2008wide} based on Combinatory Categorial Grammar (CCG) \cite{steedman2011combinatory} and  Discourse Representation Theory (DRT) \cite{kamp2011discourse}. CCG is phrase-level grammar which defines rules for generating constituency-based structures. CCG comprises of syntactically typed lexical items such that each item is a lambda-expression and uses combinatorial logic (lambda calculus) to combine them through the application of combinators. CCG derivation guides semantic composition to obtain Discourse Representation Structures (DRS) from CCG parses. DRS comprises of discourse referents and conditions defined on them which can be recursive. DRS is capable of representing varied linguistic phenomena such as anaphora, presupposition, tense and aspect. These DRSs are compatible and can be converted to FOL through a set of syntactic transformations \cite{bunt2001patrick}. Formally, predicates in FOL are atomic formulas that are combined through logical connectives - logical and ($\land$), logical or ($\lor$) ; and quantifiers. In general, a predicate $P(v_1,v_2, ... , v_n)$ is an n-ary function of variables. There are two types of quantifiers, universal ($\forall$) - which specifies that sub-formula within its scope is true for all instances of the variable and existential ($\exists$) - which asserts existence of at least one instance represented by a variable under which the sub-formula holds true. For example, ``All humans eat" can be represented as
\begin{center}
    $\forall A (\exists B (human(A) \ \land \ eat(B) \ \land \ agent(B,A)))$
\end{center}

Following generalized De Morgan's law \cite{johnstone1979conditions}, universal quantifiers can be represented through existential quantification and negation ($not$) preserving the semantics as

\begin{center}
    $not(\exists A(not(\exists B(human(A) \ \land \ eat(B) \ \land \ agent(B,A)))))$
\end{center}

\vspace{2 mm}
\noindent \textbf{Output and Mapping Format : }Given a text sentence, we obtain the following FOL output.

\vspace{2 mm}
\noindent \textit{\textbf{Sentence :} }``three women are traveling by foot"

\vspace{1 mm}
\noindent \textit{\textbf{Output FOL : } } fol(1,some(A,some(B,some(C,and (r1by(B,A),and(n1foot(A),and(r1agent(B,C),and (v1travel(B),and(n1woman(C),some(D,and(card (C,D),and(c3number(D),n1numeral(D)))))))))))))

\vspace{1 mm}
\noindent Here, the predicates are prefixed with POS-tags \cite{wilks1998grammar} and relation types. Since the output FOL comprises of existential quantifiers and disjunction of atomic formulas only, we convert it into an equivalent mapping as a sequence of predicates, argument variables, scoping symbols (such as ``fol(", ``)", ``not(") and train our models to predict the sequence. We arrange scope symbols in accordance to their nesting level (top most appearing first in the sequence) with further ordering that entities that are part of same scope are arranged as sequence of unary predicates, followed by binary predicates and other nested scoped entities.

\vspace{1 mm}
\noindent \textit{\textbf{Equivalent Mapping : }}fol( n1foot A v1travel B n1woman C c3number D n1numeral D r1by B A r1agent B C card C D )

\section{Proposed Approach}
We model parsing a given sentence into FOL as a sequence to sequence \textit{transduction} problem. Our parser $P$ generates a token in the output FOL representation in a sequential manner by greedily sampling it from a probability distribution conditioned on the input sentence and the previously generated tokens. Our input $X$ consists of a sequence of $m$ tokens $\{x_0, x_1,...,x_m\}$ which get encoded into hidden \textit{contextual} representations by an Encoder. The Decoder, then, generates an output sequence of $n$ tokens $\{y_0, y_1,...,y_n\}$.
\begin{equation}
    P(Y|X) = \prod_{i=0}^{n} P(y_i|y_0..y_{i-1},X)
\end{equation}

\subsection{Encoder}
Our Encoder $E$ is a bidirectional LSTM (biLSTM) which encodes a sequence of input tokens $X: \{x_0,x_1...,x_m\}$ into a sequence of hidden states $H_e : \{h_{e_0},h_{e_1},...,h_{e_m}\}$, $h_{e_i} \in {R}^{d_h}$ to capture contextual information from the input that is eventually used by the decoder to produce the output FOL sequence. The biLSTM block takes word embeddings for the input tokens $E_e: \{e_{e_0},e_{e_1},...,e_{e_m}\}$, $e_{e_i} \in R^D$  as input and processes them to calculate the contextual representations
\begin{equation}
   h_{e_i} = [h_{fe_i};h_{be_i}] = biLSTM(e_{e_0}, e_{e_1},...,e_{e_i})
\end{equation}
where $;$ denotes concatenation operation and $h_{fe_i}, h_{be_i}$ refer to the forward and backward hidden states of the biLSTM.

\subsection{Decoder}\label{decoder}
Decoder $D$ consists of an LSTM which uses the outputs of encoder $E$ along with previously decoded outputs, provided as embeddings $E_d : \{e_{d_0},e_{d_1},...,e_{d_n}\} $ to it as input, to generate a sequence of hidden states $H_d : \{h_{d_0},h_{d_1},...,h_{d_n}\}$.
\begin{equation}
    h_{d_i} = LSTM(e_{d_0}, e_{d_1},...,e_{d_i})
\end{equation}
Attention \cite{bahdanau2014neural} has now become ubiquitous in sequence to sequence models. We consider it to be a part of our baseline model. Following \cite{bahdanau2014neural}, we calculate the weights for encoder-decoder attention using $H_d$ as \textit{queries} while $H_e$ as \textit{keys} as well as \textit{values} (eq. \ref{enc-dec-attn}.
\begin{equation}
    \alpha_{ij} = \frac{e^{h_{d_i}^Th_{e_j}}}{\sum_{k=0}^{m}e^{h_{d_i}^T h_{e_k}}}
\label{enc-dec-attn}
\end{equation}

\begin{figure*}[t]
\centering
\includegraphics[width=0.95\textwidth,height=2.5in]{ACLFOL.png}

\caption{Overview of our architecture showing separate heads (red), category prediction (orange) and alignment mechanism (green and pink). Input to Decoder LSTM (blue) depicts the output of last step being fed at next step. Red arrow between Attention Layer and Decoder depicts standard encoder-decoder attention.}
\label{fig:arch}
\end{figure*}

The encoder-context vector is obtained by taking a weighted sum of encoder's hidden states $H_e$ (eq. \ref{enc-dec-context}).
\begin{equation}
    c_{e_i} = \sum_{j=0}^{m}\alpha_{ij} * h_{e_j}
\label{enc-dec-context}
\end{equation}

The hidden state of the decoder $h_{d_i}$ along with encoder-context vector $c_{e_i}$ is used to predict the final output token at $i^{th}$ step.
\begin{equation}
    s_i = W_o * [h_{d_i};c_{e_i}]
\end{equation}
where $W_o \in R^{V X (d_h+d_c)}$ is output \textit{head} and $d_h$ is the dimension of hidden vector and $d_c = d_h$ is the dimension of context vector.

We train the model on the standard cross-entropy objective while adopting \textit{teacher forcing} methodology \textit{i.e.} giving the inputs to the decoder from ground truth instead of previously decoded tokens while training
\begin{equation}
    L_{CE} (\theta) = -\frac{1}{n}\sum_{0}^n{y_{i}^t* log(P(y_i|y_0..y_{i-1},X))}
\end{equation}
where $y_i^t$ is the target token from ground truth at step $i$ and $\theta$ refers to the trainable model parameters.

\subsubsection{Separate Heads}\label{sep-heads}
The output tokens in an FOL sequence do not all belong to the same token category unlike majority sequence to sequence translation problems which process words. In particular, the output tokens in an FOL sequence can be divided into four major types - Unary Predicates $U$, Binary Predicates $B$, Variables $V$, and Scoped Entities $S$. We create separate vocabularies of sizes $V_u$, $V_b$, $V_v$, and $V_s$ for each category. Apart from variables $V$ which have one-hot embedding, all other types of output tokens have \textit{dense} embeddings. This is because a token of category $V$ does not posses semantic meaning that is shared across all sequences from the output distribution. Thus, they are defined in the context of an FOL sequence only. We represent them through one-hot embeddings to ensure independence between them.

Building on above motivation, we use five different \textit{heads} on top of Decoder LSTM. While one head $T$ decides what type of token is being generated at a given decoding step, the other heads decode the probabilities of different types of tokens.
\begin{equation}
    o_{i}^u = softmax(W_u * h_{d_i})
\end{equation}
\begin{equation}
    o_{i}^b = softmax(W_b * h_{d_i})
\end{equation}
\begin{equation}
    o_{i}^v = softmax(W_v * h_{d_i})
\end{equation}
\begin{equation}
    o_{i}^s = softmax(W_s * h_{d_i})
\end{equation}
\begin{equation}
    t_{i} = softmax(W_t * h_{d_i})
\end{equation}
where $W_x \in R^{V_x X d_h}$ and $x : \{u,b,v,s\}$. We also treat different \textit{categories} as words in a vocabulary of size $V_c$ and therefore, $W_t \in R^{V_c X d_h}$

We, thus, train the model on an additional \textit{auxiliary} task of predicting the type of the token being generated at each step. Hence, the overall cross-entropy objective to decode the correct type at all steps becomes
\begin{equation}
    L_{aux} (\theta, \phi) = -\frac{1}{n}\sum_{0}^n{t_i^t * log(t_i))}
\end{equation}
where $t_i^t$ is the target type (from ground truth) of token to be predicted at this step and the probability of generating token $y_i$ is given by $o_i^{x[t_i]}$. $\phi$ refers to additional decoder parameters introduced in the model. Thus, our overall objective is now a sum of both cross entropy and auxiliary objective:
\begin{equation}
    L_{sep}(\theta,\phi) = L_{CE}(\theta) + L_{aux}(\theta,\phi)
\end{equation}

\subsection{Decoder Self Attention}
One of the key challenges for the model is to identify the relationship between the \textit{variables} it generates. A variable $A$ that is an argument in a binary predicate should be aligned with the same variable used as an argument in a unary predicate previously. One of the ways to achieve such alignment is through decoder self-attention which is an extension of the regular encoder-decoder attention. In this case, \textit{queries}, \textit{keys} and \textit{values} - all are decoder hidden states $H_d$. Therefore, we determine decoder context vector $c_d$ along with the encoder-context vector. However, one of the key differences between encoder-decoder and decoder self-attention is that while encoder-decoder attention can be applied to the whole input, decoder self-attention can only be applied on the hidden states which have been decoded so far. Just like encoder-context, decoder context is calculated by taking a weighted sum of decoder hidden states

\begin{equation}
    \beta_{ij} = \frac{e^{h_{d_i}^Th_{d_j}}}{\sum_{k=0}^{m}e^{h_{d_i}^T h_{d_k}}}
\end{equation}
\begin{equation}
        c_{d_i} = \sum_{j=0}^{i-1}\beta_{ij} * h_{d_j}
\end{equation}

The linear head on the decoder now uses both encoder and decoder contexts along with decoder hidden state to generate the final output
\begin{equation}
    s_i = W_o * [h_{d_i};c_{e_i};c_{d_i}]
\end{equation}
where $W_o \in R^{d_h + 2*d_c}$
\subsubsection{Alignment Mechanism} \label{align-mech}
Through decoder self attention, the model does not receive any explicit signal on alignment and relies only on cross-entropy objective to identify such relations between different \textit{variables}. In order to provide an explicit signal to the model, we introduce \textbf{Alignment Mechanism}.

At each \textit{variable} decoding step, along with decoding the type of the token \textit{i.e.} variable, a linear classifier makes the decision whether this variable is aligned with any previously decoded token/variable or is an entirely new variable being generated at this step
\begin{equation}
    A_{d_i} = sigmoid(W_{ali} * h_{d_i})
\end{equation}
where $W_{ali} \in R^{1Xd_h}$. Depending on this decision by the classifier, an alignment mechanism similar to decoder self-attention performs the relational mapping between a previously decoded variable and the variable which is currently being decoded. This mapping is performed only for the \textit{variables} and not for any other category. All the previously generated hidden states of the decoder are linearly projected into a different space before calculating the position of the token with which the variable is aligned. The projection is performed to reduce the interference with encoder-decoder attention due to alignment mechanism training.
\begin{equation}
    h_{d_i}^{a} = W_{proj}*h_{d_i}
\end{equation}

The probabilities of whether a particular step $j$ aligns with the current decoding step $i$ is calculated with an attention-like formulation.
\begin{equation}
    \gamma_{ij} = \frac{e^{h_{d_i}^{a T} h_{d_j}^a}}{\sum_{k=0}^{m}e^{h_{d_i}^{a T} h_{d_k}^a}}
\end{equation}
\begin{equation}
    c_{a_i} = \sum_{j=0}^{i-1}\gamma_{j} * h_{d_j}^a
\label{weight-indices}
\end{equation}

For every other category of tokens but \textit{variables}, the decoder heads remain the same. However, for \textit{variables}, we first calculate aligned hidden state value as
\begin{equation}
    h_{d_i}^{ali} = A_{d_i} * c_{a_i} + (1-A_{d_i}) * h_{d_i}
\label{weight-decision}
\end{equation}
 The output is, then, calculated as
 \begin{equation}
     o_v^i = W_o * [h_{d_i}^{ali};c_{e_i}]
 \end{equation}
In order to provide explicit signal during training, we train $\gamma$ and $A_{d_i}$ on the target alignment positions and decisions with a cross entropy objective
\begin{multline}
    L_{dec}(\theta,\phi,\zeta) =
    -\frac{1}{n}\sum_{i=0}^{n}(A_{d_i}^t*log(A_{d_i}) + \\(1-A_{d_i}^t)*log(1-A_{d_i}))
\end{multline}

\begin{equation}
    L_{pos}(\theta,\phi,\zeta)  = -\frac{1}{n}\sum_{i=0}^{n}\sum_{j=0}^{i-1}A_{d_i}^t*\gamma_{ij}^t*log(\gamma_{ij})
\end{equation}
where $A_{d_i}^t$ and $\gamma_{ij}^t$ refer to ground truth decision and alignment position values and $\zeta$ refers to additional parameters introduced due to alignment mechanism.
Therefore our overall loss becomes
\begin{equation}
    L_{align}(\theta,\phi,\zeta) = L_{sep} + L_{dec} + L_{pos}
\end{equation}

\section{Experiments}

\subsection{Dataset}
We collated a subset of SNLI \cite{bowman2015large} corpus by extracting sentences from both \textit{premise} and \textit{hypothesis} for a \textit{limited} number of examples. Eliminating duplicates, we \textit{prepared} (Refer Section \ref{section:background}) two versions of the dataset - \textbf{Small} and \textbf{Large} to examine if the proposed improvements remain consistent even on small data. In the smaller version, we prepared 138,346 instances while in the larger one, we prepared 255,501 instances for training. We used the development and test sets of SNLI \textit{as provided} but eliminated the duplicates resulting in evaluation set having 10,691 instances and test set having 10,633 instances. \\

\subsection{Implementation}
We used Pytorch\footnote{https://pytorch.org} library for implementing an auto-differentiable graph of our computations. All the models were trained with an Adam Optimizer\cite{kingma2014adam} initialized with a learning rate of $0.001$ with a decay rate of $10^{-4}$. We use an embedding size $D$ = $100$ for encoder as well as decoder embeddings in the baseline model. In our separated heads model, $D$ remained the same for encoder embeddings. However, on the decoder side, Unary and Binary predicates have an embedding size of 100 each while variable and type
embeddings are one-hot having the number of dimensions equal to their respective vocabulary sizes. Scoped entities, being very less in number, were encoded with an embedding size of 50. Our final input embedding is a concatenation of Unary, Binary, Variable, Scope and type embeddings. All dense embeddings are randomly initialized and trained from scratch.\footnote{We experimented with GloVe embeddings but it did not give further improvements} We used $d_h= d_c = 400$, $m=100$ and $n=30$.

\subsection{Results and Discussion}

\subsubsection{Evaluation Framework}
We evaluate different models through estimating the accuracy of complete match between gold standard FOL and predicted output. Due to the complex nature of the task, it is less likely that the model generates exactly the same FOL. To mitigate this, we propose to evaluate the degree of partial match between two FOLs following the intuition behind D-match and Smatch \cite{cai2013smatch}, which are widely used to evaluate AMR graphs and DRGs. We align two FOLs in bottom up manner beginning with variables. For aligning two variables, it is required that the corresponding predicates' name (in which they appear as arguments) and argument positions match. Subsequently, while aligning two predicates, we check if their arguments are aligned and their names are same. We continue to follow the same process where we align nested scope symbols (``not(" etc.). In particular, given an expected scoped entity, we determine the predicted scoped entity having maximum alignment with it based on the count of other aligned predicates and scoped entities that are contained inside them. Given an FOL, we decompose it into related pairs of the form $(n_1,n_2)$ such that $n_2$ appears inside the scope of $n_1$. For instance, a variable that is an argument in a predicate or a predicate appearing inside a scoped entity. Consequently, we estimate the number of pairs in expected FOL that can be matched with pairs in predicted FOL based on the constraint that corresponding entities in the pairs should be aligned. We select the alignment with maximum matches and report metrics (precision-recall and F1 over pair-matching) as evaluation criteria along with overall FOL accuracy.

\subsubsection{Comparison with Baseline and Ablation Studies}

We conduct a range of experiments and evaluations on different models. We show our results on both \textit{development} and \textit{test} sets in Table \ref{result-table-dev}, \ref{result-table-test}, \ref{result-table-dev-150}, and \ref{result-table-test-150}. Our \textbf{Vanilla (Baseline)} model consists of a biLSTM Encoder and a plain LSTM decoder as described in Section \ref{decoder} coupled with an encoder-decoder attention mechanism. Performing disentanglement, our \textbf{Separate Heads} model uses different linear heads on the top of LSTM decoder for different category of tokens as discussed in Section \ref{sep-heads}. Our final proposed model \textbf{Separate Heads + Align} uses our alignment mechanism on the top of \textbf{Separate Heads} and utilises the disentangled variable prediction mechanism coupled with an alignment mechanism to effectively identify the relationships between variables in binary predicates and their unary counterparts. We also conduct ablations on the \textbf{Vanilla} and \textbf{Separate Heads} models by incrementally adding both decoder self-attention and alignment mechanisms.

Evidently, our final model \textbf{Separate Heads + Align} convincingly outperforms all described models and improves the baseline by $\sim8$ F-1 points. Decoder self-attention, even though, improves \textbf{Vanilla} Model does not provide any improvements when used with \textbf{Separate Heads}. This can be attributed to its inability to incorporate decoder level information which probably becomes factorized automatically during training through using separate heads. However, it provides improvements over \textbf{Vanilla} by a good margin but still only matches or remains inferior to the standalone \textbf{Separate Heads} model. \textbf{Align Mechanism} manages to provide a huge boost to the \textbf{Separate Heads} model by improving it by $\sim 5$ F-1 points. However, performance deteriorates when used with \textbf{Vanilla} model since its ability to align variables \textit{only} vanishes in this setup which we find critical for its working. We further note that by increasing the size of training data, the performance increases uniformly with our final model achieving the best F-1 of $\sim 73$ and an overall accuracy of $\sim 63\%$.

\setlength\tabcolsep{4.5pt}
\begin{table}[hbt!]
\centering
\resizebox{225pt}{!}{%
\begin{tabular}{lllll}
\hline \textbf{Model} & \textbf{Precision} & \textbf{Recall} & \textbf{F-1} & \textbf{Accuracy}\\
\hline
\textbf{Vanilla (Baseline)}& 65.48 & 65.15 & 65.31 & 59.26\\
+ Self Attention & 67.86 & 66.70 & 67.28 & 60.74\\
+ Align Mechanism & 62.13 & 61.06 & 61.59 & 56.98\\
\hdashline
\textbf{Separate Heads} & 68.48 & 66.81 & 67.64 & 60.77\\
+ Self Attention & 67.11 & 65.87 & 66.48 & 60.02\\
\hdashline
\textbf{Separate Heads + Align} & \textbf{73.68} & \textbf{72.17} & \textbf{72.92} & \textbf{63.26}\\
\hline
\end{tabular}%
}
\caption{\label{result-table-dev} Results showing overall accuracy and F1-Scores of different models (trained on \textbf{Large} dataset) on development dataset }
\end{table}
\setlength\tabcolsep{4.5pt}
\begin{table}[hbt!]
\centering
\resizebox{225pt}{!}{%
\begin{tabular}{lllll}
\hline \textbf{Model} & \textbf{Precision} & \textbf{Recall} & \textbf{F-1} & \textbf{Accuracy}\\
\hline
\textbf{Vanilla (Baseline)}& 66.43 & 65.84 & 66.13 & 60.45\\
 + Self Attention & 68.56 & 67.23 & 67.89 & 61.18\\
 + Align Mechanism & 61.74 & 60.60 & 61.17 & 56.79\\
 \hdashline
\textbf{Separate Heads} & 69.18 & 67.63 & 68.40 & 61.14\\
 + Self Attention & 66.92 & 65.69 & 66.30 & 60.12\\
 \hdashline
\textbf{Separate Heads + Align} & \textbf{74.05} & \textbf{72.53} & \textbf{73.28} & \textbf{63.24}\\
\hline
\end{tabular}%
}
\caption{\label{result-table-test} Results showing overall accuracy and F1-Scores of different models (trained on \textbf{Large} dataset) on Test dataset }
\end{table}

\setlength\tabcolsep{4.5pt}
\begin{table}[hbt!]
\centering
\resizebox{225pt}{!}{%
\begin{tabular}{lllll}
\hline \textbf{Model} & \textbf{Precision} & \textbf{Recall} & \textbf{F-1} & \textbf{Accuracy}\\
\hline
\textbf{Vanilla (Baseline)}& 59.54 & 58.14 & 58.83 & 52.99\\
\textbf{Separate Heads} & 62.00 & 60.58 & 61.28 & 55.20\\
\textbf{Separate Heads + Align} & \textbf{65.75} & \textbf{64.36} &\textbf{65.05} &\textbf{55.30} \\
\hline
\end{tabular}%
}
\caption{\label{result-table-dev-150} Results showing overall accuracy and F1-Scores of different models (trained on \textbf{Small} Dataset) on development dataset }
\end{table}

\setlength\tabcolsep{4.5pt}
\begin{table}[hbt!]
\centering
\resizebox{225pt}{!}{%
\begin{tabular}{lllll}
\hline \textbf{Model} & \textbf{Precision} & \textbf{Recall} & \textbf{F-1} & \textbf{Accuracy}\\
\hline
\textbf{Vanilla (Baseline)}& 59.87 & 58.41 & 59.13 & 52.92\\
\textbf{Separate Heads} & 62.56 & 61.10 & 61.82 & 55.94\\
\textbf{Separate Heads + Align} &\textbf{66.45} &\textbf{65.05} & \textbf{65.74}&\textbf{56.10} \\
\hline
\end{tabular}%
}
\caption{\label{result-table-test-150} Results showing overall accuracy and F1-Scores of different models (trained on \textbf{Small} Dataset on test dataset }
\end{table}

\subsubsection{Analysis}
We perform additional experiments to analyse the results observed.
We conduct two sets of analysis - Variation of F-1 score with input length and \textit{Perturbed} training to establish the robustness of our proposed method.
\begin{figure}[htbt!]
    \centering
    \includegraphics[width=1.1\linewidth]{Plot.png}
    \caption{Variation of output F-1 Score with input length on Test Dataset}
    \label{fig:plot}
\end{figure}

\noindent\textbf{Variation with Input Length:} Evidently, our proposed models are relatively much more robust to increase in length in the input sentence as shown in Fig. \ref{fig:plot}. This can be attributed to many factors - increased model capacities as well as their abilities to process different categories of output tokens separately giving better long range dependencies and less confusion in generating many variables over FOL owing to better alignment across the sequence.
\setlength\tabcolsep{4.5pt}
\begin{table}[hbt!]
\centering
\resizebox{225pt}{!}{%
\begin{tabular}{lllll}
\hline \textbf{Model} & \textbf{Precision} & \textbf{Recall} & \textbf{F-1} & \textbf{Accuracy}\\
\hline
\textbf{Vanilla(Baseline)} & 63.75 & 62.32 & 63.03 & 57.95\\
\hdashline
\textbf{Separate Heads} & 67.39 & 65.59 & 66.48 & 61.44\\
\textbf{Separate Heads + Align} & 72.90 & 71.95& 72.42 & 63.14 \\
\hline

\end{tabular}%
}
\caption{Results on Test set showing both accuracy and F1-Scores with perturbed training}
\label{results-adversary}
\end{table}

\noindent\textbf{Perturbed Training:} It has been noticed in literature (\cite{jia2017adversarial, niven2019probing} that neural models sometimes exploit trivial patterns in outputs/inputs to fool and provide pseudo-improved results. One such pattern could be presence of variables like \textit{A} and \textit{B} with some specific unary and binary predicates. In order to disturb such patterns, we randomly permute the presence of such variables in the ground truth during training. Our \textbf{baseline} model indeed shows a significant drop in results (Table \ref{results-adversary}). On the other hand, our other two main models do not show such large drop proving their robustness to such disturbances.

\section{Related Work}
Early semantic parsers were majorly rule based \cite{johnson1984natural,woods1973progress,thompson1969rel} using grammar systems \cite{waltz1978english,hendrix1978developing}, employing shallow pattern matching \cite{johnson1984natural} and parse tree to generate database query language \cite{woods1973progress}. These were succeeded by data driven learning techniques which use language data paired with meaning representations and can be broadly classified into statistical methods \cite{thompson2003acquiring,zettlemoyer2012learning,zelle1996learning,kwiatkowski2010inducing} and neural approaches \cite{kovcisky2016semantic,dong2016language,jia2016data,buys2017robust,cheng2017learning,liu2018discourse,li2018seq2seq}. \cite{zettlemoyer2012learning} proposed to use sentences and their lambda calculus expressions to learn a log linear model through probabilistic CCG to rank different parses for a given sentence using simple features such as count of lexical entries. Additionally, captioned videos have been used to perform visually grounded semantic parsing \cite{ross2018grounding}. Feedback based semantic parsing has been done to facilitate continuous improvement in the quality of parse through conversations \cite{artzi2011bootstrapping} and user interaction \cite{iyer2017learning,lawrence2018improving}.

Neural approaches alleviate the need for manually defining lexicons and can further be categorized based on the structure of parse into sequential parse prediction \cite{jia2016data,kovcisky2016semantic} and graph structure decoding which tailor network architecture to utilize the syntactic structure of meaning representation. \cite{yin2017syntactic,alvarez2016tree,rabinovich2017found}. \cite{dong2016language} proposed SEQ2TREE to generate domain-specific hierarchical logical form by introducing parenthesis token and parent connections to recursively generate sub-trees. \cite{rabinovich2017found} introduced a dynamic decoder whose components are composed depending on generated tree parse. \cite{liu2018discourse} parse DRSs using dedicated hierarchical decoders to generate partial structure first before the semantic content. We instead make the model disambiguate syntactic types (unary and binary predicates, variables, scope symbols) through performing category type prediction as an auxiliary task and using separate prediction heads. Constrained decoding using target language syntax and grammar rules has been explored \cite{yin2017syntactic,xiao2016sequence}. Copy-mechanism \cite{gu2016incorporating} has been used to facilitate the generation of out of vocabulary entities through encoder attention \cite{jia2016data}. However, our variable alignment mechanism is different since it constrains the model to align binary predicate arguments with previously generated unary structures (alignment happening at decoder level) through specifying explicit loss on whether to align and where to align.

\section{Conclusion}
In this work, we examined the capability of neural models on the task of parsing First-Order Logic from natural language sentences. We proposed to disentangle the representations of different token categories while generating FOL output and used category prediction as an auxiliary task. We utilized token factorization to build an alignment mechanism which effectively manages to capture the relationship between variables across different predicates in FOL. Our analysis showed the difficulties faced by neural networks in modeling FOL and ways to tackle them. We also experimented by introducing a perturbation in inputs in order to examine the robustness of different proposed models. In a bid to promote research further in the area, we aim to release our code as well as data publicly.

redpajama_set_name: RedPajamaArXiv


text:
EU politician shouts 'Nazis' in German airport spat
German police said Thursday they would file a complaint against a European Parliament vice president for calling Frankfurt airport customs officers "Nazis" and exclaiming "Heil Hitler".
Jacek Protasiewicz, 46, of Poland's governing centrist Civic Platform party, confirmed Tuesday evening's heated altercation, but denied a German media report that he was drunk.
"A young German customs officer stopped me," Protasiewicz later told Poland's RMF24 radio news website. "His haircut was more reminiscent of a football fan than a state official.
"But that's not what upset me. He asked me for my documents. I gave him my passport. It says I'm a member of the European Parliament. He was obliged to know who I am.
"I heard the word 'raus' (out) and I really did become furious. I told him that it sounds very bad in my country. In that country, it is associated with 'Heil Hitler'."
Germany's Bild daily said Protasiewicz was visibly drunk at the time and asked the two officers, "Were you ever in Auschwitz?", referring to the Nazi concentration camp in occupied Poland.
Speaking separately in the European Parliament, Protasiewicz said "the Bild publication is a lie and it is offensive".
A German police spokesman told AFP that in the incident "two customs officers were alleged to have been insulted", without repeating the alleged insults.
"His personal details were taken and his diplomatic status determined. Police will pass on a complaint to the prosecutor," the spokesman said, adding that no alcohol test was performed on Protasiewicz.
Polish Prime Minister Donald Tusk said in Warsaw that "regardless of how inappropriate German customs officers or police had behaved, the behaviour of Jacek Protasiewicz was also inappropriate".

redpajama_set_name: RedPajamaCommonCrawl


I've taken photos of my outfit wearing this watch,but there very grainy as it was already dark mid afternoon when we attempted to take pictures,but managed to take a couple pics inside that are a bit clearer. I wish I was more photograpy/photoshop savy so could upload my outfit too :( but I've just realised how poor they are,maybe I'll post them another time. I have just stopped by bornprettystore and seen this watch has gone down to $5.99 which is insane,how is it possible to be so cheap?


Logitech m337 bluetooth mouse red compare prices, this product is a popular item this season. this product is a new item sold by LDPL store and shipped from Singapore. Logitech M337 Bluetooth Mouse (Red) comes at lazada.sg which has a cheap price of SGD44.50 (This price was taken on 09 June 2018, please check the latest price here). do you know the features and specifications this Logitech M337 Bluetooth Mouse (Red), let's wait and watch the details below.
For detailed product information, features, specifications, reviews, and guarantees or other question that is more comprehensive than this Logitech M337 Bluetooth Mouse (Red) products, please go straight to the owner store that is due LDPL @lazada.sg.
LDPL is a trusted seller that already is skilled in selling Mice products, both offline (in conventional stores) and online. many of the clientele are extremely satisfied to acquire products through the LDPL store, that could seen with all the many upscale reviews given by their buyers who have bought products inside the store. So there is no need to afraid and feel focused on your product not up to the destination or not in accordance with what is described if shopping within the store, because has lots of other clients who have proven it.
In addition LDPL also provide discounts and product warranty returns in the event the product you purchase does not match whatever you ordered, of course together with the note they offer. Including the product that we're reviewing this, namely "Logitech M337 Bluetooth Mouse (Red)", they dare to give discounts and product warranty returns if the products they sell don't match precisely what is described.
So, if you want to buy or look for Logitech M337 Bluetooth Mouse (Red) then i highly recommend you acquire it at LDPL store through marketplace lazada.sg.
Why should you buy Logitech M337 Bluetooth Mouse (Red) at LDPL shop via lazada.sg?
Obviously there are several advantages and benefits that exist while you shop at lazada.sg, because lazada.sg is really a trusted marketplace and also have a good reputation that can provide security from all of kinds of online fraud. Excess lazada.sg when compared with other marketplace is lazada.sg often provide attractive promotions such as rebates, shopping vouchers, free postage, and frequently hold flash sale and support that's fast and which is certainly safe. as well as what I liked is simply because lazada.sg can pay on the spot, which has been not there in almost any other marketplace.


1. Only comprehensive solution in the world for building and maintaining data driven graphics intensive real GUI applications for web (i.e. to run in browsers). Today there exists no other comprehensive GUI solution for building large GUI applications for web, which is comparable to the GUI platforms (e.g. Windows-API or Java/Swing) that are used for building desktop GUI applications.
2. Our GUI library is the simplest solution for building GUI applications for web. Even a junior programmer or collage graduate having basic training in Java can start creating large GUI application from day one. Today there exists many incomplete solutions for building data driven GUI application for web, each of them are too complex. That is, they require substantial learning such as mastering new tag-library, new framework and programming models.
3. Only GUI library in the world designed to create real-software-components for achieving real CBSD (Component Based Design for software). Even the desktop GUI libraries can't create real-software-components for achieving real CBSD, which is to implement about 90% of the features and functionality in real-software-components. Just like physical functional components, real-software-components eliminate spaghetti code. Each real-software-component can be redesigned and tested individually outside of the product free from spaghetti code. So it requires a fraction of the cost and time to make large changes, since most products require changing frequently and/or continuously.
Our product offers many other such unique advantages over existing products (e.g. Wicket, GWT, ZK or Vaadin). For example, while documentation for their framework and programming models span several hundred pages, our documentation is under two pages (not including, of course, reference to set-methods for each reusable class to configure each GUI component).
We are offering very simple instruction for using each of the reusable GUI components. For example, see chart gallery at: http://www.pioneer-soft.com/Bhalu%20Docs/Samples/BhaluCharts.html. Please click on the link for reviewing a half-page documentation for start using Bhalu's reusable Java-classes. We are planning to market this product using Freemium model. So our first version will be free for early adopters.


Integrating the consumption of agro-industrial residues with satisfactory consumers is an opposition that, may affect the financial dividend of the frozen meat manufacturing, aside from producing products with diversified nutritional values and satisfying technical characteristics. Thence, we aimed in this research to elaborate and appraise the physicochemical and microbiological characteristics of beef hamburger with the addendum 2 and 4 % of red grape pomace powder (RGPP) throughout its stockpiling at (-18 ± 2°C) over a period 12 weeks. A significant influence on pH value was observed at the beginning (zero time), however the control sample, 2% and 4% had pH values of 6.68, 6.41 and 6.22, respectively. Meantime, there was a significant influence of (RGPP) on thiobarbituric acid (TBA) evidenced on 4th week. The results showed that the microbial growth increased as a function to storage period for all and the addendum of RGPP in beef burger caused a reduction in total bacterial count in comparison with control. Thus, in general, it can be concluded that addendum of RGPP is an efficacious mean to improve burger quality and increasing its storage stability.
Essa RY, Elsebaie E. Effect of Using Date Pits Powder as a Fat Replacer and Anti-Oxidative Agent on Beef Burger Quality. J Food and Dairy Sci, Mansoura Univ 2018;9:91–6.
Oliveira RBSd, Lucia FD, Ferreira EB, et al. Quality of beef burger with addition of wet okara along the storage. Ciência e Agrotecnologia 2016;40:706–17.
Dave D, Ghaly AE. Meat spoilage mechanisms and preservation techniques: a critical review. American Journal of Agricultural and Biological Sciences 2011;6:486–510.
Quevedo R, Pedreschi F, Valencia E, et al. Kinetic modeling of deterioration of frozen industrial burgers based on oxidative rancidity and color. Journal of Food Processing and Preservation 2018:e13655.
Pereira D, Pinheiro RS, HELDT LFS, et al. Rosemary as natural antioxidant to prevent oxidation in chicken burgers. Food Science and Technology (Campinas) 2017:0-0.
Nowak A, Czyzowska A, Efenberger M, et al. Polyphenolic extracts of cherry (Prunus cerasus L.) and blackcurrant (Ribes nigrum L.) leaves as natural preservatives in meat products. Food microbiology 2016;59:142–9.
Nardoia M, Ruiz-Capillas C, Herrero AM, et al. Effect of added grape seed and skin on chicken thigh patties during chilled storage. International Journal of Food and Nutritional Science 2017;4: 0-0.
Cosansu S, Juneja VK. Growth of Clostridium perfringens in sous vide cooked ground beef with added grape seed extract. Meat science 2018;143:252–6.
Cagdas E, Kumcuoglu S. Effect of grape seed powder on oxidative stability of precooked chicken nuggets during frozen storage. Journal of food science and technology 2015;52:2918–25.
Mehta N, Ahlawat S, Sharma D, et al. Novel trends in development of dietary fiber rich meat products—a critical review. Journal of Food Science and Technology 2015;52:633–47.
González-Centeno M, Comas-Serra F, Femenia A, et al. Effect of power ultrasound application on aqueous extraction of phenolic compounds and antioxidant capacity from grape pomace (Vitis vinifera L.): experimental kinetics and modeling. Ultrasonics sonochemistry 2015;22:506–14.
Aleson-Carbonell L, Fernández-López J, Pérez-Alvarez J, et al. Characteristics of beef burger as influenced by various types of lemon albedo. Innovative Food Science & Emerging Technologies 2005;6:247–55.
A.O.A.C. Association of Official of Analytical Chemists, Official Methods of Analysis. 18th Ed., Pub. By the A.O.A.C.: Arlington, Virginia, 2220 USA., 2010.
Goupy P, Hugues M, Boivin P, et al. Antioxidant composition and activity of barley (Hordeum vulgare) and malt extracts and of isolated phenolic compounds. Journal of the Science of Food and Agriculture 1999;79:1625–34.
Pinero M, Parra K, Huerta-Leidenz N, et al. Effect of oat's soluble fibre (β-glucan) as a fat replacer on physical, chemical, microbiological and sensory properties of low-fat beef patties. Meat science 2008;80:675––80.
Trindade R, Mancini-Filho J, Villavicencio A. Natural antioxidants protecting irradiated beef burgers from lipid oxidation. LWT-Food Science and Technology 2010;43:98–104.
Georgantelis D, Blekas G, Katikou P, et al. Effect of rosemary extract, chitosan and α-tocopherol on lipid oxidation and colour stability during frozen storage of beef burgers. Meat Science 2007;75:256–64.
Howard L, Talcott S, Brenes C, et al. Changes in phytochemical and antioxidant activity of selected pepper cultivars (Capsicum species) as influenced by maturity. Journal of Agricultural and Food Chemistry 2000;48:1713–20.
Silvia PM, Ioan PM. The Chemical Composition Assessment Of The Feteasc Neagr Grape Pomace And Its Fractions Obtained From Wine Industry In Different Years. Analele Stiintifice ale Universitatii" Al I Cuza" Din Iasi(Serie Noua) Sectiunea 2 a Genetica si Biologie Moleculara 2015;16:167.
Lafka T-I, Sinanoglou V, Lazos ES. On the extraction and antioxidant activity of phenolic compounds from winery wastes. Food Chemistry 2007;104:1206–14.
Kalaikannan A, Anjaneyulu A, Santhi D. Effect of egg proteins on the quality and refrigerated storage life of chicken patties made with broiler‐spent hen meat and by‐products. International journal of food science & technology 2007;42:579–86.
García ML, Calvo MM, Selgas MD. Beef hamburgers enriched in lycopene using dry tomato peel as an ingredient. Meat science 2009;83:45–9.
of lycopene-containing foods. International journal of food sciences and nutrition 2001;52:143–9.
ESS. Egyptian Standard Specifications, No. 439/2005, Thermally treated.treated poultry meat products. 2005.


Dogtown Ghost Town Site, Cape Ann (between Gloucester and Rockport), MA .
Little People Village Ghost Town Site, Middlebury, CT.
Bara-Hack Colonial Ghost Town Site, Pomfret, CT.
Von Minden Hotel, Schulenburg, TX.
Bell Bottom Bridge, Marlin, TX.
The Falls Hotel, Marlin, TX.
Mount Vernon Cemetery Road, Mount Vernon Ghost Town Site, TX.
Buffalo Soldier Cemetery, Robertson/Falls County, TX.


the city of Herndon is positioned at the western fringe of Virginia's picturesque Fairfax County. within the 18th and early nineteenth centuries, this a part of Fairfax County used to be basically agricultural, and as extra settlers arrived farming flourished. Early citizens came across woods, flat open fields, outcroppings of rocks, and viable soil through the quarter. via 1857, the install of the railroad line introduced summer time citizens, commuters, and genuine property builders to the world. Residential progress persevered into the twentieth century, as extra Washington, D.C. staff selected Herndon as a handy city from which they can travel to their jobs. From 1959 to 1961, the railroad line skilled busy years whilst it was once used to haul sand and building fabrics to construct neighboring Dulles Airport, which opened in 1962. even though the sector has develop into a middle of web expertise, with a number of excessive tech businesses positioned within the region, Herndon nonetheless keeps a small-town charm.
Although the Civil battle led to April 1865, the clash among Unionists and Confederates persevered. The bitterness and rancor due to the cave in of the Confederacy spurred an ongoing cycle of hostility and bloodshed that made the Reconstruction interval a violent period of transition. The violence was once so pervasive that the government deployed devices of the U.
Situated in Florida�s lake area, the town of Umatilla sits on a sandy ridge pocked through 1000's of lakes and depressions of sinkhole beginning that leak water into the limestone Floridan aquifer, which, in flip, is tired through a variety of within sight artesian springs. whilst Florida completed American statehood in 1845, following approximately centuries of ceding via the French, British, and Spanish through treaties, vital Florida used to be the final frontier of the japanese usa.
Overlook dreary dates and uninteresting evidence. extra Montana Moments serves up a clean batch of the main humorous, strange, and fascinating tales from Montana's historical past. Meet the colourful solid of the well-known and not-so-famous desperadoes, vigilantes, madams, and darned strong women and men (and a couple of critters) who made the state's background.
Taney County, situated in southern Missouri at the Arkansas kingdom line used to be tough state with rolling limestone hills and ridges. It was once lined with timber, underbrush and rife with steep rocky cavernous ravines. Cool transparent springs and creeks flowed out of those ravines and valleys and into the White River.


TechniShow 2018- Utrecht, Netherlands | Y.T. / YT / YIH TROUN News and Events | YIH TROUN ENTERPRISE CO., LTD.
TechniShow 2018- Utrecht, Netherlands / Y.T. has been dedicated to development of new product design and increase product competitiveness.
Techni-Show is the largest and most important exhibition in the Benelux area in the field of industrial production technology, metal, tools and appliances processing and treatment. The fair offers the opportunity to meet up with the entire network of the industrial production industry and to meet new contacts in a extremely efficient way. A large variety of exhibitors and products is a garantee for visitors to get extensive informed. TechniShow is the largest and most important trade show in the field of industrial production technology, treatment and processing of metals, plastics, accessories and tools in the Benelux area.


How ironic it is that the more we try to overcome resistance, the more we actually create it.
You know what I mean….our prospect raises an objection, and the first thing we do is think about how we can neutralize it by cleverly overcoming it.
Like many people who sell, when we hear a tough objection, we immediately panic and start thinking "Roadblock ahead!" But trying to counter objections by selling harder really can lose you the sale.
My article shows you that thinking about "concerns" rather than "objections" will not only help you learn your prospects' truth but will also decrease your stress level.
Traditional approaches to selling teach us that we're suppose to "counter" or "get past" or "overcome" objections. Why? Because we've already decided that our prospect should buy what we have to offer. So we try to convince them that what they're telling us shouldn't matter.
The problem is, that's where you often lose them, because reacting to "objections" almost inevitably lets prospects pigeonhole you as "salesperson". Think about it for a moment. When prospects raise an objection and you cleverly dismiss it, what often happens? They come up with even more objections. When you dismiss those, they may suddenly remember that they have another appointment, or that they have to make an important call. Or they may agree to your offer….and then a day or two later you get a message that they're not going to go forward after all. That's probably the most demoralizing outcome of all, because you thought you "had" that sale, and they've reneged on you.
It's about sales pressure. When you're so focused on making the sale that you counter a prospect's objections, you're pressuring them. It's that simple.
I'd like to share with you a different perspective on how to view objections.
Objections aren't roadblocks, red flags, or stop signs. They're an opportunity to learn more about your prospect's truth so you and they can decide whether the solution you're offering can solve their problem or issue. In fact, we should probably stop using the term "objections" entirely and start talking about "concerns", because that's what objections really are. But for you to be able to look at objections this way, you have to let go of the traditional goal of selling, which is to make the sale.
When we adopt the mindset that objections are another way to learn about a prospect's truth, we stop panicking and falling into the trap of pressuring them that chases them away. When we respond to objections in a way that invites them to share more about their situation with us, we sustain and enhance the relationship of mutual trust and openness we've shared so far. When we don't fall into the traditional "I've got to rescue this sale" reaction, we free ourselves to continue the process of discovering whether we can help solve a problem.
Here's another advantage of reframing how you view objections-they give you another opportunity to learn whether your prospect is a match for what you have to offer. When you stop trying to overcome objections and just listen, you may hear that there really is a problem around whether your product or service is a fit for them. In that case, you can talk further, or you may decide it would be best to wish them well and move on. This means that you can make better use of your time.
Here is a common objection and how you can respond in a way that avoids introducing sales pressure and open the conversation to more exploration of your prospect's truth.
By inviting the other person to tell you more, instead of challenging or denying how they view things, you're validating their viewpoint and reopening the conversation around the idea of why they feel the price is high. By not trying to counter the "objection," you allow the dialogue to move back to a discussion that centers around whether you're a good match for each other.
If you consider a different mindset that looks at "objections" as "concerns" you too may find that they can turn into gateways instead of roadblocks.


text:
Wattam is still alive, coming in 2018
Katamari Damacy creator Keita Takahashi's friendship game will be at PAX
By Michael McWhertor@MikeMcWhertor Aug 31, 2017, 10:02am EDT
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Wattam, the next game from Katamari Damacy and Noby Noby Boy creator Keita Takahashi and studio Funomena, is coming to consoles and PC next year. The game about a green, cube-shaped mayor unlocking new friends has been picked up by publisher Annapurna Interactive, after Sony dropped the title.
Annapurna Interactive and Funomena will show the latest version of Wattam at PAX West in Seattle this weekend.
Wattam was first shown in 2014. The game debuted at PlayStation Experience that year in the form of a teaser trailer. We played it the following year at a pre-E3 event.
According to Takahashi and producer Jeb Alvarado, Wattam has been rebuilt from the ground up over the past few years. The version of the game we played in 2015 was just a demo, Takahashi and Alvarado said in an interview with Polygon, and the new version is much more robust in terms of gameplay. Alvarado used the adjective "beefier."
Wattam is still about making friends through puzzle-solving. The mayor will befriend flowers, rocks, toilets, stalks of broccoli, multicolored swirls of poop and so on. You can control everyone and everything, Takahashi said. Friendships are formed and new friends are revealed in invigorating, colorful explosions. Only now, Alvarado said, the explosions are "juicier" than before.
Takahashi said that after the game lost support at Sony, he considered ending his work on Wattam. "But you know the game is not only mine," he said. "It's a teamwork, lots of people have made Wattam ... I couldn't just stop making it ..."
"I'm always trying to make a game [that] makes people notice how our ordinary life is great," Takahashi said. "Wattam also has that same feeling."
PAX West attendees will get to play a new version of the game at Annapurna Interactive's booth this weekend. Expect that build of the game to showcase refined game mechanics and a host of playable, collectible pals.


Consider the target market for the product - get feedback from the manufacturers.
How do the key features of the product benefit the customer?
What is the competition and how is the product different from the others?
Illustrate key features and benefits of the product in a simple and direct manner.
Incorporate ideas provided by the manuafacturer.
Keep it simple - make sure potential customers get the message.
Show potential customers everything they need to know to make a decision to buy the product.
Use good lighting and composition - the image must be aesthetically pleasing as well as informative.
Keep abreast of current advertising styles and techniques - and then get ahead!


Team Jo Epilepsy Foundation started to be built in 2013 and finally became a charity of its own in March 2014. Our aim here at Team Jo headquarters is to raise as much awareness and money for Epilepsy as we can. The money will be used to make a "real" difference to those with epilepsy by working closely with Drs and local hospitals, and also raising awareness for Epilepsy and the seriousness of the condition in the South East of England. This money will also go towards making a difference to those with or with family / friends with epilepsy who need the care to be more localised. All money that is raised will go towards helping patients with epilepsy, buying much needed equipment, researching drugs, and information about epilepsy.
Chestfield Rotary Club have kindly chosen Team Jo Epilepsy Foundation for the beneficiary of their Charity Golf Day in September 2015. We have been working in association with them since March 2015 look forward to the main event of the year.
Team Jo Epilepsy Foundation are happy to be supported by East Kent Hospitals Charity. East Kent hospitals charity is the hospitals main charity and raises funds for the wards and services provided by East Kent Hospitals University NHS Foundation Trust.
This association allows us to work together towards the same goals. East Kent Hospitals Charity will be assisting us with promoting our events and activities via their wide network of hospitals throughout Kent.
Pig and Porter will be donating 10p per pint sold of a special beer dedicated to Big K who died of epilepsy to the Team Jo Epilepsy Foundation and we are very pleased to report that this will be matched by Lorenzo at the Unicorn Inn, Canterbury.


Boys of Baseball National Travel Team (AL): James Harbuck; Hernando Sierra (2nd); Cameron High (3rd) ; Steven Garrett (5th); Jeremiah Summerford (6th).
Third Base - Albert Juarez (El Monte, California) and Left Field - Curt Bremer (McHenry, (IL).
Team Travel Ball Select (GA): James Harbuck started on the pitchers mound for the Boys of Baseball. Julio Gonzales grounded out second base to first on the first pitch of the game. Tyler Alamo struck out looking. Jacob Barfield singled to left center. David Boyle walked. Carrington Bell came in to courtesy run for the pitcher David Boyle. A passed ball sent Barfield to third and Bell to second. Jerrod Hathcock hammered a three-run home run to left field. Grayson Ivey lined out to left for the third out. Totals: three runs, two hits, no errors and no one left on base.
Boys of Baseball National Travel Team (AL): David Boyle started on the mound for the Team Travel Ball Select. Carter Toth led off with a single to center. Toth stole second base. A.J. Balta grounded out second to first with Toth taking third base. Hernando Sierra struck out swinging. J.B. Bellinger struck out swinging. Totals: no runs, one hit, no errors and one left on base.
Team Travel Ball Select (GA): Brett Henderson was hit by a pitch. Nathan Rodriguez was hit on the elbow by a pitch with Henderson going to second base. Hernando Sierra came in to pitch for the Boys of Baseball with Harbuck going to second. A passed ball sent Henderson and Rodrigues to third and second respectively. Matt Jervis homered to left driving in Henderson and Rodriguez. Julio Gonzalez struck out looking. Tyler Alamo singled to left field. A wild pitch sent Alamo to second. A balk by Sierra sent Alamo to third base. Jacob Barfield walked. David Boyle hit a sacrifice fly to the right fielder to score Alamo. Barfield stole second base. Jerrod Hathcock struck out looking. Totals: four runs, two hits, no errors and one left on base.
Boys of Baseball National Travel Team (AL): Kel Johnson grounded out pitcher to first. Jeremiah Summerford ground out pitcher to first. James Harbuck struck out swinging. Totals: no runs, no hits, no errors and none left on base.
Team Travel Ball Select (GA): Cameron High in to pitch for the Boys of Baseball replacing Steven Garrett who was at third base. Hernando Sierra moved to third base. Grayson Ivey was hit by a pitch. A balk sent Ivey to second. Carrington Bell in to run for Ivey. Brett Henderson got on on an error by the third baseman with Bell scoring from second but Henderson was thrown out while trying to advance to second. Nathabn Rodriguez lined out to second for the second out. Matt Jervis struck out swinging. Totals: one run, no hits, one error and none left on base.
Boys of Baseball National Travel Team (AL): Cameron High walked with Corey Howard courtesy running for him at first. Jason Martin walked sending Howard to second. Carter Toth struck out swinging. A.J. Balta grounded out unassisted to the first baseman with Howard taking third and Martin to second. Hernando Sierra grounded out unassisted to the first baseman for the third out. Totals: no runs, no hits, no errors and two left on base.
Team Travel Ball Select (GA): Julio Gonzalez struck out looking. Tyler Alamo struck out swinging. Jacob Barfield grounded out third to first. Totals: no runs, no hits, no errors and none left on base.
Boys of Baseball National Travel Team (AL): J. B. Bellinger ground out second to first. Kel Johnson singled to centerfield. Jeremiah Summerford struck out swinging. Johnson advanced to third on an error by the pitcher who was attempting to make a play on James Harbuck who got on first via an infield single. Johnson attempted to score on a passed ball but was tagged out for the third out of the inning. Totals: no runs, two hits, one error and one left on base.
Team Travel Ball Select (GA): David Boyle walked and stole second base when the shortstop dropped the ball for an error. Kevin Wade came in to courtesy run for David Boyle. Jerrod Hathcock was hit by a pitch which put runners at first and second. Steven Garrett in to pitch for the Boys of Baseball. Carrington Bell struck out swinging. Brett Henderson reach base on an error by the third baseman with both Wade and Hathcock scoring. The catcher attempted to throw out Henderson after a passed ball but the ball got away from the catcher and his throw got by the third baseman for an error and Henderson scored. Nathan Rodriguez grounded out short to first. Matt Jervis walked. Julio Gonzalez struck out swinging. Totals: three runs, no hits, three errors and one left on base.
Boys of Baseball National Travel Team (AL): Steven Garrett grounded out third to first. Jason Martin homered to center. Grayson Ivey came in to pitch for Team Travel Ball Select. Karl Blum hit for Carter Toth and struck out looking. Dalton Davis hit for A.J. Balta struck out swinging. Totals: one run , one hit, no errors and none left on base.
Team Travel Ball Select (GA): Tyler Alamo struck out looking. Jacob Barfield ground out unassisted to the first baseman. David Boyle walked. Jerrod Hathcock walked. Grayson Ivey grounded out second to first.Totals: no runs, no hits, no errors and two left on base.
Boys of Baseball National Travel Team (AL): Corey Howard hit for Hernando Sierra and struck out swinging. Justin Goree reached first on an error by the shortstop. A.J. Balta courtesy runner for Goree. Kel Johnson grounded into a 6-4-3 double play to end the game. Totals: no runs, no hits, one error and none left on base.
-The 2008 NATC represents the 49th week that BOB-NTT has participated at Cooperstown Dreams Park, since its first trip in late August 1997.
-Program highlights include: 2005 NATC Champions, 2006 NATC Semi-Finalist, 2007 NATC Semi-Finalis. Only program with a team in the NATC final four the past four years.
-1842-182 all time Win-Lost record.
-30 players selected to the USA Baseball national age group teams (14, 16, 18, college and World Cup).
-50+ instructional activities conducted each year in 10-12 states ... consistently recognized as a first-class act on and off the baseball field ... all BOB-NTT players and families come to the program via paraticipation in the BOB-NTT instructional activities.


Welcome to our newly designed Worcestershire Royal Arch web site. We hope that you find this site interesting, informative and useful.
Whether you are already a Royal Arch Mason or not, you will find useful information about who we are, what we do and contact information, should you wish to join this important and beautiful order or as a member, simply want to peruse to further your Masonic knowledge.
At the completion of the third degree ceremony in the Craft, you were informed that the genuine secrets of a Master Mason were lost and it is in the Holy Royal Arch where they can be discovered. The Holy Royal Arch really does complete your journey into Pure Ancient Freemasonry. Thus has been established the very close connection between Craft Masonry and the Holy Royal Arch and nowhere is that connection stronger than that which exists in the Province of Worcestershire.
This web site is intended to serve several purposes, all of which depend on efficient communication and a sensitivity to the needs of the members and the community.
• To assist Freemasons outside the province to be informed about our activities, encourage them to maintain contact.
• To act as a portal to official sources of information about a wide range of Masonic interests.
• To provide a window to Freemasonry for the local community.
• To help members who are unable to regularly attend meetings to keep in touch.
• To support the work of officers and event organisers in our Lodges.
• To encourage new members by offering an open and informed view of Freemasonry both globally and in the local community.
Based in Bromsgrove, 'the Province' is staffed largely by volunteers and carries out all the administrative work required to enable nearly 1,000 individuals across 40 Chapters in Worcestershire to enjoy their Royal Arch freemasonry!
Following the decision by E Comp Colin Brown to retire from office of Most Excellent Grand Superintendent (MEGS) at the end of 2018, his successor will be E Comp John M Phenix, currently Deputy Grand Superintendent. Until the next meeting of Provincial Grand Chapter on 13th April 2019 , John's official title will be 'Deputy Grand Superintendent In Charge'.
The Installation Meeting will be held at the Kings School Hall, Worcester following which, in the afternoon he will hold his 1st Annual General Meeting .of the Provincial Grand Chapter of Worcestershire.


Livin Sous Vide Loca; Is it Worth Your Investment?
Sous Vide (pronounced Sue Veed) is an absolute game changer. By adding this weapon to your culinary tool belt even the worst cook can produce perfectly cooked proteins and succulent sides with ease. Great for meal planning as well, this versatile device is a must-have in my kitchen.
Lamb rack is a meat lover's dream. This luscious lollipop is best enjoyed behind closed doors where your inner Flintstone can rise to the surface and you can go prehistoric on that thing.
As fancy as pannacotta sounds it is much easier than it's elegant name suggests. With an unctuous texture somewhere between pudding and custard this is an easy dessert that requires little more than stirring and refrigerating. Using cereal milk and chocolatey-sugary flavors this dessert transports you back to a simpler time when Saturday morning cartoons reigned supreme.
Even bad pizza is pretty dang fantastic. If you don't believe me call up any pizza joint and overpay for decent to so-so pizza to be delivered to your door. Do yourself a favor though and make this instead.
I've never shied away from my disdain of the humble cuke. Watery and fairly bland I had filed it away as a tea party fare. Suitable for water infusions or finger sandwiches at the kind of shindigs a fellow of my social standing only sees in films. Then culinary genius struck.
Bacon is like Liam Hemsworth. Anything it is involved in is pretty dang good and most people are thoroughly entranced by it. Enter Pork Belly. The Chris Hemsworth to Liam's bacon. Bigger, more robust in flavor and range, and so much more versatile. Pork belly takes the same principles that make bacon great and then turns the flavor dial up ftom ten to eleven.
These little babies are a fair bit of work but like most babies, it's totally worth it. Making your own buns from scratch produces a pillowy bun with a cloud-like interior that will have you eating them straight from the steamer.
Risotto is the undisputed king of rice. Long live the king! Treat your guests royally and make up a big batch for your next meal and watch everyone drool as you present this majestic meal.


Phytochromes regulate seed germination in response to light and temperature, and different phytochromes contribute to germination under different environmental conditions. Using Arabidopsis thaliana mutants with different combinations of non-functional phytochromes, we tested which phytochromes contribute to germination and other life-history traits under field conditions and whether that contribution changes with seed-maturation temperature. We also quantified natural selection on phytochrome variants through their influence on seed germination. We found that some phytochromes contributed to germination under field conditions and that the phytochrome that contributed most strongly depended on seed-maturation temperature. Specifically, when seeds were matured under warm temperature, phyA and phyE null plants had the most strongly reduced germination, with phyA not able to germinate late in the season. In contrast, when seeds were matured under cool temperature, phyB nulls had the most reduced germination, and effects of the phyA mutation were apparent only on a phyB background. These effects on germination translated to effects on total lifetime fitness, such that selection on phytochromes that contributed to germination sometimes depended on seed-maturation conditions. Synthesis. Natural selection on phytochromes occurs through their effects on seed germination, and maternal effects alter phytochrome contributions to germination. Therefore, maternal effects can alter natural selection on phytochromes. The results demonstrate a novel role of maternal effects in contributing to variable natural selection on specific genes associated with plant responses to climatic conditions. © 2012 The Authors. Journal of Ecology © 2012 British Ecological Society.


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Meet the cast of GLOW season 2 on Netflix
Mad Men's Alison Brie leads the line of female wrestlers in the series from the creators of Orange is the New Black
By Ben Allen
Netflix's dramedy GLOW – short for Gorgeous Ladies of Wrestling – is back for another round of bouts with season two. Expect more big hair, spandex, bright lights, the works.
Executive produced by Jenji Kohan (creator of Orange is the New Black), the show was inspired by a bizarre and hilarious women's wrestling programme (also named GLOW) that became a cult hit in the US in the late 1980s.
Everything you need to know about GLOW season 2
Watch the trailer for GLOW season 2
Everything coming to Netflix UK in June 2018
The season one finale saw Ruth (Alison Brie), Debbie (Betty Gilpin), Sam Sylvia (Marc Maron) and the gang succeed in securing a first season of their own version of GLOW. Now season 2 will see them actually make it, and face the trials and tribulations of the TV industry in the process.
Find out all you need to know about the show's stars below.
Ruth Wilder (played by Alison Brie)
Who is Ruth Wilder?
Despite her tenacity, Ruth's acting career is at almost a dead stop before she gets called to an ambiguous audition looking for unorthodox women, which turns out to be… well, GLOW. She was on the fringes of the show until she developed her character, Stoya the Russian, as a counterpart to Debbie's all-American hero, Liberty Bell.
Where have I seen Alison Brie before?
Brie is best known for her roles as Pete Campbell's long-suffering wife Trudy in Mad Men, and Annie in Community. She also plays the voice of Diane in Netflix's sardonic animation BoJack Horseman. Last year, she had a small role in James Franco's The Disaster Artist alongside real-life husband, Dave Franco.
Debbie (played by Betty Gilpin)
Who is Debbie?
Ruth's best friend turned wrestling nemesis. It was revealed early on in season one that Ruth had slept with Debbie's husband, which caused a major rift between the two, as well as bringing Debbie's marriage to an end. She quickly became the star of the show, as USA-loving single mom Liberty Bell.
Where have I seen Betty Gilpin before?
You may have caught her brief stint as Fiona in the Lucy Liu-fronted Sherlock adaptation Elementary, or perhaps her turn as Audrey in Amazon Prime Video's American Gods.
Sam Sylvia (played by Marc Maron)
Who does he play?
A washed-up B-movie director who takes on GLOW in order to secure finance for his latest film. At the end of season one, Sylvia discovered that his wrestler Justine, who had professed to being a devoted fan of his films, was actually his biological daughter.
Where have I seen him before?
Maron has his own sitcom, but he is best known for his excellent podcast (WTF with Marc Maron) in which he hosts deep-diving interviews with celebrities in his garage (check out an episode with co-star Alison Brie below).
Justine (played by Britt Baron)
Justine, the baby anarchist. Die yuppie scum. #GLOW @glownetflix
A post shared by Britt Baron (@brittbaron) on Jun 10, 2017 at 8:43am PDT
Who is Justine?
Not who she claimed to be in season one. It was revealed at the end of the season that she is actually Sam Sylvia's biological daughter. She's just 17, and her interest in wrestling was a lie, too. As season two begins it's revealed that she's now living with Sam.
Where have I seen Britt Baron before?
She has made appearances in sci-fi series Lucifer (soon to be revived by Netflix) and Grey's Anatomy.
Sheila the She Wolf (played by Gayle Rankin)
Who is Sheila?
Sheila is as method as they come: she more or less identifies as a wolf – and like a wolf, she doesn't socialise all that well to begin with. But, after rooming up with Ruth in season one, she became more immersed in the group.
Where have I seen her before?
Originally from Scotland, Rankin moved to New York when she was 17. She featured in Noah Baumbach's 2017 comedy drama The Meyerowitz Stories alongside Adam Sandler, Ben Stiller, Emma Thompson and Dustin Hoffman.
Tamee (played by Kia Stevens)
Who is Tamee?
An outrageous wrestling heel (bad guy), known as "Welfare Queen".
Where have I seen Kia Stevens before?
Stevens is a real-life lady of wrestling, who goes by the ring name of Awesome Kong or Kharma.
Melrose (played by Jackie Tohn)
Who does she play?
An entitled, sarcastic party girl who looks to get under her co-stars' skin, but quickly turns into one of the most active members of the weird wrestling clique.
In 2017, she starred in Dax Shepard's film remake of CHIPS, and also appeared in America's Got Talent in the US. Since appearing in GLOW she's even made the jump over to 'real' wrestling, making a cameo in WWE.
TOMORROW NIGHT: it's a real @WWE night of beef. Shouts to @JackieTohn for pitching me GLOW vs Pro and it becoming one of my favorite battles of the season. pic.twitter.com/vy8tO14wdH
— Jensen Karp (@JensenClan88) June 9, 2018
Rhonda (played by Kate Nash)
A kind, ditzy British actress who gets involved with the world of GLOW, and takes a shine to Maron's Sam Sylvia. Her wrestling alter ego is Britannica, the smartest woman in the world, who dons a lab-coat and spectacles.
The pop star turned actress has been a little quiet since dropping her massive single "Foundations" in 2007 at the age of 19. Though she has popped up in a couple of indie flicks, this is her first major acting role.
Cherry Bang (played by Sydelle Noel)
A former colleague of Sam Sylvia's and a B-movie actress aspiring for more. At the end of season one, she was offered a lead role on a cop drama, which means she will be unable to partake in GLOW – but she's definitely still in the mix.
She made an appearance in Marvel's smash hit Black Panther, and has starred alongside Kate Mara and David Oyelowo in the 2015 thriller Captive, and as Samanda Watson in Arrow.
Sebastian "Bash" Howard (played by Chris Lowell)
Who is Bash Howard play?
A boyish wrestling fanatic who uses his family's wealth to fund the show.
Where have I seen Chris Lowell before?
Lowell played Piz on Veronica Mars, and Kevin in George Clooney's Up In The Air.
Carmen Wade (played by Britney Young)
The daughter of a professional wrestler called Goliath Jackson, and the most seasoned wrestler of the bunch.
This is her first major role.
GLOW season 2 is released on Netflix on Friday 29th June
The real story of GLOW | When comedy, Cold War politics and wrestling collide
Alison Brie and Betty Gilpin learned to wrestle for real for new Netflix series GLOW
All about GLOW
When is new comedy series GLOW released on Netflix? What is it about? Who will be in the cast?
GLOW creator Liz Flahive on the women's wrestling show that inspired the Netflix comedy


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Clay Fuller
Briles confirms return of Baylor receivers for Iowa State
By Kevin McGuireSep 24, 2014, 6:13 PM EDT
Baylor's offense is about to get a bit more depth thanks to the return of some injured players. Wide receivers Corey Coleman and Antwan Goodley and running back Devin Chafrin are all expected to return to the team this weekend when the Bears take on Iowa State in Big 12 play.
It was reported last week Baylor was expecting to have Coleman and Goodley as well as Clay Fuller return for this weekend's game, and head coach Art Briles confirmed that is the plan for this week.
Goodley was lost to a quad injury and Coleman suffered a hamstring. Both injuries took place in offseason camps. Goodley attempted to come back in the season opener against SMU but aggravated his injury. Chafrin suffered a high ankle sprain in the SMU game as well.
Baylor is off to an explosive start on offense, as you might have expected, regardless of the injury concerns. With Big 12 conference play opening up, it will be important for the defending Big 12 champions to be as healthy as possible. Oklahoma has emerged as the team to beat in the conference, but a healthy Baylor figures to be just as capable of getting through the season and winning the conference once again.
Tags: Antwan Goodley, Art Briles, Baylor, Clay Fuller, Corey Coleman, Devin Chafrin
Baylor expected to get three WRs back for ISU game
By John TaylorSep 15, 2014, 3:03 PM EDT
Not that it actually needed them, but Baylor was down all four of its leading returning wide receivers from 2013 for last Friday's 63-12 romp over Buffalo.
For the Bears' next game, they should be back to near full-strength in the receiving corps.
On his teleconference Monday, head coach Art Briles confirmed that Antwan Goodley (pictured), Corey Coleman and Clay Fuller will play in BU's next game against Iowa State. Neither Goodly (quad) nor Coleman (hamstring) nor Fuller (clavicle) have caught a pass this season after combining for 138 receptions last season. Goodly alone accounted for 71 of those.
There's no word on when Levi Norwood (47-733-4 in 2013) will return. Two weeks ago, Norwood underwent wrist surgery and was originally scheduled to be out for at least three weeks. That would've put him back on the field for the Cyclones game, although that doesn't appear to be the case as of today.
With all of that experience sidelined, youth has been served for Baylor in the passing game. True freshman KD Cannon has proven to be one of the early breakout stars in the whole of college football, catching 14 passes for 471 yards and five touchdowns. The receiving yards lead the FBS, while his receiving scores are tied for second nationally.
Tags: Antwan Goodley, Baylor, Clay Fuller, Corey Coleman, KD Cannon, Levi Norwood
Baylor loses another WR: Levi Norwood to undergo wrist surgery
By JJ StankevitzSep 4, 2014, 12:24 PM EDT
Baylor will now be without its four top returning receivers from 2013 on Saturday, as senior Levi Norwood will undergo wrist surgery and miss at least three weeks, per the Waco Tribune-Herald's John Werner.
Art Briles' Bears were already missing Antwan Goodley, Corey Coleman and Clay Fuller for Saturday's date with FCS side Northwestern State. Goodley, Baylor's leading returning receiver with 71 catches for 1,339 yards and 13 touchdowns a year ago, is out with a quad injury.
Norwood caught 47 passes for 733 yards and eight touchdowns a year ago, while Coleman had 35 catches for 527 yards and two touchdowns while Fuller had 32 catches for 512 yards and three touchdowns.
But there's no need to panic in Waco. Baylor's next four games are against Northwestern State, at Buffalo and at Iowa State, with a bye week sandwiched between Buffalo and Iowa State. That's plenty of time for these four receivers (and quarterback Bryce Petty) to get healthy before Oct. 4's date with Texas in Austin.
Tags: Antwan Goodley, Baylor, Bryce Petty, Clay Fuller, Corey Coleman, Levi Norwood
Bear Trap: Oklahoma State dismantles Baylor, controls Big 12
By Kevin McGuireNov 23, 2013, 11:50 PM EST
For the first time this season, the No. 4 Baylor (9-1, 6-1 Big 12) offense was not invincible. In fact, at teams it was impotent. Perhaps it was the venue, where the Bears have not won in 74 years, or perhaps it was the first time the Bears were met by an opponent capable of scoring and defending everything Baylor does well. No. 10 Oklahoma State (10-1, 7-1 Big 12) played about as perfect a game you could play in a 49-17 taming of Baylor.
Oklahoma State quarterback Clint Chelf had his second career 300-yard passing performance with 337 yards, a new career high. both of Chelf's 300-yard games have come against Baylor. The Cowboys quarterback threw two touchdown passes before getting a rest in the fourth quarter. He completed 18 of 24 attempts. his top target was Tracy Moore, who had five catches for 126 yards and a touchdown. The Cowboys outscored Baylor 35-14 in the second half after taking a statement 14-3 lead to the half.
If you watched Baylor for the first time this season on Saturday night you may have never guessed it, but Baylor's offense came in to the game averaging 61.2 points and amassing 683 yards of total offense per game. That is how good Oklahoma State played on the defensive side of the football. Baylor's pass offense was shut down by an aggressive defensive secondary, with Oklahoma State not allowing Antwan Goodley, Levi Norwood or Clay Fuller much room to work with as they have most of the season. Baylor's recently emerging Heisman candidate at quarterback, Bryce Petty, had a rough night from start to finish, ending his night completing 28 of 48 attempts for 359 yards and two touchdowns. Most of those yards came in the second half when the game was well out of hand. Petty also missed a snap and failed to cover it up before Oklahoma State's Tyler Patmon and returned 78 yards for a touchdown. That particular sequence ay have altered the entire momentum of the game, as Baylor was on the door step and about to cut a 25-point deficit down to 17 points with time to play. It still would have been an uphill climb for Baylor, but it would have been less steep.
Oklahoma State now has complete control of the Big 12 race with one game to play. The Cowboys get a week off before hosting rival Oklahoma (9-2, 6-2 Big 12) in the annual Bedlam game. A win by Oklahoma State will clinch the Big 12 championship and the automatic BCS berth that goes with it.That would send Oklahoma State to their second BCS bowl game in three seasons if they can finish the job against the Sooners.
Baylor's BCS chances took a bit of a hit, but it is still too early to completely rule out the Bears from the BCS bowl picture. While the BCS championship game is now out of the mix, Baylor can still be in play for an at-large bid. It may be a reach, but if Baylor ends the season with regular season wins against TCU and Texas, Baylor could remain an option for a BCS bowl game. The problem is Fresno State or Northern Illinois are currently in line to grab one of the BCS spots, leaving one less spot available to an at-large option from a power conference.
Of course, Baylor can still win the Big 12 if they win their next two games and Oklahoma tops Oklahoma State.
Tags: Antwan Goodley, Baylor, Bryce Petty, Clay Fuller, Clint Chelf, Levi Norwood, Oklahoma State, Tracy Moore, Tyler Patmon
Briles confirms return of Baylor receivers for Iowa State September 24, 2014 6:13 pm Baylor expected to get three WRs back for ISU game September 15, 2014 3:03 pm Baylor loses another WR: Levi Norwood to undergo wrist surgery September 4, 2014 12:24 pm Bear Trap: Oklahoma State dismantles Baylor, controls Big 12 November 23, 2013 11:50 pm


Natta was delighted to visit the Brightwells Gostrey Centre in Farnham today and present them with a cheque for £6,440 from the proceeds of our Natta Group Annual 2014 Charity Golf Day.
We have been supporting Brightwells as our local charity for several years. We help this worthwhile charity in a bid to ensure that our local elderly community has somewhere to go for a hot meal, company and support if they need it.
The cheque was presented by Jo Whelan from Natta to the newly appointed Chair of Brightwells Lucinda Fleming, Brightwells Treasurer John McIntyre and the Centre Manager Maggie Groves.
Brightwells is a day centre located in Farnham town centre, which provides a community for the local elderly residents. It has many years experience of helping a wide range of senior citizens, from the most youthful to those with higher needs.
Earlier this month we also visited our national charity The Royal Marsden Cancer Charity and John Murphy from Natta Homes presented a cheque for £4,290 for their Oak Centre for Young People and Children.
Both donations were raised during our 2014 Charity Golf Day that took place earlier this year. Our thanks go out to all those who attended and a special thank you to all our clients and suppliers for their donations and everyone who was involved in making the event so successful.


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Australia's leading source of security and threat news
Gartner names Sophos leader in Unified Threat Management
Network and endpoint security company Sophos has announced that it has been positioned as a leader in Gartner's September Magic Quadrant for Unified Threat Management (SMB Multifunction Firewalls).
Sophos is positioned furthest for completeness of vision and ability to execute.
Sophos has been consistently positioned as a leader by Gartner in both the January 2018 Magic Quadrant for Endpoint Protection Platforms and the Magic Quadrant for Unified Threat Management.
Sophos Products senior vice president and general manager Dan Schiappa says, "Introducing technologies such as deep learning in the sandbox and synchronised security helps us stay ahead of the evolving threat landscape."
"We develop our cybersecurity products to work together as a system, enabling our defenses to be as coordinated as attacks such as the persistent SamSam ransomware."
One of the advantages of network and endpoint synchronised security is the visibility it can provide for network traffic.
In a recent survey from Sophos, The Dirty Secrets of Network Firewalls, IT managers admitted that they could not identify about 45% of the traffic on their network, which presents a serious security concern.
By enabling the firewall to receive information directly from the endpoint, XG Firewall can positively identify all applications – even obscure or custom applications.
In an average network, XG Firewall Sync App Control is able to identify 100-200 previously unknown apps, revealing hidden risks to network security.
Sophos' portfolio of network protection includes the next-generation XG Firewall series and the SG UTM series.
Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation.
Gartner research publications consist of the opinions of Gartner's research organisation and should not be construed as statements of fact.
Sophos unveils new open AI developments to sharpen defenses against cyberattacks
Gartner Magic Quadrant Gartner Sophos Unified threat management Firewall Network Security
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