text:
Engineered Elevation; info@nexogen.hu / +36-20-373-7674
Waberer's Case Study
An autonomous decision support system for the optimization of planning processes arising in the context of Waberer's International.
Problem definition, brief motivation
In the long-haul full-truckload freighting and transportation industry, satisfying 5000 orders per week and planning the route of more than 3000 trucks, trailers and drivers all across Europe can prove to be a substantial business challenge.
From time to time, one has to assign available resources to freight tasks derived from orders. While doing this particular job, route planners and unit fleet operators have to consider many constraints, such as the driving time limits according to regulation of the 561/2006/EC directives, the opening hours of depots, drivers' work schedule and other business factors. The cost-efficient operation of the fleet depends on the efficiency of the work these people perform on a thrice a day basis.
The resulting assignment plans should adhere to the strategic goals of the company. There is a signification risk involved if these plans, despite satisfying demand, fail to meet the company's cost efficiency requirements. The result can be undesirably high operating cost, due to the suboptimal utilization of the fleet.
NEXOGEN approached Waberer's International with a custom-tailored decision support solution based on the company's own proprietary mathematical optimization framework. This framework was capable of effectively addressing the issues of delivering high-quality assignment and routing plans on the total fleet. The goal was to introduce a solution into the existing IT infrastructure, which could allow optimal planning to satisfy business constraints under very short time constraints. A secondary objective was to save error-prone and time consuming activities for the planners which could otherwise hinder the efficacy of the underlying decision making pipeline.
Analyzing the domain problem was our first step. Our proficient business analysts identified and assessed the business processes arising in the context of FTL (full truck load) long-haul freight transportation planning at Waberer's International. In the very early stage of the project,critical factors and key performance indicators of the underlying problem were determined. The simultaneous, multi-objective minimization of the average waiting time and the empty kilometers performed by the trucks assigned to the respective tasks were necessary to make the overall project successful and efficient from both technical and business standpoints.
After this stage complex mathematical models and accompanying algorithms have been developed to efficiently describe and solve the NP-hard combinatorial optimization problem of a generalized resource and task assignment. This modeling process also involved the incorporation of various side-constraints and objectives imposed by the domain problem. These models and methods have been embedded into the optimization framework of NEXOGEN. The solution was integrated into the corporate IT infrastructure of Waberer's, which is based on a custom made ERP solution, via SOAP/WCF interfaces. Additionally, our system has clear and well-documented interfaces, which can be connected to any major ERP platform/vendor, such as SAP, Microsoft Dynamics AX and Oracle.
The integrated solution has evolved into a high performance, custom-tailored decision support system and optimization tool, which suits the needs of Waberer's International the best and is able to obtain high quality, executable, valid and optimal plans. The integration of this solution into the customer's planning pipeline resulted in substantial improvement of the key performance indicators, which could be measured in explicit cost savings. The solution has paid off within the month after introduction, while the whole project, from the signing of the contract to the installation, took only 6 months in total.
info@nexogen.hu
6th floor, 53. Alkotás str. 1123 Budapest, Hungary
© 2019 / created by: pixelephant


text:
Q: Order of evaluation with assignment operator Can anyone explain to me why the following code:
#include <iostream>
using namespace std;
int main() {
    int y=3;
    int z = (--y) + (y=10);
    printf("%d \n", z);
}

prints out z=20? I thought it should be z=19 instead?

redpajama_set_name: RedPajamaStackExchange


text:
\section{Introduction}

Text classification is a fundamental task in Natural Language Processing (NLP) and has received constant attention due to its wide applications, ranging from spam detection to social media analytics \cite{pang2002thumbs,hu2004mining,choi2008learning,socher2012semantic,vo2015target}. Over the past couple of decades, supervised machine learning methods have shown dominant performance for text classification, such as naive bayes classifiers \cite{troussas2013sentiment}, support vector machines \cite{li2018new} and neural networks \cite{wu2020dualb}. In particular, with the advent of deep learning, neural network-based text classification models have gained impressive achievements. However, text classification is known to be highly domain-dependent, the same word could convey different sentiment polarities in different domains \cite{glorot2011domain}. For example, the word \textit{infantile} expresses neutral sentiment in baby product review (e.g., \textit{The infantile cart is easy to use}), while in book review, it indicates a negative polarity (e.g.,  \textit{This book is infantile and boring}). Thus a text classifier trained on one domain is likely to make spurious predictions on another domain whose distribution is different from the training data distribution. In addition, it is always difficult to collect sufficient labeled data for all interested domains. Therefore, it is of great significance to explore how to leverage available resources from related domains to improve the classification accuracy on the target domain.

The major line of approaches to tackle the above problem is multi-domain text classification (MDTC) \cite{li2008multi}, which can handle the scenario where labeled data exist for multiple domains, but in insufficient amounts to training an effective classifier for each domain. Deep learning models have yielded impressive performance in MDTC \cite{wu2020dual,wu2021mixup}. Most recent MDTC methods adopt the shared-private paradigm, which divides the latent space into two types: one is the shared feature space among domains with the aim of capturing domain-invariant knowledge, the other one is the private feature space for each domain which extracts domain-specific knowledge. To explicitly ensure the optimum separations among the shared latent space and multiple domain-specific feature spaces, the adversarial training \cite{goodfellow2014generative} is introduced in MDTC. By employing the adversarial training, the domain-specific features can be prevented from creeping into the shared latent space, which will lead to feature redundancy \cite{liu2017adversarial}. In adversarial training, a multinomial domain discriminator is trained against a shared feature extractor to minimize the divergences across different domains. When the domain discriminator and the shared feature extractor reach equilibrium, the learned shared features can be regarded as domain-invariant and used for the subsequent classification. The adversarial training-based MDTC approaches yield the state-of-the-art results \cite{liu2017adversarial,chen2018multinomial}. However, these methods still have a significant limitation: when the data distributions present complex structures, adversarial training may fail to perform global alignment among domains. Such a risk comes from the challenge that in adversarial training, only aligning the marginal distributions can not sufficiently guarantee the discriminability of the learned features. The features with different labels may be aligned, as shown in Figure \ref{Fig8}. The fatal mismatch can lead to weak discriminability of the learned features.

\begin{figure}
    \centering
    \includegraphics[width=0.9\columnwidth]{EACL_2.pdf}
    \caption{The mismatch risk when aligning the marginal distributions in MDTC, we present the case containing two domains $D_1$ and $D_2$. The blue regions denote distributions of $D_1$, and the yellow regions denote distributions of $D_2$. (a) The scenario before performing domain alignment. (2) When aligning the marginal distributions, a mismatch may occur with regard to the label.
	}
    \label{Fig8}
\end{figure}

In this paper, motivated by the conditional generative adversarial networks (CGANs), which aligns distributions of real and generated images via conditioning the generator and discriminator on extra information \cite{mirza2014conditional}, we propose conditional adversarial networks (CANs) to address the aforementioned challenge. The CAN method introduces a conditional domain discriminator that models domain variance in both shared features and label predictions, exploring the relationship between shared feature representations and class-aware information conveyed by label predictions to encourage the shared feature extractor to capture more discriminative information. Moreover, we use entropy conditioning to avoid the risk of conditioning on the class-aware information with low certainty. The entropy conditioning strategy can give higher priority to easy-to-transfer instances. We also provide a theoretical analysis demonstrating the validity of CANs. Our approach adopts the shared-private paradigm. We validate the effectiveness of CAN on two MDTC benchmarks. It can be noted that CAN outperforms the state-of-the-art methods for both datasets. Finally, we empirically illustrate that CAN has the ability to generalize in cases where no labeled data exist for a subset of domains. The contributions of our work are listed as follows:

\begin{itemize}
    \item We propose conditional adversarial networks (CANs) for multi-domain text classification which incorporate conditional domain discriminator and entropy conditioning to perform alignment on the joint distributions of shared features and label predictions to improve the system performance.
    \item We present the theoretical analysis of the CAN framework, demonstrating that CANs are minimizers of divergences among multiple joint distributions of shared features and label predictions, and providing the condition where the conditional domain discriminator reaches its optimum.
    \item We evaluate the effectiveness of CAN on two MDTC benchmarks. The experimental results show that CAN yields state-of-the-art results. Moreover, further experiments on unsupervised multi-source domain adaptation demonstrate that CAN has a good capacity to generalize to unseen domains.
\end{itemize}

\section{Related Work}

Multi-domain text classification (MDTC) was first proposed by \cite{li2008multi}, aiming to simultaneously leverage all existing resources across different domains to improve the system performance. Currently, there are two main streams for MDTC: one strand exploits covariance matrix to model the relationship across domains \cite{dredze2008online,saha2011online,zhang2012convex}; the other strand is based on neural networks, sharing the first several layers for each domain to extract low-level features and generating outputs with domain-specific parameters. The multi-task convolutional neural network (MT-CNN) utilizes a convolutional layer in which only the lookup table is shared for better word embeddings \cite{collobert2008unified}. The collaborative multi-domain sentiment classification (CMSC) combines a classifier that learns common knowledge among domains with a set of classifiers, one per domain, each of which captures domain-dependent features to make the final predictions \cite{wu2015collaborative}. The multi-task deep neural network (MT-DNN) maps arbitrary text queries and documents into semantic vector representations in a low dimensional latent space and combines tasks as disparate as operations necessary for classification \cite{liu2015representation}.

Pioneered by the generative adversarial network (GAN) \cite{goodfellow2014generative}, adversarial learning has been firstly proposed for image generation. \cite{ganin2016domain} applies adversarial learning in domain adaptation to extract domain-invariant features across two different distributions (binary adversarial learning). \cite{zhao2017multiple} extends it to multiple adversarial learning, enabling the model to learn domain-invariant representations across multiple domains. However, only considering domain-invariant features can not provide optimal solutions for MDTC, because domain-specific information also plays an important role in training an effective classifier. \cite{bousmalis2016domain} proposes the shared-private paradigm to combine domain-invariant features with domain-specific ones to perform classification, illustrating that this scheme can improve system performance. To date, many state-of-the-art MDTC models adopt adversarial learning and shared-private paradigm. The adversarial multi-task learning for text classification (ASP-MTL) utilizes long short-term memory (LSTM) without attention as feature extractors and introduces orthogonality constraints to encourage the shared and private feature extractors to encode different aspects of the inputs \cite{liu2017adversarial}. The multinomial adversarial network (MAN) exploits two forms of loss functions to train the domain discriminator: the least square loss (MAN-L2) and negative log-likelihood loss (MAN-NLL) \cite{chen2018multinomial}. The multi-task learning with bidirectional language models for text classification (MT-BL) introduces language modeling as an auxiliary task to encourage the domain-specific feature extractors to capture more syntactic and semantic information, and a uniform label distribution-based loss constraint to the shared feature extractor to enhance the ability to learn domain-invariant features \cite{yang2019multi}.

Adversarial learning has several advantages, such as Markov chains are not needed and no inference is required during learning \cite{mirza2014conditional}. However, there still exists an issue in adversarial learning. When data distributions embody complex structures, adversarial learning can fail in performing the global alignment. The conditional generative adversarial network (CGAN) is proposed to address this problem \cite{mirza2014conditional}. In CGAN, both the generator and discriminator are conditioned on some extra information, such as labels or data from other modalities, to yield better results. Sharing some spirit of CGAN, this paper extends conditional adversarial learning in MDTC, enabling a domain discriminator on the shared features while conditioning it on the class-aware information conveyed by the label predictions. Moreover, in order to guarantee the generalizability of the learned features, we also utilize the entropy conditioning strategy.

\section{Approach}

\begin{figure*}
    \centering
    \includegraphics[width=1.3\columnwidth]{Model.pdf}
    \caption{The architecture of the CAN model. A shared feature extractor $\mathcal{F}_s$ learns to capture domain-invariant features;
	each domain-specific feature extractor $\mathcal{F}^i_{d}$
	learns to capture domain-dependent features;
	a conditional domain discriminator $\mathcal{D}$ models shared feature distributions by conditioning on discriminative information provided by label predictions; a classifier $\mathcal{C}$ is used to conduct text classification; $\mathcal{J}_\mathcal{C}$ is the classification loss function; and $\mathcal{J}_{\mathcal{D}}^E$ is the entropy conditioning adversarial loss function which guides the domain-invariant feature extraction.
	}
    \label{Fig2}
\end{figure*}

In this paper, we consider MDTC tasks in the following setting. Assume there exist $M$ domains $\{D_i\}_{i=1}^{M}$. For each domain, both labeled and unlabeled samples are taken into consideration. Specifically, $D_i$ contains two parts: a limited amount of labeled samples $\mathbb{L}_i=\{({\bf x}_j,y_j)\}_{j=1}^{l_i}$; and a large amount of unlabeled samples $\mathbb{U}_i=\{{\bf x}_j\}_{j=1}^{u_i}$. The challenge of MDTC lies in how to improve the system performance of mapping the input ${\bf x}$ to its corresponding label $y$ by leveraging all available resources across different domains. The performance is measured as the average classification accuracy across the $M$ domains.

\subsection{Model Architecture}

We propose conditional adversarial networks (CANs), as shown in Figure \ref{Fig2}, which adopt the shared-private scheme and consist of four components: a shared feature extractor $\mathcal{F}_s$, a set of domain-specific feature extractors $\{\mathcal{F}_d^i\}_{i=1}^M$, a conditional domain discriminator $\mathcal{D}$, and a text classifier $\mathcal{C}$. The shared feature extractor $\mathcal{F}_s$ learns to capture domain-invariant features that are beneficial to classification across all domains, while each domain-specific feature extractor $\mathcal{F}_d^i$ aims to learn knowledge that is unique to its own domain. The architecture of these feature extractors are flexible and can be decided based on the practical task. For instance, it can adopt the form of a convolutional neural network (CNN), a recurrent neural network (RNN), or a multiple layer perceptron (MLP). Here, a feature extractor generates vectors with a fixed length, which is considered as the hidden representation of certain input. The classifier $\mathcal{C}$ takes the concatenation of a shared feature and a domain-specific feature as its input and outputs label probabilities. The conditional domain discriminator $\mathcal{D}$ takes the concatenation of a shared feature and the prediction of the given instance provided by $\mathcal{C}$ as its input and predicts the likelihood of that instance coming from each domain.

\subsection{Conditional Adversarial Training}

Adversarial learning has been successfully investigated in minimizing divergences among domains \cite{chen2018multinomial,zhao2017multiple}. In standard adversarial learning for MDTC, a two-player mini-max game is conducted between a domain discriminator and a shared feature extractor: the domain discriminator is trained to distinguish features across different domains, and the shared feature extractor aims to deceive the discriminator. By performing this mini-max optimization, the domain-invariant features can be learned. The error function of the domain discriminator corresponds well to the divergences among domains. Most MDTC methods align the marginal distributions. However, the transferability with representations transition from general to specific along deep networks is decreasing significantly \cite{yosinski2014transferable}, only adapting the marginal distributions is not sufficient to guarantee the global alignment. In addition, when the data distributions embody complex structures, which is a real scenario for NLP applications, there is a high risk of failure by matching features with different labels.

Recent advances in the conditional generative adversarial network (CGAN) disclose that better alignment on two different distributions can be obtained by conditioning the generator and discriminator on class-aware information \cite{mirza2014conditional}. The discriminative information provided by the label prediction potentially reveals the structure information underlying the data distribution. Thus, conditional adversarial learning can better model the divergences among domains on shared feature representations and label predictions. Unlike the prior works that adapting the marginal distributions \cite{liu2017adversarial,chen2018multinomial}, our proposed CAN framework is formalized on aligning joint distributions of shared features and label predictions. There exist two training flows in our model. Due to the nature of adversarial learning, the conditional domain discriminator is updated with a separate optimizer, while the other components of CAN are trained with the main optimizer. These two flows are supposed to complement each other. Denote $\mathcal{L}_{\mathcal{C}}$ and $\mathcal{L}_{\mathcal{D}}$ as the loss functions of the classifier $\mathcal{C}$ and the conditional domain discriminator $\mathcal{D}$, respectively. We utilize the negative log-likelihood (NLL) loss to encode these two loss functions:

\begin{align}
    \mathcal{L}_{\mathcal{C}}(\widetilde{y}, y) = -\log P(\widetilde{y} = y)
\end{align}

\begin{align}
    \mathcal{L}_{\mathcal{D}}(\widetilde{d}, d) = -\log P(\widetilde{d} = d)
\end{align}

\noindent where $y$ is the true label, $\widetilde{y}$ is the label prediction, $d$ is the domain index and $\widetilde{d}$ is the domain prediction. Therefore, we formulate CAN as a mini-max optimization problem with two competitive terms defined as follows:

\begin{align}
    \mathcal{J}_{\mathcal{C}} = \sum_{i=1}^M \mathbb{E}_{({\bf x},y)\sim\mathbb{L}_i}[\mathcal{L}_{\mathcal{C}}(\mathcal{C}_i,y)]
\end{align}

\begin{equation}
    \begin{aligned}
    \mathcal{J}_{\mathcal{D}} = \sum_{i=1}^M \mathbb{E}_{{\bf x} \sim \mathbb{L}_i \cup \mathbb{U}_i}[\mathcal{L}_{\mathcal{D}}(\mathcal{D}([\mathcal{F}_s({\bf x}), \mathcal{C}_i]),d)]
    \end{aligned}
\end{equation}

\noindent where $[\cdot,\cdot]$ is the concatenation of two vectors, $\mathcal{C}_i=\mathcal{C}([\mathcal{F}_s({\bf x}),\mathcal{F}_d^i({\bf x})])$ is the prediction probability of the given instance ${\bf x}$. $\mathcal{C}$ and $\mathcal{D}$ adopt MLPs with a softmax layer on top. For the domain-specific feature extractors $\{\mathcal{F}_d^i\}_{i=1}^M$, the training is straightforward, as their objective is simple: help $\mathcal{C}$ perform better classification. While the shared feature extractor $\mathcal{F}_s$ has two goals: (1) help $\mathcal{C}$ reduce prediction errors, and (2) confuse $\mathcal{D}$ to reach equilibrium.

\subsection{Entropy Conditioning}

We condition the domain discriminator $\mathcal{D}$ on the joint variable $(f,c) = (\mathcal{F}_s({\bf x}), \mathcal{C}_i)$. For brevity, here we use $f$ and $c$ to denote $\mathcal{F}_s({\bf x})$ and $\mathcal{C}_i$, respectively. If we enforce different instances to have equal importance, the hard-to-transfer instances with uncertain predictions may deteriorate the system performance \cite{saito2019semi}. In order to alleviate the harmful effects introduced by the hard-to-transfer instances, we introduce the entropy criterion $E(c)=-\sum_{k=1}^2[c_k log c_k]$ to quantify the uncertainty of label predictions, where $c_k$ is the probability of predicting an instance to category $k$ (negative: k = 1, positive: k = 2). By using the entropy conditioning, the easy-to-transfer instances with certain predictions are given higher priority. We reweigh these instances by an entropy-aware term: $w(c) = 1 + e^{-E(c)}$. Therefore, the improved $\mathcal{J}_{\mathcal{D}}$ is defined as:

\begin{equation}
    \begin{aligned}
    \mathcal{J}_{\mathcal{D}}^E &= \sum_{i=1}^M \mathbb{E}_{{\bf x} \sim \mathbb{L}_i \cup \mathbb{U}_i}[w(c) \mathcal{L}_{\mathcal{D}}(\mathcal{D}([\mathcal{F}_s({\bf x}), \mathcal{C}_i]),d)]
    \end{aligned}
\end{equation}

\noindent Therefore, the mini-max game of CAN is formulated as:

\begin{align}
    \min_{\mathcal{F}_s,\{\mathcal{F}_d^i\}_{i=1}^M, C}\max_{\mathcal{D}} \quad \mathcal{J}_{\mathcal{C}} + \lambda \mathcal{J}_{\mathcal{D}}^E
\end{align}

\noindent where $\lambda$ is a hyperparameter balancing the two objectives. The entropy conditioning empowers the entropy minimization principle \cite{grandvalet2005semi} and controls the certainty of the predictions, enabling CAN have the ability to generalize on unseen domains with no labeled data. The CAN training is illustrated in Algorithm  \ref{trainingalg}.

\begin{algorithm}
\caption{Stochastic gradient descent training algorithm}\label{trainingalg}
\begin{algorithmic}[1]
\STATE{\bf Input:} labeled data $\mathbb{L}_i$ and unlabeled data $\mathbb{U}_i$ in $M$ domains;
	a hyperparameter $\lambda$.
\FOR{number of training iterations}
	\STATE Sample labeled mini-batches from the multiple domains $B^\ell=\{B^\ell_1,\cdots, B^\ell_M\}$.
	\STATE Sample unlabeled mini-batches from the multiple domains $B^u=\{B^u_1,\cdots, B^u_M\}$.

	\STATE Calculate $loss = \mathcal{J}_{\mathcal{C}}+\lambda \mathcal{J}_{\mathcal{D}}^E$ on $B^\ell$ and $B^u$;\\
	Update $\mathcal{F}_s$, $\{\mathcal{F}_d^i\}_{i=1}^M$, $\mathcal{C}$ by descending along the gradients $\Delta loss$.\\[.2ex]

	\STATE Calculate $l_D=\mathcal{J}_{\mathcal{D}}^E$ on $B^\ell$ and $B^u$;\\
	Update $\mathcal{D}$ by ascending along the gradients $\Delta l_D$.\\[.2ex]

\ENDFOR
\end{algorithmic}
\end{algorithm}

\subsection{Theoretical Analysis}

In this section, we present an analysis showing the validity of the CAN approach for MDTC. All proofs are given in the Appendix. The objective of CAN is equivalent to minimizing the total divergence among the $M$ joint distributions. First, we define different joint distributions as $P_i(f,c) \triangleq P(f = \mathcal{F}_s({\bf x}), c = \mathcal{C}_i | {\bf x} \in D_i)$. Combining $\mathcal{L}_\mathcal{D}$ with $\mathcal{J}_\mathcal{D}$, the objective of $\mathcal{D}$ can be written as:

\begin{align}
    \mathcal{J}_\mathcal{D} = -\sum_{i=1}^M \mathbb{E}_{(f,c)\sim P_i}[\log\mathcal{D}_i([f,c])]
\end{align}

\noindent where $\mathcal{D}_i([f,c])$ yields the probability of the vector $([f,c])$ coming from the $i$-th domain. We first derive that CAN could achieve its optimum if and only if all $M$ joint distributions are identical.

\begin{lemma}
\label{lem1}
 For any given $\mathcal{F}_s$, $\{\mathcal{F}_d^i\}_{i=1}^M$ and $\mathcal{C}$, the optimum conditional domain discriminator $\mathcal{D}^*$ is:
\begin{align}
    \mathcal{D}_i^*([f,c]) = \frac{P_i(f,c)}{\sum_{j=1}^M P_j(f,c)}
\end{align}
\end{lemma}

\noindent Then we provide the main theorem for the CAN framework:

\begin{theorem}
\label{theo1}
Let $\widetilde{P}(f,c) = \frac{\sum_{i=1}^M P_i(f,c)}{M}$, when $\mathcal{D}$ is trained to its optimum $\mathcal{D}^*$, we have:
\begin{align}
    \mathcal{J}_{\mathcal{D}^*}=M \log M-\sum_{i=1}^M KL(P_i(f,c)||\widetilde{P}(f,c))
\end{align}
where $KL(\cdot)$ is the Kullback-Leibler (KL) divergence \cite{aslam2007query} of each joint distribution $P_i(f,c)$ to the centroid $\widetilde{P}(f,c)$.
\end{theorem}

\noindent Finally, considering the non-negativity and convexity of the
KL-divergence \cite{brillouin2013science}, we have:

\begin{corollary}
\label{coro1}
When $\mathcal{D}$ is trained to its optimum $\mathcal{D}^*$, $\mathcal{J}_{\mathcal{D}^*}$ is $M \log M$. The optimum can be obtained if and only if $P_1(f,c) = P_2(f,c) = ... = P_M(f,c) = \widetilde{P}(f,c)$.
\end{corollary}

\noindent Therefore, by using conditional adversarial training, we can train the conditional domain discriminator on the joint variable $(f,c)$ to minimize the total divergence across different domains, yielding promising performance in MDTC tasks.

\section{Experiments}

We evaluate the effectiveness of the CAN model on both MDTC and unsupervised multi-source domain adaptation tasks. The former refers to the setting where the test data falls into one of the $M$ domains, and the latter refers to the setting where the test data comes from an unseen domain without labels. Moreover, an ablation study is provided for further analysis of the CAN model.

\subsection{Experimental Settings}
\paragraph{Dataset}

We conduct experiments on two MDTC benchmarks: the Amazon review dataset \cite{blitzer2007biographies} and the FDU-MTL dataset \cite{liu2017adversarial}. The Amazon review dataset consists of four domains: books, DVDs, electronics, and kitchen. For each domain, there exist 2,000 instances: 1,000 positive ones and 1,000 negative ones. All data was pre-processed into a bag of features (unigrams and bigrams), losing all word order information. In our experiments, the 5,000 most frequent features are used, representing each review as a 5,000-dimensional vector. The FDU-MTL dataset is a more complicated dataset, which contains 16 domains: books, electronics, DVDs, kitchen, apparel, camera, health, music, toys, video, baby, magazine, software, sport, IMDB, and MR. All data in the FDU-MTL dataset are raw text data, tokenized by the Stanford tokenizer. The detailed statistics of the FDU-MTL dataset are listed in the Appendix.

\paragraph{Implementation Details}

All experiments are implemented by using \textbf{PyTorch}. The CAN has one hyperparameter: $\lambda$, which is fixed as 1 in all experiments, the parameter sensitivity analysis is presented in the Appendix. We use Adam optimizer \cite{kingma2014adam}, with the learning rate 0.0001, for training. The batch size is 8. We adopt the same model architecture as in \cite{chen2018multinomial}. For the Amazon Review dataset, MLPs are used as feature extractors, with an input size of 5,000. Each feature extractor is composed of two hidden layers, with size 1,000 and 500, respectively. The output size of the shared feature extractor is 128 while 64 for the domain-specific ones. The dropout rate is 0.4 for each component. Classifier and discriminator are MLPs with one hidden layer of the same size as their input ($128+64$ for classifier and $128+2$ for discriminator). ReLU is used as the activation function. For the FDU-MTL dataset, CNN with a single convolutional layer is used as the feature extractor. It uses different kernel sizes $(3,4,5)$, and the number of kernels is 200. The input of the convolutional layer is a 100-dimensional vector, obtained by using word2vec \cite{mikolov2013efficient}, for each word in the input sequence.

\subsection{Multi-Domain Text Classification}

\begin{table*}[t]
\caption{\label{font-table} MDTC classification accuracies on the Amazon review dataset.}\smallskip
\label{table_ref1}
\centering
\resizebox{1.60\columnwidth}{!}{
\smallskip\begin{tabular}{ l|  c c c c c c c}
\hline
	Domain & CMSC-LS & CMSC-SVM & CMSC-Log & MAN-L2 & MAN-NLL & CAN(Proposed)\\
\hline
Books &  82.10 & 82.26 & 81.81 & 82.46 & 82.98 & $\mathbf{83.76\pm0.20}$ \\
DVD &  82.40 & 83.48 & 83.73 & 83.98 & 84.03 & $\mathbf{84.68\pm0.16}$ \\
Electr.  & 86.12 & 86.76 & 86.67 & 87.22 & 87.06 & $\mathbf{88.34\pm0.14}$ \\
Kit.  &  87.56 & 88.20 & 88.23 & 88.53 & 88.57 & $\mathbf{90.03\pm0.19}$\\
\hline
AVG  &  84.55 & 85.18 & 85.11 & 85.55 & 85.66 & $\mathbf{86.70\pm0.11}$\\
\hline
\end{tabular}}
\end{table*}
\begin{table*}[t]
\caption{\label{font-table} MDTC classification accuracies on the FDU-MTL dataset. }\smallskip
\label{table_ref2}
\centering
\resizebox{1.60\columnwidth}{!}{
\begin{tabular}{ l| c c c c c c c}
\hline
	Domain & MT-CNN & MT-DNN & ASP-MTL & MAN-L2 & MAN-NLL & MT-BL & CAN(Proposed)\\
\hline
books & 84.5 & 82.2 & 84.0 & 87.6 & 86.8 & $\mathbf{89.0}$ & $87.8\pm0.2$ \\
electronics & 83.2 & 81.7 & 86.8 & 87.4 & 88.8 & 90.2 & $\mathbf{91.6\pm0.5}$ \\
dvd & 84.0 & 84.2 & 85.5 & 88.1 & 88.6 & 88.0 & $\mathbf{89.5\pm0.4}$ \\
kitchen & 83.2 & 80.7 & 86.2 & 89.8 & 89.9 & 90.5 & $\mathbf{90.8\pm0.3}$\\
apparel & 83.7 & 85.0 & 87.0 & $\mathbf{87.6}$ & $\mathbf{87.6}$ & 87.2 & $87.0\pm0.7$\\
camera & 86.0 & 86.2 & 89.2 & 91.4 & 90.7 & 89.5 & $\mathbf{93.5\pm0.1}$\\
health & 87.2 & 85.7 & 88.2 & 89.8 & 89.4 & $\mathbf{92.5}$ & $90.4\pm0.6$ \\
music & 83.7 & 84.7 & 82.5 & 85.9 & 85.5 & 86.0 & $\mathbf{86.9\pm0.1}$ \\
toys & 89.2 & 87.7 & 88.0 & 90.0 & 90.4 & $\mathbf{92.0}$ & $90.0\pm0.3$ \\
video & 81.5 & 85.0 & 84.5 & 89.5 & $\mathbf{89.6}$ & 88.0 & $88.8\pm0.4$ \\
baby & 87.7 & 88.0 & 88.2 & 90.0 & 90.2 & 88.7 & $\mathbf{92.0\pm0.2}$ \\
magazine & 87.7 & 89.5 & 92.2 & 92.5 & 92.9 & 92.5 & $\mathbf{94.5\pm0.5}$ \\
software & 86.5 & 85.7 & 87.2 & 90.4 & 90.9 & $\mathbf{91.7}$ & $90.9\pm0.2$ \\
sports & 84.0 & 83.2 & 85.7 & 89.0 & 89.0 & 89.5 & $\mathbf{91.2\pm0.7}$ \\
IMDb & 86.2 & 83.2 & 85.5 & 86.6 & 87.0 & 88.0 & $\mathbf{88.5\pm0.6}$\\
MR & 74.5 & 75.5 & 76.7 & 76.1 & 76.7 & 75.7 & $\mathbf{77.1\pm0.9}$\\
\hline
AVG & 84.5 & 84.3 & 86.1 & 88.2 & 88.4  & 88.6  & $\mathbf{89.4\pm0.1}$ \\
\hline
\end{tabular} }
\end{table*}

\paragraph{Comparison Methods}

We first conduct experiments of multi-domain text classification. The CAN model is compared with a number of state-of-the-art methods, which are listed below:

\begin{itemize}
    \item MT-CNN: A CNN-based model which shares the lookup table across domains for better word embeddings \cite{collobert2008unified}.
    \item MT-DNN: The multi-task deep neural network model with bag-of-words input and MLPs, in which a hidden layer is shared \cite{liu2015representation}.
    \item CMSC-LS, CMSC-SVM, CMSC-Log: The collaborative multi-domain sentiment classification method combines an overall classifier across domains and a set of domain-dependent classifiers to make the final prediction. The models are trained on least square loss, hinge loss, and log loss, respectively \cite{wu2015collaborative}.
    \item ASP-MTL: The adversarial multi-task learning framework of text classification, which adopts the share-private scheme, adversarial learning, and orthogonality constraints \cite{liu2017adversarial}.
    \item MAN-L2, MAN-NLL: The multinomial adversarial network for multi-domain text classification \cite{chen2018multinomial}. This model uses two forms of loss functions to train domain discriminator: least square loss and negative log-likelihood loss.
    \item MT-BL: The multi-task learning with bidirectional language models for text classification, which adds language modeling and a uniform label distribution-based loss constraint to the domain-specific feature extractors and shared feature extractor, respectively \cite{yang2019multi}.
\end{itemize}

All the comparison methods use the standard partitions of the datasets. Thus, we cite the results from \cite{chen2018multinomial,yang2019multi} for fair comparisons.

\paragraph{Results}

\begin{table}
\caption{\label{font-table} Ablation study analysis.}\smallskip
\label{table_ref4}
\centering
\resizebox{0.8\columnwidth}{!}{
\begin{tabular}{ l| c c c c c }
\hline
Method & Books & DVD & Electr. & Kit. & AVG\\
\hline
	CAN (full)& 83.76 & 84.68 & 88.34 & 90.03 & 86.70 \\
CAN w/o C & 82.45 & 84.45 & 87.30 & 89.65 & 85.96 \\
CAN w/o E & 83.60 & 84.80 & 87.70 & 89.40 & 86.38 \\
CAN w/o CE & 82.98 & 84.03 & 87.06 & 88.57 & 85.66 \\
\hline
\end{tabular}}
\end{table}

\begin{table*}
\caption{\label{font-table} Unsupervised multi-source domain adaptation results on the Amazon review dataset.}\smallskip
\label{table_ref3}
\centering
\resizebox{1.60\columnwidth}{!}{
\begin{tabular}{ l| c c c c c c c c}
\hline
Domain & MLP & mSDA & DANN & MDAN(H) & MDAN(S) & MAN-L2 & MAN-NLL & CAN(Proposed)\\
\hline
Books & 76.55 & 76.98 & 77.89 & 78.45 & 78.63 & 78.45 & 77.78 & $\mathbf{78.91}$\\
DVD & 75.88 & 78.61 & 78.86 & 77.97 & 80.65 & 81.57 & 82.74 & $\mathbf{83.37}$\\
Elec. & 84.60 & 81.98 & 84.91 & 84.83 & $\mathbf{85.34}$ & 83.37 & 83.75 & 84.76\\
Kit. & 85.45 & 84.26 & 86.39 & 85.80 & 86.26 & 85.57 & 86.41 & $\mathbf{86.75}$\\
\hline
AVG & 80.46 & 80.46 & 82.01 & 81.76 & 82.72 & 82.24 & 82.67 & $\mathbf{83.45}$\\
\hline
\end{tabular}}
\end{table*}

We conduct MDTC experiments following the setting of \cite{chen2018multinomial}: A 5-fold cross-validation is implemented for the Amazon review dataset. All data is divided into 5 folds per domain: three of the five folds are used as the training set, one is the validation set, and the remaining one is treated as the test set. The 5-fold average test accuracy is reported. All reports are based on 5 runs.

Table \ref{table_ref1} and Table \ref{table_ref2} show the experimental results on the Amazon review dataset and the FDU-MTL dataset, respectively. From Table \ref{table_ref1}, it can be seen that our model yields state-of-the-art results not only for the average classification accuracy, but also on each individual domain.

From the experimental results on the FDU-MTL dataset, reported in Table \ref{table_ref2}, we can see that the CAN model obtains the best accuracies on 10 of 16 domains and achieves the best result in terms of the average classification accuracy. The experimental results on these two MDTC benchmarks illustrate the efficacy of our model.

\paragraph{Ablation Study}

The CAN model adopts a conditional domain discriminator and entropy conditioning. In this section, we investigate how these two strategies impact the performance of our model on the Amazon review dataset. In particular, three ablation variants are evaluated: (1) CAN w/o C, the variant of the proposed CAN model without conditioning the domain discriminator on label predictions, which utilizes the standard domain discriminator and entropy conditioning; (2) CAN w/o E, the variant of the proposed CAN model without the entropy conditioning, which hence imposes equal importance to different instances; (3) CAN w/o CE, the variant of the proposed CAN model which only uses standard adversarial training for domain alignment. The results of the ablation study are shown in Table \ref{table_ref4}, where we can see that all variants produce inferior results, and the full model gives the best performance. Thus, it indicates that both strategies contribute to the CAN model.

\subsection{Unsupervised Multi-Source Domain Adaptation}

In the MDTC scenario, the model requires labeled training data from each domain. However, in reality, many domains may have no labeled data at all. Therefore, it is important to evaluate the performance of unseen domains for MDTC models.

In the unsupervised multi-source domain adaptation setting, we have multiple source domains with both labeled and unlabeled data and one target domain with only unlabeled data. The CAN has the ability to learn domain-invariant representations on unlabeled data, and thus it can be generalized to unseen domains. Since the target domain has no labeled data at all, the domain discriminator is updated only on unlabeled data in this setting. When conducting text classification on the target domain, we only feed the shared feature to $\mathcal{C}$ and set the domain-specific feature vector to 0.

We conduct the experiments on the Amazon review dataset. In the experiments, three of the four domains are regarded as the source domains, and the remaining one is used as the target one. The evaluations are conducted on the target domain. In order to validate CAN's effectiveness, we compare CAN with several domain-agnostic methods, including: (1) the MLP model; (2) the marginalized denoising autoencoder (mSDA) \cite{chen2012marginalized}; (3) the domain adversarial neural network (DANN) \cite{ganin2016domain}, these methods ignore the differences among domains. And certain state-of-the-art unsupervised multi-source domain adaptation methods: (4) the multi-source domain adaptation neural network (MDAN(H) and MDAN(S)) \cite{zhao2017multiple}; (5) the multinomial adversarial network (MAN-L2 and MAN-NLL) \cite{chen2018multinomial}. When training the domain-agnostic methods, the data in the multiple source domains are combined together as a single source domain.

In Table \ref{table_ref3}, we observe that the CAN model outperforms all the comparison methods on three out of four domains. In terms of the average classification accuracy, the CAN method achieves superior performance. This suggests that our model has a good ability to generalize on unseen domains.

\section{Conclusion}
In this paper, we propose conditional adversarial networks (CANs) for multi-domain text classification. This approach can perform alignment on joint distributions of shared features and label predictions to improve the system performance. The CAN approach adopts the shared-private paradigm, trains domain discriminator by conditioning it on discriminative information conveyed by the label predictions to encourage the shared feature extractor to capture more discriminative information, and exploits entropy conditioning to guarantee the transferability of the learned features. Experimental results on two MDTC benchmarks demonstrate that the CAN model can not only improve the system performance on MDTC tasks effectively but also boost the generalization ability when tackling unseen domains.

redpajama_set_name: RedPajamaArXiv


text:
Our opponents are not super humans, says Jamal
Tribune Report
Published at 10:29 pm August 23rd, 2019
Bangladesh footballers react after reporting at a city hotel Friday BFF
The Afghans might be physically stronger than most of the Bangladesh players but captain Jamal Bhuiyan said he's not reading too much into it
Bangladesh's preparation for the upcoming World Cup 2022 and Asian Cup 2023 joint qualifier against Afghanistan, scheduled for September 10, began Friday as the 26 footballers reported at a city hotel.
The men in red and green will depart for neutral venue Dushanbe in Tajikistan on September 1 for the Afghan clash in order to get acclimatized to the conditions.
The Afghans might be physically stronger than most of the Bangladesh players but captain Jamal Bhuiyan said he's not reading too much into it.
"Size doesn't matter in football. Before maybe, but now size doesn't matter. If you have the right mentality, if you remain fit, anything can happen," said Jamal to the media in Dhaka.
Bangladesh men's national football team will begin their preparation for the next month's 2022 World Cup and 2023 Asian Cup qualifiers' opening group match against Afghanistan from Friday.https://t.co/1kTgilcea8
— Dhaka Tribune Sports (@Sport_DT) August 19, 2019
"I don't about the strategy. We met [yesterday], this is our first day in the camp. Coach (Jamie Day) is now in here so he has responsibility to set the plan and strategy for the team.
"Actually I don't know so much about Afghanistan. Jamie has videos and he will tell about the opponent team. We just know that they are strong side," he added.
The Saif Sporting Club midfielder said the current Bangladesh footballers have developed a great camaraderie over the past 15 months and that the other teams in the group – Qatar, India and Oman – pose no fear for them.
"As a captain, we all know that Qatar are the best in this group. For the second position, we have chance. There are also Oman and India. If we are fit, and prepare well then we have a chance because we are all human beings. They have no super humans. Some people considered the ranking and said we are not ready to face such opponents, but we know we are in good shape. Last 15 months we did well so I believe we have a great chance," said Jamal.
#Day eager as #national #camp begins on Friday https://t.co/9YhUMdsgaK
In the last few days, Bangladesh football has been riding a wave of euphoria with six-time professional league winner Abahani Limited beating North Korean outfit April 25 4-3 in the first leg of the AFC Cup Inter-Zone semi-final at Bangabandhu National Stadium Wednesday night.
The Bangladesh boys then blanked Bhutan 5-2 in the Saff U-15 Championship in West Bengal, India Friday.
Jamal said Abahani's triumph would inspire their compatriots to strive for success in the international stage.
"First of all, I want to say congratulations to Abahani. It was a big win for the club and for the country. Of course this would inspire the other players to work harder to do more because Abahani really did very well in the AFC Cup. Hopefully in future more Bangladeshi teams would represent their country there," said Jamal.
Utd executive Woodward's house attacked with flares
Man Utd close to completing Fernandes deal
Inter Milan sign Eriksen from Tottenham
Of Joya and her football dream
Marzia, Shamsunnahar get no club
Sabina, the top-paid signing
Bangladesh football
Abahani
Bangladesh football team
Jamie Day
Jamal Bhuiyan
Saff U-15 Championship
Bangladesh U-15 football team
Asian Cup Football

redpajama_set_name: RedPajamaCommonCrawl


I also have my own studio and produce my own voiceover reels as well!
I have a wide range. I can do soft and sultry as well as high pitch and energetic, teen voices. Very fun range. Great energy and great for kids spots too.
protools audio software and music production software, microphone, home studio. I can also deliver files with a program called You Send It or with idisk.
Got your vo demo on your website (great animation material) but couldn't pull up anything on this page. Maybe it's the iPad?
Hi Stefani. Feel free to email me your demo at porkysvoice@aol.com. Always easier to listen/critique via email than having to click around a website.
Thanks for the connect. Keep in touch.


text:
alain robert tour eiffel
Alain Robert (born as Robert Alain Philippe on 7 August 1962) is a French rock climber and urban climber, from Digoin, Saône-et-Loire, Burgundy, France.Known as "the French Spider-Man" (after the comic character Spider-Man) or "the Human Spider," Robert is famous for his free solo climbing, scaling skyscrapers using no … It is named after the engineer Gustave Eiffel, whose company designed and built the tower.. ... Tower and Opera House, and the Eiffel Tower were all conquered in 1997. Robert did it in his usual style, alone … A video of the man climbing the building has surfaced online, and people are comparing him to Spider-Man. Official website: http://www.tour-eiffel.fr. No Harness, No Fear: Man Arrested In Paris For Climbing The Tallest Building In The City, Play Cricket Quiz & Earn Upto 50,000 Coins Daily. The Eiffel Tower, a muse for the seventh art. Tickets: adult - € 7 to 17, young people from 12 to 24 years 5 to 15 euros, children from 4 to 12 years - € 3-10, depending on elevation, and method (lift or stairs). Masterpiece London 2015 opens with record attendees, significant sales across the board and major unveilings - Alain.R.Truong Exterior of Masterpiece London 2015 with 'Adam' and 'Eve' by Richard Hudson, represented by Leila Heller Gallery. The construction of a steel monster in the center of the capital caused an unusually sharp negative reaction of the French intelligentsia. Now the tower is one of the largest antennas of Europe, delivers a signal of 10 million people. In 1925, a con man, taking advantage of the press reports about the possible demolition of the tower, managed to sell it to unsuspecting buyer "for parts". With Jean-Claude Bouillon, Jean-Paul Tribout, Pierre Maguelon, Pinkas Braun. And there is in Paris more Parisian than the Eiffel tower. 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La Tur d'Eiffel (franzos La Tour Eiffel, [la tuʀ ɛˈfɛl]) è ina tur da fier d'in'autezza da 324 meters situada en la citad da Paris.La construcziun cun armadira da travs sa chatta en il 7avel arrondissement en il nordvest dal Champ de Mars en vischinanza da la riva da la Seine.L'edifizi è vegnì construì tranter il 1887 ed il 1889 sco portal … the tour eiffel Blogs, Comments and Archive News on Economictimes.com The urban climber has made a living of free climbing skyscrapers. And there is in Paris more Parisian than the Eiffel tower. Tagged under Alain Robert, Rocket, Building, Tower, Skyscraper. From there, you can climb to an open-air terrace more than 650 feet above the Parisian skyline. Robert has been solo climbing tall towers since the mid-1990s. However, at the end of the exhibition the interest in the tower fell, revived only after the Second world war. It has taken him to more than 150 skyscrapers around the world, including Dubai's Burj Khalifa, the world's tallest building, the Eiffel Tower and the Sydney Opera House. Won presented by engineer Gustave Eiffel: an elegant metal structure with height more than 300 meters, inscribed on the ground in a square with sides of 125 meters. Every day here thousands of tourists. Self-styled 'human Spider-man' Alain Robert has conquered the 153 metre high Galaxy Macau hotel in China to promote the latest Spider-Man movie. In those days there were registered two million visitors. Vu actuelle d'un drone survolant l'homme araignée pic.twitter.com/0Ibwfttsy0, This is also not the first time a climber dared to climb the Tour Montparnasse. #Paris pic.twitter.com/04f2Alyejo. Guy de Maupassant called it a "skeleton.". But gradually the controversy subsided. The tower has become the romantic legend of her eager lovers, her viewpoints are clearly visible leisurely Hay, magnificent palaces, bustling neighborhoods. Alain Robert has done it again. In 1944, American fighter pilot William Overstreet on his "Mustang" flying under the steel arches and knocked the Nazi "Messer". Note. For the exhibition of 1889 decided to build in the centre of Paris unseen, the tallest structure in the world. the tour eiffel Latest Breaking News, Pictures, Videos, and Special Reports from The Economic Times. The Tour Montparnasse is not as high as the famous Eiffel Tower, but remains the only skyscraper in the Paris city centre. These works, photos, engravings, objects and newspapers, dating essentially from the construction of the Eiffel Tower up to the 1930s, will be auctioned on Monday, June 26th on the 1st floor of the Eiffel Tower by Mr. Pierre Cornette of Saint Cyr, in association with the SOCIÉTÉ D'EXPLOITATION DE LA TOUR EIFFEL … Five years later, here began experimental TV broadcasting. Metallic tower of Fourviere: photos, description (Tour metallique de Fourviere), Statue of Liberty: photos, description Statue de la Liberte), Arc de Triomphe: photo description (Passerelle Debilly), The Palais de Chaillot: photos, description (Palais de Chaillot), The monument to Marshal Joseph Gallieni: photos, description Monument a Joseph Gallieni), Pont Alexandre III: photo description (Pont Alexandre III), Museum on the Quai Branly, photo, description (Musee du quai Branly), The Grand Palace (Grand Palais): photo description (Grand Palais). … Burj Khalifa Jeddah Tower Willis Building - Alain Robert - Tour Eiffel is a 1121x893 PNG image with a transparent background. Factories produced more than 18 thousand wrought iron parts, to assembling them took more than one million special rivets. The Tower was designed by Gustave Eiffel for the 1889 World's Fair, to mark the 100-year anniversary of the French Revolution. The video has gone crazy viral on social media and has been viewed more than 580,000 times with over 5,000 retweets and almost 16,000 likes. Robert terkenal kerana berjaya memanjat … The unique silhouette of the Tour Eiffel depicted in the paintings of Rousseau, Signac, Marquet, Utrillo, Chagall. REUTERS/Charles Platiau (FRANCE - Tags: CITYSCAPE) In the First world is that it was strategically important for France the transmitter, since 1920 access to the tower and got a civilian radio station. In 1887, Alexander Dumas, Charles Gounod, Sully Prudhomme - fifty artists who glorified France - publicly spoke out against the "useless and monstrous Eiffel tower". In 1996, climber Alain Robert without any devices has risen to the top of the steel mountains. Alain Robert AKA the french Spiderman a tenté d'escalader mercredi 6 juin la Lotte World Tower à Séoul. The tower has become the romantic legend of her eager lovers, her viewpoints are clearly visible leisurely Hay, magnificent palaces, bustling neighborhoods. Eiffel made use of new technology that was already popular in iron-framed buildings. The skyscraper climber Alain Robert, nicknamed the 'French Spiderman', struck again on October 25, when he climbed Heron Tower, one of the tallest buildings in London. Locally nicknamed "La dame de fer" (French for "Iron Lady"), it was constructed from … In 1996, climber Alain Robert without any devices has risen to the top of the steel mountains. However, his talent was imposed on the shape of the structure an indelible imprint. The Tour Montparnasse is not as high as the famous Eiffel Tower, but remains the only skyscraper in the Paris city centre. FRANCE/ - RTR2OVFY The Eiffel Tower and La Defence business district are seen in an aerial view in Paris on Bastille Day The Eiffel Tower (R) and La Defence business district (background) are seen in an aerial view in Paris on Bastille Day July 14, 2011. Last Thursday the Frenchman needed 36 minutes to reach the "summit" f the Tour Ariane, the large glass building in the Defense quarter of Paris. In fairness I must say that the original design of the tower was developed by the engineers Maurice Kahlin and Emile Nouguier, Eiffel also bought their patent. See more ideas about paris, photo, tour eiffel. Steel tower on the Champ de Mars - a monument to the daring of the nineteenth century. Steel tower weighing 7 thousand tons was the highlight of the world exhibition of 1889: the first week, still had no functioning lifts, about 30 thousand people rose to a height of three hundred meters on foot. The Human Spider backs up his nickname better than perhaps any other athlete past or present. Construction took more than two years and used some 18,000 pieces of metal and 2,500,000 rivets. Opening hours: from 15 June to 1 September from 9.00 am to 0.00, from 1 September to 15 June - from 9.30 to 22.30. Tour Montparnasse is a stunning skyscraper less than two miles from the Eiffel Tower. Nearest metro stations: "Bir Hakeim" line 6 to "Trocadero" (line 9). But it was not always so. But different levels are closed in different ways, the Elevator stops too early. As for use, it on the Eiffel tower in 1898, began experimenting with wireless communication. The Eiffel Tower was forced to close yesterday after a would-be climber nearly scaled to the top of the monument. La tour Coréenne est la 5e plus haute du monde. Alain Robert (born as Robert Alain Philippe on 7 August 1962) is a French rock climber and urban climber, from Digoin, Saône-et-Loire, Burgundy, France.Known as "the French Spider-Man" (after the comic character Spider-Man) or "the Human Spider," Robert is famous for his free solo climbing, scaling skyscrapers using no … On 8 October French climber Alain Robert scaled the 152m high Tour Ariane, at the Defense quarter in Paris, France. ALERTE INFO - Un homme est en train d'escalader la Tour #Montparnasse à mains nues, selon un témoin qui a contacté #Mediavenir. The Eiffel Tower (/ ˈ aɪ f əl / EYE-fəl; French: tour Eiffel [tuʁ‿ɛfɛl] ()) is a wrought-iron lattice tower on the Champ de Mars in Paris, France. Subscribe to Indiatimes and get handpicked updates based on your interests! Oct 22, 2020 - Explore Raul B's board "Paris" on Pinterest. Alain Robert relate ensuite ses ascensions, comme celle de la Tour Eiffel en 1996, qu'il qualifie « d'aventure sympathique » pour terminer l'année au sommet. During construction killed the only worker decided to show the bride how well he holds the balance on top. Extreme urban climber Alain Robert, nicknamed the 'French Spiderman', tackles the Tour T1, one of the tallest buildings in Paris, France, all without the aid of a harness. Il faisait – 15 °, il y avait de la glace sur la structure. He is not to be confused with another self-styled Spiderman, the … ... Back in 2015, a man named Alain Robert, also known as the French 'Spider-Man', climbed the skyscraper with a Nepalese flag in … Directed by Victor Vicas. A 38-second trip in Europe's fastest elevator will whisk you to a luxurious observation deck on the 56th floor. Back in 2015, a man named Alain Robert, also known as the French 'Spider-Man', climbed the skyscraper with a Nepalese flag in his hand, as a tribute to the victims of a devastating earthquake in the country.Â. Go Robert! The construction lasted two years, the number of workers at the construction site never exceeded two hundred and fifty. It wasn't the first time a climber had taken on the Tour Montparnasse. Alain Robert: Meet the real amazing Spider-Man. Alain Robert atau Robert Alain Philippe (lahir pada 7 Ogos 1962), adalah pemanjat bangunan dari Perancis.Beliau berasal dari Digoin, Saône-et-Loire, Bourgogne, Perancis.Beliau dikenali sebagai "Spider-Man Perancis" sebagaimana watak utama komik Marvel atau "Human Spider". The contest was extended to 107 projects. Eiffel tower - symbol of Paris, its silhouette is visible from almost anywhere here.
I Like You, I Don't Care About Your Boyfriend Traduction, Directeur D'hôtel De Luxe Salaire, Je Ne Suis Pas Autonome Au Travail, Vélo électrique Easy, Restaurant ô Baya St Pierre Réunion,
alain robert tour eiffel 2020


text:
rape culture (6 Results)
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Rape Culture and the Feminist Politics of Social Media
Carrie A. Rentschler
Young feminists use social media in order to respond to rape culture and to hold accountable the purveyors of its practices and ways of thinking when mainstream news media, police and school authorities do not. This article analyzes how social networks identified with young feminists take shape via social media responses to sexual violence, and how those networks are organized around the conceptual framework of rape culture. Drawing on the concept of response-ability, the article analyzes how recent social media responses to rape culture evidence the affective and technocultural nature of current feminist network building and the ways this online criticism re-imagines the position of feminist witnesses to rape culture.
In Girlhood Studies
Published (Print):
Tradition's Desire
The Politics of Culture in the Rape Trial of Jacob Zuma
Thembisa Waetjen and Gerhard Maré
This article examines the recent trial of ANC president Jacob Zuma, and how gender power was framed in respect to, and within, the politics of culture. The trial centred on allegations of rape by Zuma of an HIV positive woman many years his junior, who was also the daughter of a former anti-apartheid struggle comrade. All of these details were considered pertinent, not only to the legal debates about whether a crime had been committed, but also to the political debates raging around the nation's key challenges of high rates of sexual violence and the 'denialist' state response to devastating levels of HIV infection. Many Zuma supporters saw the accusation of rape as politically motivated and as evidence of an anti-Zuma conspiracy. In visibly smaller numbers, women's rights groups were present on the streets as well, trying to draw attention to the general problem of the nation's extraordinarily high rates of sexual violence and the general failure of the justice system to address cases of rape. The article argues that the fervour surrounding this trial, the burning political question of women's status was continually cast as a private matter: debates about relations between men and women came to be focused on issues of propriety, behaviour and etiquette rather than on questions about rights and power. In short, the privatisation of gender was effected through the politics of culture. As culture is politicised as a legal and secular 'right', gender is de-politicised to become a normatively 'private' and 'customary' domain. This is not merely a South African dilemma, but a dilemma which is con-concomitant to the social conditions of modernity itself.
In Theoria
Girls and Young Women Resisting Rape Culture through YouTube Videos
Chloe Krystyna Garcia and Ayesha Vemuri
Sexual violence continues to be normalized in modern society through heterosexist jokes and problematic portrayals of female sexuality. A number of young female activists use YouTube as a technology of nonviolence to share their thoughts about rape culture and how it can be transformed. We performed a thematic analysis of 10 videos produced by young women and girls to investigate what they identify as rape culture and how they use videos to communicate their messages. We argue that they offer meaningful insight into the institutions that contribute to the normalization of sexual violence, including schools and universities, the media, and legal and political systems. We believe that stakeholders interested in dismantling rape culture can use these videos to educate themselves and others about the concerns voiced by women and girls, who are, arguably, the population most affected by sexual violence.
Assessing Violence in the Modern World
Richard Bessel
The argument put forward by Steven Pinker that violence has been in decline and that "we have been getting kinder and gentler" rests to a considerable degree upon data concerning violent events, in particular homicide and deaths on the battlefield. In discussing such data for the modern period, this article questions their reliability and, in particular, their comparability over time. Pinker's argument may be stronger with respect to a growing public sensitivity toward many forms of violence, not least sexual violence, for which there is considerable evidence. However, the relationship between changing public sensibilities and changing levels of actual violent acts remains difficult to determine.
In Historical Reflections/Réflexions Historiques
"Moi quand on dit qu'une femme ment, eh bien, elle ment"
The Administration of Rape in Twenty-First Century France and England & Wales
Nicole Fayard and Yvette Rocheron
In France and England & Wales rape is now understood as a diverse social phenomenon. It is reported, counted, categorized, and dealt with by the authorities as a serious crime. Yet, despite notable initiatives intended to improve the conviction of alleged perpetrators, major hurdles for alleged victims remain. We show how rape is defined and prosecuted in France and England & Wales, and we use statistical analyzes to understand the scale of the problem, still largely unknown. We also discuss recent controversies (attrition rate;loicadre), exploring a culture of scepticism among police and judiciary that causes complaints to be dropped or downgraded to lesser crimes. Our interview material from France explores two difficulties: When is rape not rape? Did the alleged victim consent to the penetration? Finally we analyze the paradoxical role played by voluntary victim support groups that resist but also collude with a complex regulatory system that fails those who do not speak in legitimate codes.
In French Politics, Culture & Society
Silence against Political Rape
Arab Women's Subalterniy During Political Struggles
Ola Abdalkafor
Arab Spring movements in many Arab countries revealed a gap at the heart of Arab society and politics: the large-scale subalternity of Arab women in such movements. In this essay, I hypothesize that, with few exceptions, Arab women have always avoided participation in social and political activism because of their fear of political rape – raping women as punishment during political turmoil. The essay traces the history of political rape through different stages of Arab history. The examples are taken from history, literature and international reports and they mainly cover three countries: Syria, Egypt, and Libya. These examples prove that vulnerable women's horror at any possibility of their being sexually abused and then rejected by their families and society has always haunted them, preventing them from struggling or protesting. The essay concludes that subalternity is the only stance from which Arab women can encounter political rape. Then, the essay discusses the subalternity of Arab women in the light of the thought of the postcolonial critic Gayatri Spivak. This argument leads to the contention that the silence of Arab women vulnerable to political rape should not be considered passive and that feminist theories and actions cannot be successful in supporting subaltern Arab women without the ethical responsibility theorized by Spivak as the most appropriate approach to the subaltern female. This approach entails respecting subaltern Arab women's culture and fears and avoiding any attempt to make them copies of the European feminist self. Subaltern Arab women who are afraid of being sexually abused have the right to protect their bodies and stick to their culture while still participating in public life.
© 2019 Berghahn Books


The C-Note Files - Episode 19: Review - RETRO Video Game Magazine - Duration: 4 minutes, 42 seconds.
The C-Note Files - Episode 18: Collector's Corner - WATCH_DOGS PS4 CE Unboxing - Duration: 10 minutes.
The C-Note Files - Episode 17: Review - Custom DS4 Lightbar Decals - Duration: 5 minutes, 11 seconds.
The C-Note Files - Episode 16: Review - PS4 Smart Clip by Nyko - Duration: 8 minutes, 28 seconds.
The C-Note Files - Episode 15: Tutorial - PS4 Custom Skin - Duration: 15 minutes.
The C-Note Files - Episode 14: Tutorial - PS4 Hard Drive Upgrade - Duration: 15 minutes.
The C-Note Files - Episode 13: Tutorial - PS4 Voice Recognition - Duration: 6 minutes, 49 seconds.
The C-Note Files - Episode 12: Tutorial - PS4 Face Recognition - Duration: 6 minutes, 10 seconds.
The C-Note Files - Episode 11: Collector's Corner - Assassin's Creed IV Black Flag Unboxing - Duration: 8 minutes, 3 seconds.
The C-Note Files - Episode 10: Collector's Corner - Batman Arkham Origins CE Unboxing - Duration: 12 minutes.
The C-Note Files - Episode 9: Collector's Corner - The Last Of Us Post-Pandemic Edition Unboxing - Duration: 7 minutes, 44 seconds.
The C-Note Files - Episode 8: Collector's Corner - COD Ghosts Prestige Edition Unboxing - Duration: 8 minutes, 16 seconds.


text:
aL-Qaeda and C.I.A. Recording Studios announce the release of Bin Laden's "Greatest Hits"
You've read about it online and saw the text messages on your cell. Now, C.I.A. Recording Studios is pleased to announce the release of the long awaited Bin Laden CD, entitled: "Yankee Imperialists Go Home." Yes, this CD is bound to bring a bang--literally-- to anyone ordering it. The CD features some of BL's greatest hits, like:
You've read about it online and saw the text messages on your cell. Now, C.I.A. Recording Studios is pleased to announce the release of the long awaited Bin Laden CD, entitled: "Yankee Imperialists Go Home."
Yes, this CD is bound to bring a bang–literally– to anyone ordering it. The CD features some of BL's greatest hits, like:
Street Fighting Man… in the streets of Tal Afar, Hebron and Baghdad
Gimme Shelter (From Armageddon)
The Last Time (America embarks on another war for Empire)
You Can't Always Get What you Want (Ode to Ariel Sharon)
Under My Thumb (Are the American Occupation Forces)
Sympathy For The Devil: Dedicated to GW Bush
Missed You… (With my first shot)
Honky Tonk Women (Won't be seen in Islamabad)
It's All Over Now (Imperialist American Forces)
Beast Of Burden (Carrying IED's)
Shattered (By the B-2 Carpet Bombing)
Love Is Strong (But my AK-47 is stronger)
Anybody Seen My Baby (Since the Smart Bombs Dropped?)
And if you send in your money now–no Yankee dollars, only Euro's–our VP in charge of recording, Mike Jerkoff, will throw in a upcoming video they're working on that has an actor portraying BL, threatening death and destruction to America.
(Editor's note: This is a golden opportunity to view this unreleased video ahead of the official release. This video will not be released until Bush and Cheney need their aL-Qaeda "boogeyman" to pop up on cue, to scare and distract Americans from reality.
Word is that since the last video release of BL flopped, C.I.A. Recording Studios has upgraded their computers and is promising a more realistic looking BL the next time. Don't miss viewing this video before its actual launch date.)
The package consists of two 20-track CDs ( on the 72 Virgins label) that represent an unprecedented joint cooperative venture between Bin Laden, Rita Katz and S.I.T.E. and the C.I.A.
Destined to confirm to both long time and new fans why BL and company have been acknowledged as "The World's Most Dangerous Rock 'n Roll Band", the collection features digitally remastered classic BL hits.
Decades in the making, this CD will now be available to anyone with enough balls to send in 29.95 (Euro) to:
BL's Greatest Hits
P.O. Box 1984: Cave #3
Waziristan, Pakistan
P.S. Expect delivery in 6-10 weeks, unless the infidels find our recording studio.


Although Agent Coulson didn't take on much screen time this week, his presence as a leader in Agents of S.H.I.E.L.D.'s fourth episode might have been bigger than ever. Coulson's a quick-thinking man who's slow and deliberate when it comes to making actual moves, so it's naturally taken this long for us to get a full sense for his leadership. Here, in the not-so-cleverly titled "Eye Spy" episode, S.H.I.E.L.D. explores a theme that's probably been on most viewers' minds since the show's beginning: the necessary evils of surveillance and what it means for a leader to use it responsibly.
The idea is present early in "Eye Spy," where Skye uses those home-grown hacker skills of hers to identify suspects involved in a heist (a scene, by the way, which involved red-masked men and a severed hand. Probably the darkest scene we've viewed on S.H.I.E.L.D. so far.) Her tactics aren't terribly high-tech, maybe a glorified version of the way those years-long Catfish cases are cracked with Google image search. But importantly, here we see the extreme differences between S.H.I.E.L.D.'s tactics and what's actually possible with technology in one of Coulson's former S.H.I.E.L.D. agents-turned-bad: Akela Amador, a character who seems to touch an exposed nerve with the leader when she pops up as the problem.
It's quickly decided the reported heists would be near-impossible for a human to complete without some sort of psychic ability, a quality that is decidedly impossible in this universe (sorry, X-Men). And after Akela's attack on three S.H.I.E.L.D. members, which has just about everyone on-board to get rid of this menace permanently, we discover has an ocular-tool that beams x-ray-like vision to not only herself, but monitors to handlers pre-determining her every move. Here, Coulson's intuition is again proven—there's more than meets the, well, eye, and Akela's not serving simply herself.
In fact, the device she's fitted with could execute her if direct orders aren't carried out, and we see the character reacting to years of wearing the device in a showdown with Agent May, who Akela will receive orders to kill if she sees her. After Coulson knocks Akela out, avoiding a one-of-us-has-to-die-right-now situation, S.H.I.E.L.D. takes the weight of her next mission by installing the eye on Agent Ward, who's hilariously awkward banter with a guard brings the episode's comic relief and proves he's more of an action guy than a smooth talker. He hastily completes Akela's objective, and after a shifty surgery from Fitzsimmons, Akela is free after years of having her every vision monitored.
But most importantly, whatever happened between Akela's departure was resolved in the mind of Coulson, a now-peaceful guy who she's surprised isn't rubbing the whole thing in her face. On one hand, Coulson looks like a seasoned parent who'd rather let the weight of her situation be the punishment itself—he offers Akela a fair trial, he's calm and direct with his former team member, that I'm disappointed, but not as much as you are right now-kinda thing. And although S.H.I.E.L.D. is probably bigger in every way than the forces controlling her, it resonates that her former organization never held this kind of power over her, no matter how bad things might have been. It also seems this character knows better than to think Coulson just had a really relaxing time in Tahiti, so she thinks it over and asks May what happened to the guy. Four episodes in, and we don't know much more than Akela—Coulson's wildly different, maybe for the better, but we're starting to piece together that the man leading this team isn't the same guy who was taken out by Loki.
Still, S.H.I.E.L.D. feels slow-building, but with Graviton surely rising in the future, the Rising Tide looming in the background and Akela's controllers up in the air, maybe that's what we need for a long-running show. These one-offs that separate the show from what the super-sized spectacle it might have appeared to be at the beginning might be head-scratching on a week-to-week scale. But it appears that Marvel and ABC are simply building what S.H.I.E.L.D. requires—a stellar web of baddies to develop our young team around, and ones with pretty interesting histories with S.H.I.E.L.D. Throw all of that in with some still-funny humor and a tone that's on-point with every episode—I'm still excited to see what pops up next every week.


text:
/* Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package org.camunda.bpm.engine.rest.impl.history;

import javax.ws.rs.core.Response.Status;

import org.camunda.bpm.engine.rest.dto.runtime.JobDto;
import org.camunda.bpm.engine.rest.exception.InvalidRequestException;
import org.camunda.bpm.engine.rest.exception.RestException;
import org.camunda.bpm.engine.rest.history.HistoricActivityInstanceRestService;
import org.camunda.bpm.engine.rest.history.HistoricActivityStatisticsRestService;
import org.camunda.bpm.engine.rest.history.HistoricBatchRestService;
import org.camunda.bpm.engine.rest.history.HistoricCaseActivityInstanceRestService;
import org.camunda.bpm.engine.rest.history.HistoricCaseActivityStatisticsRestService;
import org.camunda.bpm.engine.rest.history.HistoricCaseInstanceRestService;
import org.camunda.bpm.engine.rest.history.HistoricDecisionInstanceRestService;
import org.camunda.bpm.engine.rest.history.HistoricDecisionStatisticsRestService;
import org.camunda.bpm.engine.rest.history.HistoricDetailRestService;
import org.camunda.bpm.engine.rest.history.HistoricExternalTaskLogRestService;
import org.camunda.bpm.engine.rest.history.HistoricIdentityLinkLogRestService;
import org.camunda.bpm.engine.rest.history.HistoricIncidentRestService;
import org.camunda.bpm.engine.rest.history.HistoricJobLogRestService;
import org.camunda.bpm.engine.rest.history.HistoricProcessInstanceRestService;
import org.camunda.bpm.engine.rest.history.HistoricTaskInstanceRestService;
import org.camunda.bpm.engine.rest.history.HistoricVariableInstanceRestService;
import org.camunda.bpm.engine.rest.history.HistoryRestService;
import org.camunda.bpm.engine.rest.history.UserOperationLogRestService;

import org.camunda.bpm.engine.rest.impl.AbstractRestProcessEngineAware;
import org.camunda.bpm.engine.runtime.Job;

import com.fasterxml.jackson.databind.ObjectMapper;

public class HistoryRestServiceImpl extends AbstractRestProcessEngineAware implements HistoryRestService {

  public HistoryRestServiceImpl(String engineName, ObjectMapper objectMapper) {
    super(engineName, objectMapper);
  }

  public HistoricProcessInstanceRestService getProcessInstanceService() {
    return new HistoricProcessInstanceRestServiceImpl(getObjectMapper(), getProcessEngine());
  }

  public HistoricCaseInstanceRestService getCaseInstanceService() {
    return new HistoricCaseInstanceRestServiceImpl(getObjectMapper(), getProcessEngine());
  }

  public HistoricActivityInstanceRestService getActivityInstanceService() {
    return new HistoricActivityInstanceRestServiceImpl(getObjectMapper(), getProcessEngine());
  }

  public HistoricCaseActivityInstanceRestService getCaseActivityInstanceService() {
    return new HistoricCaseActivityInstanceRestServiceImpl(getObjectMapper(), getProcessEngine());
  }

  public HistoricVariableInstanceRestService getVariableInstanceService() {
    return new HistoricVariableInstanceRestServiceImpl(getObjectMapper(), getProcessEngine());
  }

  public HistoricActivityStatisticsRestService getActivityStatisticsService() {
    return new HistoricActivityStatisticsRestServiceImpl(getProcessEngine());
  }

  public HistoricDecisionStatisticsRestService getDecisionStatisticsService() {
    return new HistoricDecisionStatisticsRestServiceImpl(getProcessEngine());
  }

  public HistoricCaseActivityStatisticsRestService getCaseActivityStatisticsService() {
    return new HistoricCaseActivityStatisticsRestServiceImpl(getProcessEngine());
  }

  public UserOperationLogRestService getUserOperationLogRestService() {
    return new UserOperationLogRestServiceImpl(getObjectMapper(), getProcessEngine());
  }

  public HistoricDetailRestService getDetailService() {
    return new HistoricDetailRestServiceImpl(getObjectMapper(), getProcessEngine());
  }

  public HistoricTaskInstanceRestService getTaskInstanceService() {
    return new HistoricTaskInstanceRestServiceImpl(getObjectMapper(), getProcessEngine());
  }

  public HistoricIncidentRestService getIncidentService() {
    return new HistoricIncidentRestServiceImpl(getObjectMapper(), getProcessEngine());
  }

  public HistoricIdentityLinkLogRestService getIdentityLinkService() {
    return new HistoricIdentityLinkLogRestServiceImpl(getObjectMapper(), getProcessEngine());
  }

  public HistoricJobLogRestService getJobLogService() {
    return new HistoricJobLogRestServiceImpl(getObjectMapper(), getProcessEngine());
  }

  public HistoricDecisionInstanceRestService getDecisionInstanceService() {
    return new HistoricDecisionInstanceRestServiceImpl(getObjectMapper(), getProcessEngine());
  }

  public HistoricBatchRestService getBatchService() {
    return new HistoricBatchRestServiceImpl(getObjectMapper(), getProcessEngine());
  }

  @Override
  public HistoricExternalTaskLogRestService getExternalTaskLogService() {
    return new HistoricExternalTaskLogRestServiceImpl(getObjectMapper(), getProcessEngine());
  }

  @Override
  public JobDto cleanupAsync(boolean immediatelyDue) {
    Job job = processEngine.getHistoryService().cleanUpHistoryAsync(immediatelyDue);
    return JobDto.fromJob(job);
  }

  @Override
  public JobDto findCleanupJob() {
    Job job = processEngine.getHistoryService().findHistoryCleanupJob();
    if (job == null) {
      throw new RestException(Status.NOT_FOUND, "History cleanup job does not exist");
    }
    return JobDto.fromJob(job);
  }
}

redpajama_set_name: RedPajamaGithub


ABC 27 reports that police have identified the pedestrian who was seriously injured in a Harrisburg auto accident.
The 67-year-old man was struck as he attempted to cross Route 22. He was struck by a westbound vehicle occupied by a family from the Hershey area. The man remains in critical condition at the Penn State Hershey Medical Center.
Investigators are currently looking for anyone who may have witnessed the Harrisburg auto accident.
Do you believe pedestrians have the right of way when crossing the street? Do you think a driver is always liable in Harrisburg auto accidents involving pedestrians?
If you have been injured in a Harrisburg auto accident, the Harrisburg auto accident attorneys at Schmidt Kramer Injury Lawyers can help you.


----Chanel No. 5 paper bag. The size of the bag is approximately 13" long x approximately 11" wide, not including handles.
---- Curve by Liz Claiborne paper bag from 1996. The size of the bag is approximately 10.75" long x approximately 8.75" wide, not including handles.
----Nautica Woman by Nautica paper bag from 1997. The size of the bag is approximately 13.5" long x approximately 9" wide, not including handles.
----Petits et Mamans by Bvlgari paper bag from 1997. The size of the bag is approximately 4" long x approximately 4" wide, not including handles.
-----Glamourous by Ralph Lauren mesh fabric bag. The size of the bag is approximately 9.5" long x approximately 9.5" wide, not including handles.


From prehistoric woolly mammoths to the Concord Town Cow, animals shape and reflect the history of the people of Concord. Be inspired by the furry, feathered, and fluffy creatures wild, tame, and magical that roam our town. Explore the history of Concord through animals in the Concord Museum's Rasmussen Education Center this April Vacation Week!
Get out your binoculars! We're going on a history hunt and we're not going to stop until we find every creature hidden in the galleries. Tours are 20 minutes. All ages welcome.
What do you do when there are termites in eighteenth-century wooden furniture? Has a mouse ever scurried through collection storage? Talks are 15 minutes. All ages welcome.
What magical creatures did Henry David Thoreau and his young companion Louisa May Alcott find on their walks through the forests of Concord? Sit by the roaring colonial hearth and hear stories written by Louisa May Alcott of the magical creatures that you might find on your next nature walk if you listen carefully and look for the clues left by fairies! Stay for one story or for the full 30 minutes. All ages welcome.
Meet the rabbits whose fluffy fur transforms into cozy clothes. Work with a master weaver through the process of making fabric from carding to spinning to weaving on a loom. All ages welcome.
What animals were in Concord 1,000 years ago? What about 10,000 years ago? Hold real stone tools and figure out how the people of Musketaquid lived and used animals to help their culture thrive. All ages welcome.
When English colonists came to Concord for the first time, they met animals and people that they had never seen before. The colonists brought farm animals on their boats all the way from England, which were new and unusual to the native people who had lived in this area for thousands of years. The different ways these two cultures used very different animals sometimes led to trade and friendship but sometimes caused misunderstanding and conflict. Look closely at Native American and English objects to find the similarities, differences, and opportunities for trade between the two cultures. All ages welcome.


The Punisher skull logo seen painted on the helmet of an Iraqi army soldier patrolling in Baghdad in 2007.
The stencils of skulls on the vests of Iraqi fighters entering Tikrit last week may look familiar to many Americans. The long fanged, wincing face is that of the Punisher, a Marvel comic character whose mission is to fight evil employing all means necessary.
The Punisher's journey from the mind of a Californian comic-book writer to the battle for Tikrit has been a long one. He was created 40 years ago as an anti-hero cameo for a Feb. 1974 edition of The Amazing Spider-Man.
In his regular life the Punisher is Frank Castle, a veteran of the Vietnam War whose family was killed in the crossfire of a mob dispute. Angry that the police fail to bring his family's killers to justice, Castle takes the law into his own hands as the Punisher, using torture, murder and kidnapping in his anti-crime crusade. For the Punisher the ends justify the means in fighting evil.
"That is paralleling the Shi'ite militia," says Aymenn al-Tamimi, a researcher at the Philadelphia-based think tank Middle East Forum.
As a poorly-guided vigilante the Punisher is a well-suited icon for the Iraqi security forces and Shi'ite militia that have been accused of looting towns, burning homes and murder in their fight against the Islamic State of Iraq and Greater Syria (ISIS).
Italian journalist Daniele Raineri documented the popularity of the Punisher image throughout Iraq in a series of tweets last week.
These Iraqi fighters are not the first Middle East militants to appropriate American insignia. Tamimi points to the example of the Iraqi Shiite militia Faylaq al-Wa'ad al-Sadiq, which uses the famous image of American soldiers raising the flag at Iwo Jima during World War II on their logo—despite its association with the U.S.
In the case of the Punisher, it was actually U.S. soldiers that first brought him to the battlefield in Iraq. The unit of Chris Kyle, the American Sniper, called themselves the Punishers, labeling their gear with the four-fang skull and painting it on walls of Iraqi homes and buildings to mark their territory.
For Conway, the appropriation of his comic character by gun-toting soldiers and militiamen is uncomfortable, if not depressing.
Perhaps the strangest thing for Conway is how popular the Punisher has become despite the character's moral ambiguity and violent actions. People wearing t-shirts with the skull emblem regularly approach Conway at comic-book conventions, proclaiming the Punisher is their favorite character.
"In my mind he's not a good guy," say Conway.
However, Conway says he can understand how the Punisher may appeal to soldiers and militiamen who risk their lives for a cause in sometimes morally difficult situations.
It was so attractive, that in Kyle's memoir he notes that his unit's sister platoon had wanted to use the Punisher also.
"We told them we are the Punishers. They had to get their own symbol," wrote Kyle. But while they might have stopped their American comrades from adopting the Punisher, they clearly had no control over the Iraqi fighters of today. And Conway has no control at all over who uses his character.


text:
package org.ry8.CeaFrame.activity;

import android.annotation.SuppressLint;
import android.app.Activity;
import android.content.Intent;
import android.os.Bundle;
import android.os.Handler;
import android.os.Message;

import org.ry8.CeaFrame.CeaFrameApp;
import org.ry8.CeaFrame.model.ActivityManagerModel;
import org.ry8.CeaFrame.model.BusinessMessage;
import cn.ecailan.esy.R;

import org.json.JSONException;

@SuppressLint("NewApi")
public class BaseActivity extends Activity implements Handler.Callback
{
    public Handler mHandler;

    public BaseActivity()
    {

    }

    @Override
    protected void onCreate(Bundle savedInstanceState) {
        // TODO Auto-generated method stub
        super.onCreate(savedInstanceState);
        mHandler = new Handler(this);
        ActivityManagerModel.addLiveActivity(this);
    }

    @Override
	public void setContentView(int layoutResID) {
		// TODO Auto-generated method stub
		super.setContentView(layoutResID);
		// 由于view必须在视图记载之后添加注入
		getTAApplication().getInjector().injectView(this);
	}

	public CeaFrameApp getTAApplication() {
		return (CeaFrameApp) getApplication();
	}

    @Override
    protected void onStart()
    {
        super.onStart();
        ActivityManagerModel.addVisibleActivity(this);
    }

    @Override
    protected void onStop() {
        super.onStop();

        ActivityManagerModel.removeVisibleActivity(this);
    }

    @Override
    protected void onRestart() {
        super.onRestart();
    }

    @Override
    protected void onResume() {
        super.onResume();

        ActivityManagerModel.addForegroundActivity(this);
    }

    @Override
    protected void onPause() {
        super.onPause();
        ActivityManagerModel.removeForegroundActivity(this);
    }

    @Override
    protected void onDestroy() {
        super.onDestroy();
        ActivityManagerModel.removeLiveActivity(this);
    }

    @Override
    public boolean handleMessage(Message msg) {
        return false;
    }

    public void OnMessageResponse(BusinessMessage response)
            throws JSONException
    {
        if (response.messageState == BusinessMessage.FAILURE_MESSAGE)
        {
        }
    }

    @Override
    public void startActivity(Intent intent) {
        super.startActivity(intent);
        overridePendingTransition(R.anim.push_right_in,R.anim.push_right_out);
    }

    @Override
    public void finish() {
        super.finish();
        overridePendingTransition(R.anim.push_left_in,R.anim.push_left_out);
    }
}


This collector's version is presented in an elegant box protecting the book, signed by Anders Petersen.
Each print on Hahnemühle fine art is available in 10 copies only, in paper format 18 x 24cm at a unit price of 400€ for N° 1 & N°3. For N°2 & N°4, you must contact us for further information.


With its British accent, the Bar of the Domaine aims to cultivate unique moments. During daylight, the view of the window overlooking the park is an exquisite spectacle. At night, under the stars with a glass of champagne, it embraces and shares the pleasure of jazz music or various other styles of concerts honoring the most famous of sparkling wines.
The wine list offers more than 600 references of champagne, from small producers to famous Houses, and more than 1000 references of wine. A snack menu is also available.