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Sharing source and target side vocabularies and word embeddings has been a popular practice in neural machine translation (briefly, NMT) for similar languages (e.g., English to French or German translation). The success of such wordlevel sharing motivates us to move one step further: we consider model-level sharing and tie the whole parts of the encoder and decoder of an NMT model. We share the encoder and decoder of Transformer (Vaswani et al. 2017), the state-of-the-art NMT model, and obtain a compact model named Tied Transformer. Experimental results demonstrate that such a simple method works well for both similar and dissimilar language pairs. We empirically verify our framework for both supervised NMT and unsupervised NMT: we achieve a 35.52 BLEU score on IWSLT 2014 German to English translation, 28.98/29.89 BLEU scores on WMT 2014 English to German translation without/with monolingual data, and a 22.05 BLEU score on WMT 2016 unsupervised German to English translation.

Surveillance and security scenarios usually require high efficient facial image compression scheme for face recognition and identification. While either traditional general image codecs or special facial image compression schemes only heuristically refine codec separately according to face verification accuracy metric. We propose a Learning based Facial Image Compression (LFIC) framework with a novel Regionally Adaptive Pooling (RAP) module whose parameters can be automatically optimized according to gradient feedback from an integrated hybrid semantic fidelity metric, including a successfully exploration to apply Generative Adversarial Network (GAN) as metric directly in image compression scheme. The experimental results verify the framework's efficiency by demonstrating performance improvement of 71.41%, 48.28% and 52.67% bitrate saving separately over JPEG2000, WebP and neural network-based codecs under the same face verification accuracy distortion metric. We also evaluate LFIC's superior performance gain compared with latest specific facial image codecs. Visual experiments also show some interesting insight on how LFIC can automatically capture the information in critical areas based on semantic distortion metrics for optimized compression, which is quite different from the heuristic way of optimization in traditional image compression algorithms.

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Integrative value generation through negotiated business deals is a fundamental way in which organizations and economic systems attain economic benefits. It is also an important way in which individuals can improve their financial situation. We propose that individuals most in need of improving their financial standing, those in a financially vulnerable situation, are least likely to reap the benefits of integrative value generation. We theorize that financial vulnerability induces a more zero-sum construal of success, or a view that success for one person must come at another person's success. A more zero-sum construal of success, in turn, hampers negotiators' ability to realize integrative potential in negotiations. In a large archival dataset (N ϭ 191,648), we found evidence that various proxies of financial vulnerability are associated with a more zero-sum construal of success. In two subsequent face-to-face negotiation studies, we found that financial vulnerability, whether measured or induced experimentally, undermined integrative value generation. The final two-part study found evidence of the hypothesized psychological process. Taken together, our studies uncover a fundamental pathway through which the disadvantage of financially vulnerable people is reproduced through economic exchanges.

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