Coarse Alignment of Topic and Sentiment: A Unified Model for Cross-Lingual Sentiment Classification.

Cross-lingual sentiment classification (CLSC) aims to leverage rich-labeled resources in the source language to improve prediction models of a resource-scarce domain in the target language. Existing feature representation learning-based approaches try to minimize the difference of latent features between different domains by exact alignment, which is achieved by either one-to-one topic alignment or matrix projection. Exact alignment, however, restricts the representation flexibility and further degrades the model performances on CLSC tasks if the distribution difference between two language domains is large. On the other hand, most previous studies proposed document-level models or ignored sentiment polarities of topics that might lead to insufficient learning of latent features. To solve the abovementioned problems, we propose a coarse alignment mechanism to enhance the model's representation by a group-to-group topic alignment into an aspect-level fine-grained model. First, we propose an unsupervised aspect, opinion, and sentiment unification model (AOS), which trimodels aspects, opinions, and sentiments of reviews from different domains and helps capture more accurate latent feature representation by a coarse alignment mechanism. To further boost AOS, we propose ps-AOS, a partial supervised AOS model, in which labeled source language data help minimize the difference of feature representations between two language domains with the help of logistics regression. Finally, an expectation-maximization framework with Gibbs sampling is then proposed to optimize our model. Extensive experiments on various multilingual product review data sets show that ps-AOS significantly outperforms various kinds of state-of-the-art baselines.

Large Non-planar Conjugated Molecule with Strong Intermolecular Interactions Achieved with Homoleptic Zn(II) Complex of Di(5-quinolylethynyl)-tetraphenylazadipyrromethene.

Zinc(II) complexes of tetraphenylazadipyrromethenes are potential non-planar n-type conjugated materials. To tune the properties, we installed 5-quinolylethynyl groups at the pyrrolic positions. Compared to the complex with 1-napthylethynyl, we found evidence for stronger intermolecular interactions in the new complex, including much higher overlap integrals in crystals. X-ray analysis revealed unconventional C-H···N hydrogen bonding between two quinolyls of neighboring molecules, pointing to a new strategy for the development of non-planar molecular semiconductors with stronger intermolecular interactions.

Softly Associative Transfer Learning for Cross-Domain Classification.

The main challenge of cross-domain text classification is to train a classifier in a source domain while applying it to a different target domain. Many transfer learning-based algorithms, for example, dual transfer learning, triplex transfer learning, etc., have been proposed for cross-domain classification, by detecting a shared low-dimensional feature representation for both source and target domains. These methods, however, often assume that the word clusters matrix or the clusters association matrix as knowledge transferring bridges are exactly the same across different domains, which is actually unrealistic in real-world applications and, therefore, could degrade classification performance. In light of this, in this paper, we propose a softly associative transfer learning algorithm for cross-domain text classification. Specifically, we integrate two non-negative matrix tri-factorizations into a joint optimization framework, with approximate constraints on both word clusters matrices and clusters association matrices so as to allow proper diversity in knowledge transfer, and with another approximate constraint on class labels in source domains in order to handle noisy labels. An iterative algorithm is then proposed to solve the above problem, with its convergence verified theoretically and empirically. Extensive experimental results on various text datasets demonstrate the effectiveness of our algorithm, even with the presence of abundant state-of-the-art competitors.

A Chiral Sensor Array for Peptidoglycan Biosynthesis Monitoring Based on MoS2 Nanosheet-Supported Host-Guest Recognitions.

Monitoring the dynamic change with respect to chirality and species of amino acids in bacterial peptidoglycan (PG) during cell wall biosynthesis is correlated with bacterial taxonomy, physiology, micropathology, and antibacterial mechanisms. However, this is challenging because reported methods usually lack the ability of chiral analysis with the coexistence of d- and l-amino acids in PG. Here we report a chiral sensor array for PG biosynthesis monitoring through chiral amino acid recognition. Multitypes of host molecule modified MoS2 nanosheets (MNSs) were used as receptor units to achieve more accurate and specific sensing. By applying indicator displacement strategy, the distinct and reproducible fluorescence-response patterns were obtained for linear discriminant analysis (LDA) to accurately discriminate achiral Gly, 19 l-amino acids and the corresponding 19 d-enantiomers simultaneously. The sensor array has also been used for identifying bacterial species and tracking the subtle change of amino acid composition of PG including chirality and species during biosynthesis in different growth status and exogenous d-amino acid stimulation.

Massive pulmonary embolism after application of an Esmarch bandage.

UNLABELLED: A 71-yr-old patient who underwent spinal anesthesia for left femoral fracture operation became hypotensive and unconscious after the application of an Esmarch bandage. The transesophageal echocardiography performed during resuscitation revealed pulmonary embolism and acute right ventricular failure. Pulmonary embolectomy with cardiopulmonary bypass was undertaken immediately after the echocardiographic diagnosis. Extracorporeal membrane oxygenation was used after the operation to support the failing right ventricle. The patient was successfully weaned from extracorporeal membrane oxygenation 10 days after the operation. We conclude that transesophageal echocardiography can be very useful in the immediate differential diagnosis of sudden cardiovascular collapse and that extracorporeal membrane oxygenation can be very helpful when acute right ventricular failure follows massive pulmonary embolism. IMPLICATIONS: Transesophageal echocardiography was highly valuable in finding the cause of sudden intraoperative cardiovascular collapse. The use of extracorporeal membrane oxygenation to support the failing right ventricle after emergent pulmonary embolectomy could help to rescue patients with massive pulmonary embolism.