Cooperative order and excitation spectra in the bicomponent spin networks

A ferrimagnetic spin model composed of $S=1/2$ spin-dimers and $S=5/2$ spin-chains is studied by combining the bond-operator representation (for $S=1/2$ spin-dimers) and Holstein-Primakoff transformation (for $S=5/2$ spins). A finite interaction $J_{\rm DF}$ between the spin-dimer and the spin chain makes the spin chains ordered antiferromagnetically and the spin dimers polarized. The effective interaction between the spin chains, mediated by the spin dimers, is calculated up to the third order. The staggered magnetization in the spin dimer is shown proportional to $J_{\rm DF}$. It presents an effective staggered field reacting on the spin chains. The degeneracy of the triplons is lifted due to the chain magnetization and a mode with longitudinal polarization is identified. Due to the triplon-magnon interaction, the hybridized triplon-like excitations show different behaviors near the vanishing $J_{\rm DF}$. On the other hand, the hybridized magnon-like excitations open a gap $\Delta_A\sim J_{\rm DF}$. These results consist well with the experiments on Cu$_{2}$Fe$_{2}$Ge$_{4}$O$_{13}$.Comment: 7 pages, 5 figure

Matching Natural Language Sentences with Hierarchical Sentence Factorization

Semantic matching of natural language sentences or identifying the relationship between two sentences is a core research problem underlying many natural language tasks. Depending on whether training data is available, prior research has proposed both unsupervised distance-based schemes and supervised deep learning schemes for sentence matching. However, previous approaches either omit or fail to fully utilize the ordered, hierarchical, and flexible structures of language objects, as well as the interactions between them. In this paper, we propose Hierarchical Sentence Factorization---a technique to factorize a sentence into a hierarchical representation, with the components at each different scale reordered into a "predicate-argument" form. The proposed sentence factorization technique leads to the invention of: 1) a new unsupervised distance metric which calculates the semantic distance between a pair of text snippets by solving a penalized optimal transport problem while preserving the logical relationship of words in the reordered sentences, and 2) new multi-scale deep learning models for supervised semantic training, based on factorized sentence hierarchies. We apply our techniques to text-pair similarity estimation and text-pair relationship classification tasks, based on multiple datasets such as STSbenchmark, the Microsoft Research paraphrase identification (MSRP) dataset, the SICK dataset, etc. Extensive experiments show that the proposed hierarchical sentence factorization can be used to significantly improve the performance of existing unsupervised distance-based metrics as well as multiple supervised deep learning models based on the convolutional neural network (CNN) and long short-term memory (LSTM).Comment: Accepted by WWW 2018, 10 page

Metal-organic framework-derived single atom catalysts for electrocatalytic reduction of carbon dioxide to C1 products

Electrochemical carbon dioxide reduction reaction (eCO RR) is an efficient strategy to relieve global environmental and energy issues by converting excess CO from the atmosphere to value-added products. Single-atom catalysts (SACs) derived from metal-organic frameworks (MOF), which feature unique active sites and adjustable structures, are emerging as extraordinary materials for eCO RR. By modulating the MOF precursors and their fabrication strategy, MOF-derived SACs with specific-site coordination configuration have been recently designed for the conversion of CO to targeted products. In the first part of this review, MOF synthesis routes to afford well-dispersed SACs along with the respective synthesis strategy have been systematically reviewed, and typical examples for each strategy have been discussed. Compared with traditional M-N active sites, SACs with regulated coordination structures have been rapidly developed for eCO RR. Secondly, the relationship between regulation of the coordination environment of the central metal atoms, including asymmetrical M-N sites, heteroatom doped M-N sites, and dual-metal active sites (M-M sites), and their respective catalytic performance has been systematically discussed. Finally, the challenges and future research directions for the application of SACs derived from MOFs for eCO RR have been proposed

Evaluation of Biological Toxicity of CdTe Quantum Dots with Different Coating Reagents according to Protein Expression of Engineering Escherichia coli

The results obtained from toxicity assessment of quantum dots (QDs) can be used to establish guidelines for the application of QDs in bioimaging. This paper focused on the design of a novel method to evaluate the toxicity of CdTe QDs using engineering Escherichia coli as a model. The toxicity of mercaptoacetic acid (MPA), glutathione (GSH), and L-cysteine (Cys) capped CdTe QDs was analyzed according to the heterologous protein expression in BL21/DE3, engineering Escherichia coli extensively used for protein expression. The results showed that the MPA-CdTe QDs had more serious toxicity than the other two kinds of CdTe QDs. The microscopic images and SEM micrographs further proved that both the proliferation and the protein expression of engineering Escherichia coli were inhibited after treatment with MPA-CdTe QDs. The proposed method is important to evaluate biological toxicity of both QDs and other nanoparticles

The role of EGFR mutation as a prognostic factor in survival after diagnosis of brain metastasis in non-small cell lung cancer: A systematic review and meta-analysis

Abstract Background The brain is a common site for metastasis in non-small-cell lung cancer (NSCLC). This study was designed to evaluate the relationship between the mutational of the epidermal growth factor receptor (EGFR) and overall survival (OS) in NSCLC patients with brain metastases. Methods Searches were performed in PubMed, EmBase, and the Cochrane Library to identify studies evaluating the association of EGFR mutation with OS in NSCLC patients through September 2017. Results 4373 NSCLC patients with brain metastases in 18 studies were involved. Mutated EGFR associated with significantly improved OS compared with wild type. Subgroup analyses suggested that this relationship persisted in studies conducted in Eastern, with retrospective design, with sample size ≥500, mean age of patients ≥65.0 years, percentage male < 50.0%, percentage of patients receiving tyrosine kinase inhibitor ≥30.0%. Finally, although significant publication bias was observed using the Egger test, the results were not changed after adjustment using the trim and fill method. Conclusions This meta-analysis suggests that EGFR mutation is an important predictive factor linked to improved OS for NSCLC patients with brain metastases. It can serve as a useful index in the prognostic assessment of NSCLC patients with brain metastases

IoT and Wearable Devices-Enhanced Information Provision of AR Glasses: A Multi-Modal Analysis in Aviation Industry

While Augmented Reality (AR) glasses are now instrumental in industries for delivering work-related information, the current one-size-fits-all information provision of AR glasses fails to cater to diverse workers’ needs and environmental conditions. We propose a framework for harnessing Internet of thing (IoT) and wearable technology to improve the adaptability and customization of information provision by AR. As a preliminary exploration, this short paper develops a multi-modal data processing system for work performance classification in the aviation industry. Using machine learning algorithms for multi-modal feature extraction and classifier construction, this framework provides a more objective and consistent evaluation of work performance compared to single-modal approaches. The proposed analytics architecture can provide valuable insights for other industries struggling to implement IoT and mixed reality

Timing of Maximal Weight Reduction Following Bariatric Surgery: A Study in Chinese Patients

Introduction: Bariatric surgery is a well-received treatment for obesity with maximal weight loss at 12–36 months postoperatively. We investigated the effect of early bariatric surgery on weight reduction of Chinese patients in accordance with their preoperation characteristics. Materials and Methods: Altogether, 409 patients with obesity from a prospective cohort in a single bariatric center were enrolled retrospectively and evaluated for up to 4 years. Measurements obtained included surgery type, duration of diabetic condition, besides the usual body mass index data tuple. Weight reduction was expressed as percent total weight loss (%TWL) and percent excess weight loss (%EWL). Results: RYGB or SG were performed laparoscopically without mortality or complications. BMI generally plateaued at 12 months, having decreased at a mean of 8.78 kg/m2. Successful weight loss of \u3e 25% TWL was achieved by 35.16, 49.03, 39.22, 27.74, 20.83% of patients at 6, 12, 24, 36, and 48 months after surgery. Overall, 52.91% of our patients had lost 100% of their excess weight at 12 months, although there was a rather wide range among individuals. Similar variability was revealed in women of child-bearing age. Conclusion: Chinese patients undergoing bariatric surgery tend to achieve maximal weight loss and stabilization between 12 and 24 months postoperatively, instead of at \u3e 2 years. The finding of the shorter stabilization interval has importance to earlier intervention of weight loss related conditions and women\u27s conception planning