Improved English to Russian Translation by Neural Suffix Prediction

Neural machine translation (NMT) suffers a performance deficiency when a limited vocabulary fails to cover the source or target side adequately, which happens frequently when dealing with morphologically rich languages. To address this problem, previous work focused on adjusting translation granularity or expanding the vocabulary size. However, morphological information is relatively under-considered in NMT architectures, which may further improve translation quality. We propose a novel method, which can not only reduce data sparsity but also model morphology through a simple but effective mechanism. By predicting the stem and suffix separately during decoding, our system achieves an improvement of up to 1.98 BLEU compared with previous work on English to Russian translation. Our method is orthogonal to different NMT architectures and stably gains improvements on various domains.Comment: 8 pages, 3 figures, 5 table

Stacking engineering induced Z-scheme MoSSe/WSSe heterostructure for photocatalytic water splitting

Stacking engineering is a popular method to tune the performance of two-dimensional materials for advanced applications. In this work, Jansu MoSSe and WSSe monolayers are constructed as a van der Waals (vdWs) heterostructure by different stacking configurations. Using first-principle calculations, all the relaxed stacking configurations of the MoSSe/WSSe heterostructure present semiconductor properties while the direct type-II band structure can be obtained. Importantly, the Z-scheme charge transfer mode also can be addressed by band alignment, which shows the MoSSe/WSSe heterostructure is an efficient potential photocatalyst for water splitting. In addition, the built-in electric field of the MoSSe/WSSe vdWs heterostructure can be enhanced by the S–Se interface due to further asymmetric structures, which also results in considerable charge transfer comparing with the MoSSe/WSSe vdWs heterostructure built by the S–S interface. Furthermore, the excellent optical performances of the MoSSe/WSSe heterostructure with different stacking configurations are obtained. Our results provide a theoretical guidance for the design and control of the two-dimensional heterostructure as photocatalysts through structural stacking

Statistics and Analysis of the Relations between Rainstorm Floods and Earthquakes

The frequent occurrence of geophysical disasters under climate change has drawn Chinese scholars to pay their attention to disaster relations. If the occurrence sequence of disasters could be identified, long-term disaster forecast could be realized. Based on the Earth Degassing Effect (EDE) which is valid, this paper took the magnitude, epicenter, and occurrence time of the earthquake, as well as the epicenter and occurrence time of the rainstorm floods as basic factors to establish an integrated model to study the correlation between rainstorm floods and earthquakes. 2461 severe earthquakes occurred in China or within 3000 km from China and the 169 heavy rainstorm floods occurred in China over the past 200+ years as the input data of the model. The computational results showed that although most of the rainstorm floods have nothing to do with the severe earthquakes from a statistical perspective, some floods might relate to earthquakes. This is especially true when the earthquakes happen in the vapor transmission zone where rainstorms lead to abundant water vapors. In this regard, earthquakes are more likely to cause big rainstorm floods. However, many cases of rainstorm floods could be found after severe earthquakes with a large extent of uncertainty

T-Eval: Evaluating the Tool Utilization Capability of Large Language Models Step by Step

Large language models (LLM) have achieved remarkable performance on various NLP tasks and are augmented by tools for broader applications. Yet, how to evaluate and analyze the tool-utilization capability of LLMs is still under-explored. In contrast to previous works that evaluate models holistically, we comprehensively decompose the tool utilization into multiple sub-processes, including instruction following, planning, reasoning, retrieval, understanding, and review. Based on that, we further introduce T-Eval to evaluate the tool utilization capability step by step. T-Eval disentangles the tool utilization evaluation into several sub-domains along model capabilities, facilitating the inner understanding of both holistic and isolated competency of LLMs. We conduct extensive experiments on T-Eval and in-depth analysis of various LLMs. T-Eval not only exhibits consistency with the outcome-oriented evaluation but also provides a more fine-grained analysis of the capabilities of LLMs, providing a new perspective in LLM evaluation on tool-utilization ability. The benchmark will be available at https://github.com/open-compass/T-Eval.Comment: Project: https://open-compass.github.io/T-Eva

Rational syntheses of helical π-conjugated oligopyrrins with a bipyrrole linkage: geometry control of bis-copper(II) coordination

Rational syntheses of long-chain helical π-conjugated oligopyrrins and their bis-copper complexes afford systematically modulated optical and magnetic properties.</p

ISPTM: an Iterative Search Algorithm for Systematic Identification of Post-translational Modifications from Complex Proteome Mixtures

Identifying protein post-translational modifications (PTMs) from tandem mass spectrometry data of complex proteome mixtures is a highly challenging task. Here we present a new strategy, named iterative search for identifying PTMs (ISPTM), for tackling this challenge. The ISPTM approach consists of a basic search with no variable modification, followed by iterative searches of many PTMs using a small number of them (usually two) in each search. The performance of the ISPTM approach was evaluated on mixtures of 70 synthetic peptides with known modifications, on an 18-protein standard mixture with unknown modifications and on real, complex biological samples of mouse nuclear matrix proteins with unknown modifications. ISPTM revealed that many chemical PTMs were introduced by urea and iodoacetamide during sample preparation and many biological PTMs, including dimethylation of arginine and lysine, were significantly activated by Adriamycin treatment in NM associated proteins. ISPTM increased the MS/MS spectral identification rate substantially, displayed significantly better sensitivity for systematic PTM identification than the conventional all-in-one search approach and offered PTM identification results that were complementary to InsPecT and MODa, both of which are established PTM identification algorithms. In summary, ISPTM is a new and powerful tool for unbiased identification of many different PTMs with high confidence from complex proteome mixtures