Using categorical DEA to assess the effect of subsidy policies and technological learning on R&D efficiency of it industry

Government subsidies are an important policy tool that can help firms develop technological learning, and this technological learning effect plays a key role in firms’ research and development (R&D) efficiency. Thus, this study develops a two-stage approach to illustrate the effect of subsidy policies and technological learning on R&D efficiency in the information technology (IT) industry. The technological learning effect in 128 firms in the IT industry from 2008 to 2015 was measured using the learning experience curve. Subsequently, government R&D subsidy intensity was considered as a categorical variable, and this estimated result was treated as an intangible input into a data envelopment analysis (DEA) structure to evaluate R&D efficiency in 2015. This study makes three major contributions. First, the developed approach incorporates the effect of subsidy policies and technological learning into the DEA structure. Second, the empirical results demonstrate the appropriateness of incorporating subsidy policies and technological learning into evaluations of R&D efficiency. Finally, our results identify the key sources of inefficiency as a shortfall in the number of patents and a lack of technological learning. Based on these key findings, some improved strategies were recommended to decision makers. First published online 19 November 201

Non-invasive Self-attention for Side Information Fusion in Sequential Recommendation

Sequential recommender systems aim to model users' evolving interests from their historical behaviors, and hence make customized time-relevant recommendations. Compared with traditional models, deep learning approaches such as CNN and RNN have achieved remarkable advancements in recommendation tasks. Recently, the BERT framework also emerges as a promising method, benefited from its self-attention mechanism in processing sequential data. However, one limitation of the original BERT framework is that it only considers one input source of the natural language tokens. It is still an open question to leverage various types of information under the BERT framework. Nonetheless, it is intuitively appealing to utilize other side information, such as item category or tag, for more comprehensive depictions and better recommendations. In our pilot experiments, we found naive approaches, which directly fuse types of side information into the item embeddings, usually bring very little or even negative effects. Therefore, in this paper, we propose the NOninVasive self-attention mechanism (NOVA) to leverage side information effectively under the BERT framework. NOVA makes use of side information to generate better attention distribution, rather than directly altering the item embedding, which may cause information overwhelming. We validate the NOVA-BERT model on both public and commercial datasets, and our method can stably outperform the state-of-the-art models with negligible computational overheads.Comment: Accepted at AAAI 202

Can Large Language Models Be Good Companions? An LLM-Based Eyewear System with Conversational Common Ground

Developing chatbots as personal companions has long been a goal of artificial intelligence researchers. Recent advances in Large Language Models (LLMs) have delivered a practical solution for endowing chatbots with anthropomorphic language capabilities. However, it takes more than LLMs to enable chatbots that can act as companions. Humans use their understanding of individual personalities to drive conversations. Chatbots also require this capability to enable human-like companionship. They should act based on personalized, real-time, and time-evolving knowledge of their owner. We define such essential knowledge as the \textit{common ground} between chatbots and their owners, and we propose to build a common-ground-aware dialogue system from an LLM-based module, named \textit{OS-1}, to enable chatbot companionship. Hosted by eyewear, OS-1 can sense the visual and audio signals the user receives and extract real-time contextual semantics. Those semantics are categorized and recorded to formulate historical contexts from which the user's profile is distilled and evolves over time, i.e., OS-1 gradually learns about its user. OS-1 combines knowledge from real-time semantics, historical contexts, and user-specific profiles to produce a common-ground-aware prompt input into the LLM module. The LLM's output is converted to audio, spoken to the wearer when appropriate.We conduct laboratory and in-field studies to assess OS-1's ability to build common ground between the chatbot and its user. The technical feasibility and capabilities of the system are also evaluated. OS-1, with its common-ground awareness, can significantly improve user satisfaction and potentially lead to downstream tasks such as personal emotional support and assistance.Comment: 36 pages, 25 figures, Under review at ACM IMWU

Degradation of the Separase-cleaved Rec8, a Meiotic Cohesin Subunit, by the N-end Rule Pathway

The Ate1 arginyltransferase (R-transferase) is a component of the N-end rule pathway, which recognizes proteins containing N-terminal degradation signals called N-degrons, polyubiquitylates these proteins, and thereby causes their degradation by the proteasome. Ate1 arginylates N-terminal Asp, Glu, or (oxidized) Cys. The resulting N-terminal Arg is recognized by ubiquitin ligases of the N-end rule pathway. In the yeast Saccharomyces cerevisiae, the separase-mediated cleavage of the Scc1/Rad21/Mcd1 cohesin subunit generates a C-terminal fragment that bears N-terminal Arg and is destroyed by the N-end rule pathway without a requirement for arginylation. In contrast, the separase-mediated cleavage of Rec8, the mammalian meiotic cohesin subunit, yields a fragment bearing N-terminal Glu, a substrate of the Ate1 R-transferase. Here we constructed and used a germ cell-confined Ate1−/− mouse strain to analyze the separase-generated C-terminal fragment of Rec8. We show that this fragment is a short-lived N-end rule substrate, that its degradation requires N-terminal arginylation, and that male Ate1−/− mice are nearly infertile, due to massive apoptotic death of Ate1−/− spermatocytes during the metaphase of meiosis I. These effects of Ate1 ablation are inferred to be caused, at least in part, by the failure to destroy the C-terminal fragment of Rec8 in the absence of N-terminal arginylation

MiR-365-3p is a negative regulator in IL-17-mediated asthmatic inflammation

BackgroundInterleukin-17, the major proinflammatory cytokine secreted by Th17 cells, makes essential contribution to pathogenesis of severe asthma, while the detailed mechanisms, especially the involvement of microRNAs which are also important participants in asthma progression, remains largely unclear.MethodsIn this study, we established a house dust mite (HDM) extract-induced murine asthmatic models and the miRNA expression in the lung tissues of mice were profiled by miRNA microarray assay. The effect of miR-365-3p on IL-17-mediated inflammation was examined by qRT-PCR and immunoblotting analysis. The involvement of ARRB2 as target gene of miR-365-3p was verified by overexpression or RNA interference.ResultsHDM extract-induced asthmatic inflammation was proved to be IL17-mediated and miR-365-3p was screened out to be the only miRNA exclusively responsive to IL-17. miR-365-3p, whose expression was significantly downregulated upon IL-17 stimulation, was demonstrated to exert remarkable anti-inflammatory effect to decrease IL-17-provoked inflammatory cytokines (KC/IL-8 and IL-6) in both airway epithelial cells and macrophages of murine and human origins, verifying its universal antagonizing activity against IL-17-initiated inflammation across the two species. ARRB2 was characterized as the key target of miR-365-3p to negate IL-17-induced inflammatory cytokines.ConclusionTaken together, our data supported the notion that miR-365-3p, which was diminished by IL-17 in murine and human asthmatic pathogenesis, functioned as an essential negative mediator in IL-17-stimuated inflammatory response by targeting ARRB2, which would shed new light to the understanding and therapeutics thereof of asthmatic inflammation

The DArk Matter Particle Explorer mission

The DArk Matter Particle Explorer (DAMPE), one of the four scientific space science missions within the framework of the Strategic Pioneer Program on Space Science of the Chinese Academy of Sciences, is a general purpose high energy cosmic-ray and gamma-ray observatory, which was successfully launched on December 17th, 2015 from the Jiuquan Satellite Launch Center. The DAMPE scientific objectives include the study of galactic cosmic rays up to $\sim 10$ TeV and hundreds of TeV for electrons/gammas and nuclei respectively, and the search for dark matter signatures in their spectra. In this paper we illustrate the layout of the DAMPE instrument, and discuss the results of beam tests and calibrations performed on ground. Finally we present the expected performance in space and give an overview of the mission key scientific goals.Comment: 45 pages, including 29 figures and 6 tables. Published in Astropart. Phy

Direct detection of a break in the teraelectronvolt cosmic-ray spectrum of electrons and positrons

High energy cosmic ray electrons plus positrons (CREs), which lose energy quickly during their propagation, provide an ideal probe of Galactic high-energy processes and may enable the observation of phenomena such as dark-matter particle annihilation or decay. The CRE spectrum has been directly measured up to $\sim 2$ TeV in previous balloon- or space-borne experiments, and indirectly up to $\sim 5$ TeV by ground-based Cherenkov $\gamma$-ray telescope arrays. Evidence for a spectral break in the TeV energy range has been provided by indirect measurements of H.E.S.S., although the results were qualified by sizeable systematic uncertainties. Here we report a direct measurement of CREs in the energy range $25~{\rm GeV}-4.6~{\rm TeV}$ by the DArk Matter Particle Explorer (DAMPE) with unprecedentedly high energy resolution and low background. The majority of the spectrum can be properly fitted by a smoothly broken power-law model rather than a single power-law model. The direct detection of a spectral break at $E \sim0.9$ TeV confirms the evidence found by H.E.S.S., clarifies the behavior of the CRE spectrum at energies above 1 TeV and sheds light on the physical origin of the sub-TeV CREs.Comment: 18 pages, 6 figures, Nature in press, doi:10.1038/nature2447

Direct Measurement of the Pseudoscalar Decay Constant fD+

The absolute branching fraction of $D^+ \to \mu^+ \nu$ has been directly measured by an analysis of a data sample of about 33 ${\rm pb^{-1}}$ collected around $\sqrt{s}=3.773$ GeV with the BES-II at the BEPC. At these energies, $D^-$ meson is produced in pair as $e^+e^-\to D^{+} D^{-}$. A total of $5321 \pm 149 \pm 160$ $D^-$ mesons are reconstructed from this data set. In the recoil side of the tagged $D^-$ mesons, $2.67\pm1.74$ purely leptonic decay events of $D^+ \to \mu^+ \nu$ are observed. This yields a branching fraction of $BF(D^+ \to \mu^+ \nu_{\mu}) = (0.122^{+0.111}_{-0.053}\pm 0.010)$, and a corresponding pseudoscalar decay constant $f_{D^+}=(371^{+129}_{-119}\pm 25)$ MeV.Comment: 7 pages, 8 figures, Submitted to Physics Letters B in October, 200