State Ownership and Firm Innovation in China: An Integrated View of Institutional and Efficiency Logics

Using two longitudinal panel datasets of Chinese manufacturing firms, we assess whether state ownership benefits or impedes firms’ innovation. We show that state ownership in an emerging economy enables a firm to obtain crucial R&D resources but makes the firm less efficient in using those resources to generate innovation, and we find that a minority state ownership is an optimal structure for innovation development in this context. Moreover, the inefficiency of state ownership in transforming R&D input into innovation output decreases when industrial competition is high, as well as for start-up firms. Our findings integrate the efficiency logic (agency theory), which views state ownership as detrimental to innovation, and institutional logic, which notes that governments in emerging economies have critical influences on regulatory policies and control over scarce resources. We discuss the implications of these findings for research on state ownership and firm innovation in emerging economies

AssistGPT: A General Multi-modal Assistant that can Plan, Execute, Inspect, and Learn

Recent research on Large Language Models (LLMs) has led to remarkable advancements in general NLP AI assistants. Some studies have further explored the use of LLMs for planning and invoking models or APIs to address more general multi-modal user queries. Despite this progress, complex visual-based tasks still remain challenging due to the diverse nature of visual tasks. This diversity is reflected in two aspects: 1) Reasoning paths. For many real-life applications, it is hard to accurately decompose a query simply by examining the query itself. Planning based on the specific visual content and the results of each step is usually required. 2) Flexible inputs and intermediate results. Input forms could be flexible for in-the-wild cases, and involves not only a single image or video but a mixture of videos and images, e.g., a user-view image with some reference videos. Besides, a complex reasoning process will also generate diverse multimodal intermediate results, e.g., video narrations, segmented video clips, etc. To address such general cases, we propose a multi-modal AI assistant, AssistGPT, with an interleaved code and language reasoning approach called Plan, Execute, Inspect, and Learn (PEIL) to integrate LLMs with various tools. Specifically, the Planner is capable of using natural language to plan which tool in Executor should do next based on the current reasoning progress. Inspector is an efficient memory manager to assist the Planner to feed proper visual information into a specific tool. Finally, since the entire reasoning process is complex and flexible, a Learner is designed to enable the model to autonomously explore and discover the optimal solution. We conducted experiments on A-OKVQA and NExT-QA benchmarks, achieving state-of-the-art results. Moreover, showcases demonstrate the ability of our system to handle questions far more complex than those found in the benchmarks.Comment: Project page: https://showlab.github.io/assistgpt

Environmental Strategy, Institutional Force, and Innovation Capability: A Managerial Cognition Perspective

Abstract(#br)Despite the rising interest in environmental strategies, few studies have examined how managerial cognition of such strategies influences actual innovation capability development. Taking a managerial cognition perspective, this study investigates how managers’ perceptions of institutional pressures relate to their focus on proactive environmental strategy, which in turn affects firms’ realized innovation capability. The findings from a primary survey and three secondary datasets of publicly listed companies in China reveal that managers’ perceived business and social pressures are positively associated with their focus on proactive environmental strategy, which consequently fosters innovation capability development. Moreover, state ownership and government administrative..

DeltaNet:Conditional Medical Report Generation for COVID-19 Diagnosis

Fast screening and diagnosis are critical in COVID-19 patient treatment. In addition to the gold standard RT-PCR, radiological imaging like X-ray and CT also works as an important means in patient screening and follow-up. However, due to the excessive number of patients, writing reports becomes a heavy burden for radiologists. To reduce the workload of radiologists, we propose DeltaNet to generate medical reports automatically. Different from typical image captioning approaches that generate reports with an encoder and a decoder, DeltaNet applies a conditional generation process. In particular, given a medical image, DeltaNet employs three steps to generate a report: 1) first retrieving related medical reports, i.e., the historical reports from the same or similar patients; 2) then comparing retrieved images and current image to find the differences; 3) finally generating a new report to accommodate identified differences based on the conditional report. We evaluate DeltaNet on a COVID-19 dataset, where DeltaNet outperforms state-of-the-art approaches. Besides COVID-19, the proposed DeltaNet can be applied to other diseases as well. We validate its generalization capabilities on the public IU-Xray and MIMIC-CXR datasets for chest-related diseases. Code is available at \url{https://github.com/LX-doctorAI1/DeltaNet}

Endogenous small-noncoding RNAs and their roles in chilling response and stress acclimation in Cassava

BACKGROUND: Small noncoding RNA (sncRNA), including microRNAs (miRNAs) and endogenous small-interfering RNAs (endo-siRNAs) are key gene regulators in eukaryotes, playing critical roles in plant development and stress tolerance. Trans-acting siRNAs (ta-siRNAs), which are secondary siRNAs triggered by miRNAs, and siRNAs from natural antisense transcripts (nat-siRNAs) are two well-studied classes of endo-siRNAs. RESULTS: In order to understand sncRNAs’ roles in plant chilling response and stress acclimation, we performed a comprehensive study of miRNAs and endo-siRNAs in Cassava (Manihot esculenta), a major source of food for the world populations in tropical regions. Combining Next-Generation sequencing and computational and experimental analyses, we profiled and characterized sncRNA species and mRNA genes from the plants that experienced severe and moderate chilling stresses, that underwent further severe chilling stress after chilling acclimation at moderate stress, and that grew under the normal condition. We also included castor bean (Ricinus communis) in our study to understand conservation of sncRNAs. In addition to known miRNAs, we identified 32 (22 and 10) novel miRNAs as well as 47 (26 and 21) putative secondary siRNA-yielding and 8 (7 and 1) nat-siRNA-yielding candidate loci in Cassava and castor bean, respectively. Among the expressed sncRNAs, 114 miRNAs, 12 ta-siRNAs and 2 nat-siRNAs showed significant expression changes under chilling stresses. CONCLUSION: Systematic and computational analysis of microRNAome and experimental validation collectively showed that miRNAs, ta-siRNAs, and possibly nat-siRNAs play important roles in chilling response and chilling acclimation in Cassava by regulating stress-related pathways, e.g. Auxin signal transduction. The conservation of these sncRNA might shed lights on the role of sncRNA-mediated pathways affected by chilling stress and stress acclimation in Euphorbiaceous plants. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-634) contains supplementary material, which is available to authorized users

Neural EGFL-Like 1 Is a Downstream Regulator of Runt-Related Transcription Factor 2 in Chondrogenic Differentiation and Maturation

Recent studies indicate that neural EGFL-like 1 (Nell-1), a secretive extracellular matrix molecule, is involved in chondrogenic differentiation. Herein, we demonstrated that Nell-1 serves as a key downstream target of runt-related transcription factor 2 (Runx2), a central regulator of chondrogenesis. Unlike in osteoblast lineage cells where Nell-1 and Runx2 demonstrate mutual regulation, further studies in chondrocytes revealed that Runx2 tightly regulates the expression of Nell-1; however, Nell-1 does not alter the expression of Runx2. More important, Nell-1 administration partially restored Runx2 deficiency–induced impairment of chondrocyte differentiation and maturation in vitro, ex vivo, and in vivo. Mechanistically, although the expression of Nell-1 is highly reliant on Runx2, the prochondrogenic function of Nell-1 persisted in Runx2−/− scenarios. The biopotency of Nell-1 is independent of the nuclear import and DNA binding functions of Runx2 during chondrogenesis. Nell-1 is a key functional mediator of chondrogenesis, thus opening up new possibilities for the application of Nell-1 in cartilage regeneration. © 2017 American Society for Investigative Patholog