International and Intra-national Technology Spillovers and Technology Development Paths in Developing Countries: The Case of China

This paper analyses the paths of technology development among regions with heterogeneous economic and technological characteristics, focusing on the case of China. It finds that intra-national technology transfer, that is, the technology transfer from technologically advanced provinces to less advanced ones, is more important than that taking place through FDI in the backward regions. In technologically advanced areas, learning by doing, indigenous R&D and technology transfer from FDI all play a significant role in technical progress. The relationship between the strength of interprovincial technology transfer and technological distance is U-shaped, with the technology threshold falling outside the upper bound of technology distance. This suggests that technology transfer takes place more effectively when technological distance is small. The paper finds that learning by doing and R&D are important internal routes to technical progress. R&D plays a key role in the assimilation of foreign technologies, whereas learning by doing is relevant for the absorption of interprovincial technology transfers.FDI, technology spillovers, technology threshold

Entangling a series of trapped ions by moving cavity bus

Entangling multiple qubits is one of the central tasks for quantum information processings. Here, we propose an approach to entangle a number of cold ions (individually trapped in a string of microtraps) by a moved cavity. The cavity is pushed to include the ions one by one with an uniform velocity, and thus the information stored in former ions could be transferred to the latter ones by such a moving cavity bus. Since the positions of the trapped ions are precisely located, the strengths and durations of the ion-cavity interactions can be exactly controlled. As a consequence, by properly setting the relevant parameters typical multi-ion entangled states, e.g., $W$ state for 10 ions, could be deterministically generated. The feasibility of the proposal is also discussed.Comment: 8 pages, 2 figures, 1 tabl

Journal Staff

Discoba (Excavata) is an ancient group of eukaryotes with great morphological and ecological diversity. Unlike the other major divisions of Discoba (Jakobida and Euglenozoa), little is known about the mitochondrial DNAs(mtDNAs) of Heterolobosea. We have assembled a complete mtDNA genome from the aggregating heterolobosean amoeba, Acrasis kona, which consists of a single circular highly AT-rich (83.3%) molecule of 51.5 kb. Unexpectedly, A. kona mtDNA is missing roughly 40% of the protein-coding genes and nearly half of the transfer RNAs found in the only other sequenced heterolobosean mtDNAs, those of Naegleria spp. Instead, over a quarter of A. kona mtDNA consists of novel open reading frames. Eleven of the 16 protein-coding genes missing from A. kona mtDNA were identified in its nuclear DNA and polyA RNA, and phylogenetic analyses indicate that at least 10 of these 11 putative nuclear-encoded mitochondrial (NcMt) proteins arose by direct transfer from the mitochondrion. Acrasis kona mtDNA also employs C-to-U type RNA editing, and 12 homologs of DYW-type pentatricopeptide repeat (PPR) proteins implicated in plant organellar RNA editing are found in A. kona nuclear DNA. A mapping of mitochondrial gene content onto a consensus phylogeny reveals a sporadic pattern of relative stasis and rampant gene loss in Discoba. Rampant loss occurred independently in the unique common lineage leading to Heterolobosea + Tsukubamonadida and later in the unique lineage leading to Acrasis. Meanwhile, mtDNA gene content appears to be remarkably stable in the Acrasis sister lineage leading to Naegleria and in their distant relatives Jakobida

Scares and Stocks: Evidence from Twitter Sentiments During Covid-19

This paper examines the investor reaction of firm-specific pessimistic sentiment extracted from Twitter messages during the pandemic period due to the Covid-19. We find that Twitter sentiment predicts stock returns without subsequent reversals. This finding is consistent with the view that tweets provide information not already reflected in stock prices during the pandemic period. We investigate possible sources of return predictability with a Twitter sentiment. The results show that Twitter\u27s pessimistic sentiment towards the Covid-19 provides new information about the investor. This information explains about onethird of the predictive ability of Twitter sentiment for stock returns. Our findings shed new light on the predictive value of social media content for stock returns