Temporary trade barriers and enterprise export market changes: evidence from China

In theory, previous studies believed that the export market of enterprises was homogeneous. There is no difference in each export market, which is obviously inconsistent with the actual trade situation. This paper divides the export market of enterprises into main export market and secondary export market according to the export status, explores the export changes of enterprises to the main export market and secondary export market respectively when the temporary trade barriers of the main export market to the trade exporting country are raised. This paper focuses on the impact of the main export market on the anti-dumping degree, countervailing level and the improvement of trade safeguard measures on the export conversion of enterprises between the main and secondary markets. The research shows that the increase of the anti-dumping degree of the main market against the trade exporting countries will lead to the higher probability of role exchange between the main and secondary markets; The countervailing level of the main market against the trade exporting countries rises, and the export of enterprises is more likely to turn to the secondary market; The greater the trade safeguard measures in the main market, the more likely the secondary market will become the main marke

In Situ Mineralization of Magnetite Nanoparticles in Chitosan Hydrogel

Based on chelation effect between iron ions and amino groups of chitosan, in situ mineralization of magnetite nanoparticles in chitosan hydrogel under ambient conditions was proposed. The chelation effect between iron ions and amino groups in CS–Fe complex, which led to that chitosan hydrogel exerted a crucial control on the magnetite mineralization, was proved by X-ray photoelectron spectrum. The composition, morphology and size of the mineralized magnetite nanoparticles were characterized by X-ray diffraction, Raman spectroscopy, transmission electron microscopy and thermal gravity. The mineralized nanoparticles were nonstoichiometric magnetite with a unit formula of Fe2.85O4and coated by a thin layer of chitosan. The mineralized magnetite nanoparticles with mean diameter of 13 nm dispersed in chitosan hydrogel uniformly. Magnetization measurement indicated that superparamagnetism behavior was exhibited. These magnetite nanoparticles mineralized in chitosan hydrogel have potential applications in the field of biotechnology. Moreover, this method can also be used to synthesize other kinds of inorganic nanoparticles, such as ZnO, Fe2O3and hydroxyapatite

LLMaAA: Making Large Language Models as Active Annotators

Prevalent supervised learning methods in natural language processing (NLP) are notoriously data-hungry, which demand large amounts of high-quality annotated data. In practice, acquiring such data is a costly endeavor. Recently, the superior few-shot performance of large language models (LLMs) has propelled the development of dataset generation, where the training data are solely synthesized from LLMs. However, such an approach usually suffers from low-quality issues, and requires orders of magnitude more labeled data to achieve satisfactory performance. To fully exploit the potential of LLMs and make use of massive unlabeled data, we propose LLMaAA, which takes LLMs as annotators and puts them into an active learning loop to determine what to annotate efficiently. To learn robustly with pseudo labels, we optimize both the annotation and training processes: (1) we draw k-NN examples from a small demonstration pool as in-context examples, and (2) we adopt the example reweighting technique to assign training samples with learnable weights. Compared with previous approaches, LLMaAA features both efficiency and reliability. We conduct experiments and analysis on two classic NLP tasks, named entity recognition and relation extraction. With LLMaAA, task-specific models trained from LLM-generated labels can outperform the teacher within only hundreds of annotated examples, which is much more cost-effective than other baselines.Comment: Findings of EMNLP 2023 camera read

BASAR:Black-box Attack on Skeletal Action Recognition

Skeletal motion plays a vital role in human activity recognition as either an independent data source or a complement. The robustness of skeleton-based activity recognizers has been questioned recently, which shows that they are vulnerable to adversarial attacks when the full-knowledge of the recognizer is accessible to the attacker. However, this white-box requirement is overly restrictive in most scenarios and the attack is not truly threatening. In this paper, we show that such threats do exist under black-box settings too. To this end, we propose the first black-box adversarial attack method BASAR. Through BASAR, we show that adversarial attack is not only truly a threat but also can be extremely deceitful, because on-manifold adversarial samples are rather common in skeletal motions, in contrast to the common belief that adversarial samples only exist off-manifold. Through exhaustive evaluation and comparison, we show that BASAR can deliver successful attacks across models, data, and attack modes. Through harsh perceptual studies, we show that it achieves effective yet imperceptible attacks. By analyzing the attack on different activity recognizers, BASAR helps identify the potential causes of their vulnerability and provides insights on what classifiers are likely to be more robust against attack. Code is available at https://github.com/realcrane/BASAR-Black-box-Attack-on-Skeletal-Action-Recognition.Comment: Accepted in CVPR 202

The oligopeptide ABC transporter OppA4 negatively regulates the virulence factor OspC production of the Lyme disease pathogen

Borrelia burgdorferi sensu lato, the agent of Lyme disease, exists in nature through a complex enzootic life cycle that involves both ticks and mammals. The B. burgdorferi genome encodes five Oligopeptide ABC transporters (Opp) that are predicted to be involve in transport of various nutrients. Previously, it was reported that OppA5 is important for the optimal production of OspC, a major virulence factor of B. burgdorferi. In this study, possible role of another Oligopeptide ABC transporter, OppA4 in ospC expression was investigated by construction of an oppA4 deletion mutant and the complemented strain. Inactivation of oppA4 resulted an increased production of OspC, suggesting that OppA4 has a negative impact on ospC expression. Expression of ospC is controlled by Rrp2-RpoN-RpoS, the central pathway essential for mammal infection. We showed that increased ospC expression in the oppA4 mutant was due to an increased rpoS expression. We then further investigated how OppA4 negatively regulates this pathway. Two regulators, BosR and BadR, are known to positively and negatively, respectively, regulate the Rrp2-RpoN-RpoS pathway. We found that deletion of oppA4 resulted in an increased level of BosR. Previous reports showed that bosR is mainly regulated at the post-transcriptional level by other factors. However, OppA4 appears to negatively regulate bosR expression at the transcriptional level. The finding of OppA4 involved in regulation of the Rrp2-RpoN-RpoS pathway further reinforces the importance of nutritional virulence to the enzootic cycle of B. burgdorferi

Two is Better Than One: Answering Complex Questions by Multiple Knowledge Sources with Generalized Links

Incorporating multiple knowledge sources is proven to be beneficial for answering complex factoid questions. To utilize multiple knowledge bases (KB), previous works merge all KBs into a single graph via entity alignment and reduce the problem to question-answering (QA) over the fused KB. In reality, various link relations between KBs might be adopted in QA over multi-KBs. In addition to the identity between the alignable entities (i.e. full link), unalignable entities expressing the different aspects or types of an abstract concept may also be treated identical in a question (i.e. partial link). Hence, the KB fusion in prior works fails to represent all types of links, restricting their ability to comprehend multi-KBs for QA. In this work, we formulate the novel Multi-KB-QA task that leverages the full and partial links among multiple KBs to derive correct answers, a benchmark with diversified link and query types is also constructed to efficiently evaluate Multi-KB-QA performance. Finally, we propose a method for Multi-KB-QA that encodes all link relations in the KB embedding to score and rank candidate answers. Experiments show that our method markedly surpasses conventional KB-QA systems in Multi-KB-QA, justifying the necessity of devising this task