Changes of dependency structure in East Asia from 1990 to 2000:Analysis by intermediate input according to sector

This paper attempts to interpret the situation surrounding the development of regional economy integration in East Asia by examining the degree of self-dependency and dependency on foreign countries in this region by using the International Input-Output (IIO) approach. We show that the economic interdependency in East Asia grew stronger from 1990 to 2000, with a strong upturn of the interdependency on China and Korea, and a downturn of dependency on Japan in this region. Moreover, our analysis suggests that dependency on foreign countries is increasing and self-dependency is decreasing. ASEAN4 was largely dependent on Japan, China and Korea, whereas Japan, China and Korea were largely dependent on other countries.From the fall of self-dependency in the heavy industries sectors and the decrease of dependency in sectors like the iron and steel, we can know that the economic effect of ASEAN4, China, Korea and Japan is seeping into other regions. Therefore, strong efforts should be made to strengthen the economic cooperation in this region in the future

A New Neural Distinguisher Considering Features Derived from Multiple Ciphertext Pairs

Neural aided cryptanalysis is a challenging topic, in which the neural distinguisher (N D) is a core module. In this paper, we propose a new N D considering multiple ciphertext pairs simultaneously. Besides, multiple ciphertext pairs are constructed from different keys. The motivation is that the distinguishing accuracy can be improved by exploiting features derived from multiple ciphertext pairs. To verify this motivation, we have applied this new N D to five different ciphers. Experiments show that taking multiple ciphertext pairs as input indeed brings accuracy improvement. Then, we prove that our new N D applies to two different neural aided key recovery attacks. Moreover, the accuracy improvement is helpful for reducing the data complexity of the neural aided statistic attack. The code is available at https://github.com/AI-Lab-Y/ND_mc

Neural Aided Statistical Attack for Cryptanalysis

In Crypto’19, Gohr proposed the first deep learning-based key recovery attack on 11-round Speck32/64, which opens the direction of neural aided cryptanalysis. Until now, neural aided cryptanalysis still faces two problems: (1) the attack complexity estimations rely purely on practical experiments. There is no theoretical framework for estimating theoretical complexity. (2) it does not work when there are not enough neutral bits that exist in the prepended differential. To the best of our knowledge, we are the first to solve these two problems. In this paper, we propose a Neural Aided Statistical Attack (NASA) that has the following advantages: (1) NASA supports estimating the theoretical complexity. (2) NASA does not rely on any special properties including neutral bits. (3) NASA is applicable to large-size ciphers. Moreover, we propose three methods for reducing the attack complexity of NASA. One of the methods is based on a newly proposed concept named Informative Bit that reveals an important phenomenon. Four attacks on 9-round or 10-round Speck32/64 are executed to verify the correctness of NASA. To further highlight the advantages of NASA, we have performed a series of experiments. At first, we apply NASA and Gohr’s attack to round reduced DES. Since NASA does not rely on neutral bits, NASA breaks 10-round DES while Gohr’s attack breaks 8-round DES. Then, we compare the time consumption of attacks on 11-round Speck32/64. When the newly proposed three methods are used, the time consumption of NASA is almost the same as that of Gohr’s attack. Besides, NASA is applied to 13-round Speck32/64. At last, we introduce how to analyze the resistance of large-size ciphers with respect to NASA, and apply NASA to 14-round Speck96/96. The code of this paper is available at https://github.com/AI-Lab-Y/NASA. Our work arguably raises a new direction for neural aided cryptanalysis

MiR-301a Promotes Colorectal Cancer Cell Growth and Invasion by Directly Targeting SOCS6

Background/Aims: Colorectal cancer (CRC) is one of the most common malignancies worldwide, and microRNAs play a crucial role in CRC biology. The purpose of this study was to investigate the exact functions and potential mechanisms of action of miR-301a in CRC. Methods: Quantitative real-time PCR was conducted to assess the expression of miR-301a. Cell proliferation was detected using MTT and colony formation assay, and cell invasion and migration were evaluated using Transwell assay. Luciferase reporter assay was used to identify the direct regulation of suppressor of cytokine signaling 6 (SOCS6) by miR-301a. Results: We first confirmed the upregulation of miR-301a in CRC tissues and cell lines. Gain-of-function and loss-of-function studies in the human CRC cell lines, SW480 and SW620, showed that miR-301a acts as an oncogene by increasing cell proliferation, migration and invasion as well as tumor growth. Furthermore, SOCS6 was identified as a target gene of miR-301a. Reintroduction of SOCS6 partially abrogated miR-301a-induced cell proliferation, migration and invasion. Conclusion: These data suggest that miR-301a promotes CRC progression by directly downregulating SOCS6 expression, and miR-301a may represent a novel biomarker for the prevention and treatment of CRC