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

Surface functionalized titanium scaffolds for orthopaedic application: learning from nature

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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

A Deep Learning aided Key Recovery Framework for Large-State Block Ciphers

In the seminal work published by Gohr in CRYPTO 2019, neural networks were successfully exploited to perform differential attacks on Speck32/64, the smallest member in the block cipher family Speck. The deep learning aided key-recovery attack by Gohr achieves considerable improvement in terms of time complexity upon the state-of-the-art result from the conventional cryptanalysis method. A further question is whether the advantage of deep learning aided attacks can be kept on large-state members of Speck and other primitives. Since there are several key points in Gohr’s key-recovery frameworks that seem not fit for large-state ciphers, this question stays open for years. This work provides an answer to this question by proposing a deep learning aided multi-stage key-recovery framework. To apply this key-recovery framework on large-state members of Speck, multiple neural distinguishers (NDs) are trained and carefully combined into groups. Employing the groups of NDs under the multi-stage key-recovery framework, practical attacks are designed and trialed. Experimental results show the effectiveness of the framework. The practical attacks are then extended into theoretical attacks that cover more rounds. To do that, multi-round classical differentials (CDs) are used together with the NDs. To find the CDs’ neutral bits to boost signals from the distinguishers, an efficient algorithm is proposed. As a result, considerable improvement in terms of both time and data complexity of differential key-recovery attacks on round-reduced Speck with the largest, i.e., the 128-bit state, is obtained. Besides, efficient differential attacks are achieved on round-reduced Speck with 96-bit and 64-bit states. Since most real-world block ciphers have a state size of no less than 64 bits, this work paves the way for performing cryptanalysis using deep learning on more block ciphers. The code is available at https://github.com/AI-Lab-Y/NAAF