Automatic Search of Impossible Differentials and Zero-Correlation Linear Hulls for ARX Ciphers

In lightweight cryptographic primitives, round functions with only simple operations XOR, modular addition and rotation are widely used nowadays. This kind of ciphers is called ARX ciphers. For ARX ciphers, impossible differential cryptanalysis and zero-correlation linear cryptanalysis are among the most powerful attacks, and the key problems for these two attacks are discovering more and longer impossible differentials(IDs) and zero-correlation linear hulls(ZCLHs). However, finding new IDs and ZCLHs for ARX ciphers has been a manual work for a long time, which has been an obstacle in improving these two attacks. This paper proposes an automatic search method to improve the efficiency of finding new IDs and ZCLHs for ARX ciphers. In order to prove the efficiency of this new tool, we take HIGHT, LEA, SPECK three typical ARX algorithms as examples to explore their longer and new impossible differentials and zero-correlation linear hulls. To the best of our knowledge, this is the first application of automatic search method for ARX ciphers on finding new IDs and ZCLHs. For HIGHT, we find more 17 round IDs and multiple 17 round ZCLHs. This is the first discovery of 17 round ZCLHs for HIGHT. For LEA, we find extra four 10 round IDs and several 9 round ZCLHs. In the specification of LEA, the designers just identified three 10 round IDs and one 7round ZCLH. For SPECK, we find thousands of 6 round IDs and forty-four 6 round ZCLHs. Neither IDs nor ZCLHs of SPECK has been proposed before. The successful application of our new tool shows great potential in improving the impossible differential cryptanalysis and zero-correlation linear cryptanalysis on ARX ciphers..

Recent impact study of automatic search function of the pacemaker atrial hysteresis on atrial arrhythmias

Objective:To explore the recent influences of pacemaker with automatic search function of atrial hysteresis on atrial arrhythmias,and to evaluate it clinical efficacy and safety.Methods:Indentify ADx DDD 5286 implanted dual chamber pacemaker for sick sinus syndrome in 43 cases.Automatic search of atrial lag was not opened with after pacemaker implantation,and the pacemaker settings were kept.Follow-up program after 3 months,DDD mode with automatic search of atrial lag was opened,and this mode was followed up for 6 months,comparing the atrial pacing percentage and DCG atrial tachyarrhythmias of pacemaker implantation to opening atrium lag mode.Results:Compared with the preoperative and operative 3 months later,dynamic electrocardiogram(DCG)24 h showed that the number of atrial premature beats(APB)and atrial tachycardia,atrial fibrillation(AF)array increased(p<.05);the cases of APB,atrial tachycardia and AF episodes were also increased(p<.05).Compared with the automatic search function in atrial hysteresis model opened with and not opened:atrial pacing percentage decreased[0.54(0.41,0.71)vs.0.82(0.65,0.93),p<.05];DCG 24 h showed that the number of APB,AF episodes was reduced(p<.05).Conclusions:Automation search function in atrial hysteresis model can obviously reduce the proportion of atrial pacing,reduce the occurrence of atrial arrhythmias;opened with automatic search function in atrial hysteresis model was safe and reliable.

Knowledge Graph Representation Learning Based on Automatic Network Search for Link Prediction

Link prediction,also known as Knowledge Graph Completion(KGC),is the common task in Knowledge Graphs(KGs)to predict missing connections between entities.Most existing methods focus on designing shallow,scalable models,which have less expressive than deep,multi-layer models.Furthermore,most operations like addition,matrix multiplications or factorization are handcrafted based on a few known relation patterns in several wellknown datasets,such as FB15k,WN18,etc.However,due to the diversity and complex nature of real-world data distribution,it is inherently difficult to preset all latent patterns.To address this issue,we proposeKGE-ANS,a novel knowledge graph embedding framework for general link prediction tasks using automatic network search.KGEANS can learn a deep,multi-layer effective architecture to adapt to different datasets through neural architecture search.In addition,the general search spacewe designed is tailored forKGtasks.We performextensive experiments on benchmark datasets and the dataset constructed in this paper.The results show that our KGE-ANS outperforms several state-of-the-art methods,especially on these datasets with complex relation patterns.