Efficient Algorithms for Optimal 4-Bit Reversible Logic System Synthesis

Owing to the exponential nature of the memory and run-time complexity, many methods can only synthesize 3-bit reversible circuits and cannot synthesize 4-bit reversible circuits well. We mainly absorb the ideas of our 3-bit synthesis algorithms based on hash table and present the efficient algorithms which can construct almost all optimal 4-bit reversible logic circuits with many types of gates and at mini-length cost based on constructing the shortest coding and the specific topological compression; thus, the lossless compression ratio of the space of n-bit circuits reaches near 2×n!. This paper presents the first work to create all 3120218828 optimal 4-bit reversible circuits with up to 8 gates for the CNT (Controlled-NOT gate, NOT gate, and Toffoli gate) library, and it can quickly achieve 16 steps through specific cascading created circuits

Detection of cigarette appearance defects based on improved YOLOv4

Appearance defects are visible factors that affect the quality of cigarettes. Most of the consumer complaints received by tobacco companies are caused by appearance defects of cigarettes. Therefore, it is of great significance to reduce cigarettes with appearance defects. At present, tobacco factories mainly detect the appearance quality of cigarettes through manual sampling inspection. The manual method has low detection efficiency, it is difficult to unify the judgment standard, and it is easy to cause secondary pollution to cigarettes. According to the features of cigarette appearance defects, the YOLOv4 (You Only Look Once Version 4) model was improved for cigarette appearance defect detection. We have improved the following: 1) the channel attention mechanism was introduced into YOLOv4 to improve the detection precision; 2) the K-means++ algorithm was used to optimize the selection of clustering centers; 3) the spatial pyramid pooling (SPP) was replaced with atrous spatial pyramid pooling (ASPP) to improve the defect detection ability with different sizes; 4) the α-CIoU loss function was used to improve the detection precision. The mAP of our improved method reached 91.77%, the precision reached 93.32%, and the recall reached 88.81%. Compared with other models, our method has better comprehensive performance and better detection ability

Overexpression of TUF1 restores respiratory growth and fluconazole sensitivity to a Cryptococcus neoformans vad1Δ mutant

The yeast-like fungus Cryptococcus neoformans favours respiration as a mechanism of energy production, and thus depends heavily on mitochondrial function. Previous studies of a C. neoformans vad1Δ mutant revealed reduced expression of the mitochondrial elongation factor TUF1 and defects in glycerol utilization, consistent with mitochondrial dysfunction. In this study, we found that in trans expression of TUF1 in the vad1Δ mutant suppressed the mitochondrial defects, including growth on respiration-dependent carbon sources and fluconazole resistance, associated with VAD1 deletion. Tetracycline, an inhibitor of mitochondrial translation, was found to confer resistance to fluconazole in the wild-type and vad1Δ mutant, whereas the fluconazole susceptibility of the TUF1-overexpressing strain was unaffected by tetracycline treatment. In the presence of fluconazole, the vad1Δ mutant exhibited increased activation of the global transcriptional regulator Sre1. TUF1 overexpression failed to alter cleavage of Sre1 in response to fluconazole in the vad1Δ mutant, suggesting that TUF1 repression in the vad1Δ mutant is distal to Sre1, or that it occurs through an independent pathway

An Appearance Defect Detection Method for Cigarettes Based on C-CenterNet

Due to the poor adaptability of traditional methods in the cigarette detection task on the automatic cigarette production line, it is difficult to accurately identify whether a cigarette has defects and the types of defects; thus, a cigarette appearance defect detection method based on C-CenterNet is proposed. This detector uses keypoint estimation to locate center points and regresses all other defect properties. Firstly, Resnet50 is used as the backbone feature extraction network, and the convolutional block attention mechanism (CBAM) is introduced to enhance the network’s ability to extract effective features and reduce the interference of non-target information. At the same time, the feature pyramid network is used to enhance the feature extraction of each layer. Then, deformable convolution is used to replace part of the common convolution to enhance the learning ability of different shape defects. Finally, the activation function ACON (ActivateOrNot) is used instead of the ReLU activation function, and the activation operation of some neurons is adaptively selected to improve the detection accuracy of the network. The experimental results are mainly acquired via the mean Average Precision (mAP). The experimental results show that the mAP of the C-CenterNet model applied in the cigarette appearance defect detection task is 95.01%. Compared with the original CenterNet model, the model’s success rate is increased by 6.14%, so it can meet the requirements of precision and adaptability in cigarette detection tasks on the automatic cigarette production line

Inductive Method for Evaluating RFID Security Protocols

Authentication protocol verification is a difficult problem. The problem of “state space explosion” has always been inevitable in the field of verification. Using inductive characteristics, we combine mathematical induction and model detection technology to solve the problem of “state space explosion” in verifying the OSK protocol and VOSK protocol of RFID system. In this paper, the security and privacy of protocols in RFID systems are studied and analysed to verify the effectiveness of the combination of mathematical induction and model detection. We design a (r,s,t)-security experiment on the basis of privacy experiments in the RFID system according to the IND-CPA security standard in cryptography, using mathematical induction to validate the OSK protocol and VOSK protocol. Finally, the following conclusions are presented. The OSK protocol cannot resist denial of service attacks or replay attacks. The VOSK protocol cannot resist denial of service attacks but can resist replay attacks. When there is no limit on communication, the OSK protocol and VOSK protocol possess (r,s,t)-privacy; that is to say they can resist denial of service attacks

Coarse-to-Fine Structure-Aware Artistic Style Transfer

Artistic style transfer aims to use a style image and a content image to synthesize a target image that retains the same artistic expression as the style image while preserving the basic content of the content image. Many recently proposed style transfer methods have a common problem; that is, they simply transfer the texture and color of the style image to the global structure of the content image. As a result, the content image has a local structure that is not similar to the local structure of the style image. In this paper, we present an effective method that can be used to transfer style patterns while fusing the local style structure to the local content structure. In our method, different levels of coarse stylized features are first reconstructed at low resolution using a coarse network, in which style color distribution is roughly transferred, and the content structure is combined with the style structure. Then, the reconstructed features and the content features are adopted to synthesize high-quality structure-aware stylized images with high resolution using a fine network with three structural selective fusion (SSF) modules. The effectiveness of our method is demonstrated through the generation of appealing high-quality stylization results and a comparison with some state-of-the-art style transfer methods