EDIRNet: an unsupervised deformable registration model for X-ray and neutron images.

For high-precision industrial non-destructive testing, multimodal image registration technology can be employed to register X-ray and neutron images. X-ray and neutron image registration algorithms usually use conventional methods through iterative optimization. These methods will increase the cost of registration time and require more initialization parameters. The imaging results of internal sample structures can suffer from edge blurring due to the influence of a neutron beam collimator aperture, X-ray focal point, and imaging angles. We present an unsupervised learning model, EDIRNet, based on deep learning for deformable registration of X-ray and neutron images. We define the registration process as a function capable of estimating the flow field from input images. By leveraging deep learning techniques, we effectively parameterize this function. Consequently, given a registration image, our optimized network parameters enable rapid and direct estimation of the flow field between the images. We design an attention-based edge enhancement module to enhance the edge features of the image. For evaluating our presented network model, we utilize a dataset including 552 pairs of X-ray and neutron images. The experimental results show that the registration accuracy of EDIRNet reaches 93.09%. Compared with traditional algorithms, the accuracy of EDIRNet is improved by 3.17%, and the registration time is reduced by 28.75 s.

Enhanced microwave metrology using an optical grating in Rydberg atoms.

An enhanced measurement of the microwave (MW) electric (E) field is proposed using an optical grating in Rydberg atoms. Electromagnetically induced transparency (EIT) of Rydberg atoms appears driven by a probe field and a control field. The EIT transmission spectrum is modulated by an optical grating. When a MW field drives the Rydberg transition, the central principal maximum of the grating spectrum splits. It is interesting to find that the magnitude of the sharp grating spectrum changes linearly with the MW E-field strength, which can be used to measure the MW E-field. The simulation result shows that the minimum detectable E-field strength is nearly 1/8 of that without gratings, and its measurement accuracy could be enhanced by about 60 times. Other discussion of MW metrology based on a grating spectrum is also presented.

Slicing Resource Allocation Based on Dueling DQN for eMBB and URLLC Hybrid Services in Heterogeneous Integrated Networks.

In 5G/B5G communication systems, network slicing is utilized to tackle the problem of the allocation of network resources for diverse services with changing demands. We proposed an algorithm that prioritizes the characteristic requirements of two different services and tackles the problem of allocation and scheduling of resources in the hybrid services system with eMBB and URLLC. Firstly, the resource allocation and scheduling are modeled, subject to the rate and delay constraints of both services. Secondly, the purpose of adopting a dueling deep Q network (Dueling DQN) is to approach the formulated non-convex optimization problem innovatively, in which a resource scheduling mechanism and the ϵ-greedy strategy were utilized to select the optimal resource allocation action. Moreover, the reward-clipping mechanism is introduced to enhance the training stability of Dueling DQN. Meanwhile, we choose a suitable bandwidth allocation resolution to increase flexibility in resource allocation. Finally, the simulations indicate that the proposed Dueling DQN algorithm has excellent performance in terms of quality of experience (QoE), spectrum efficiency (SE) and network utility, and the scheduling mechanism makes the performance much more stable. In contrast with Q-learning, DQN as well as Double DQN, the proposed algorithm based on Dueling DQN improves the network utility by 11%, 8% and 2%, respectively.

Video Process Mining and Model Matching for Intelligent Development: Conformance Checking.

Traditional business process-extraction models mainly rely on structured data such as logs, which are difficult to apply to unstructured data such as images and videos, making it impossible to perform process extractions in many data scenarios. Moreover, the generated process model lacks analysis consistency of the process model, resulting in a single understanding of the process model. To solve these two problems, a method of extracting process models from videos and analyzing the consistency of process models is proposed. Video data are widely used to capture the actual performance of business operations and are key sources of business data. Video data preprocessing, action placement and recognition, predetermined models, and conformance verification are all included in a method for extracting a process model from videos and analyzing the consistency between the process model and the predefined model. Finally, the similarity was calculated using graph edit distances and adjacency relationships (GED_NAR). The experimental results showed that the process model mined from the video was better in line with how the business was actually carried out than the process model derived from the noisy process logs.

Two Tier Slicing Resource Allocation Algorithm Based on Deep Reinforcement Learning and Joint Bidding in Wireless Access Networks.

Network slicing (NS) is an emerging technology in recent years, which enables network operators to slice network resources (e.g., bandwidth, power, spectrum, etc.) in different types of slices, so that it can adapt to different application scenarios of 5 g network: enhanced mobile broadband (eMBB), massive machine-type communications (mMTC) and ultra-reliable and low-latency communications (URLLC). In order to allocate these sliced network resources more effectively to users with different needs, it is important that manage the allocation of network resources. Actually, in the practical network resource allocation problem, the resources of the base station (BS) are limited and the demand of each user for mobile services is different. To better deal with the resource allocation problem, more effective methods and algorithms have emerged in recent years, such as the bidding method, deep learning (DL) algorithm, ant colony algorithm (AG), and wolf colony algorithm (WPA). This paper proposes a two tier slicing resource allocation algorithm based on Deep Reinforcement Learning (DRL) and joint bidding in wireless access networks. The wireless virtual technology divides mobile operators into infrastructure providers (InPs) and mobile virtual network operators (MVNOs). This paper considers a single base station, multi-user shared aggregated bandwidth radio access network scenario and joins the MVNOs to fully utilize base station resources, and divides the resource allocation process into two tiers. The algorithm proposed in this paper takes into account both the utilization of base station (BS) resources and the service demand of mobile users (MUs). In the upper tier, each MVNO is treated as an agent and uses a combination of bidding and Deep Q network (DQN) allows the MVNO to get more resources from the base station. In the lower tier allocation process, each MVNO distributes the received resources to the users who are connected to it, which also uses the Dueling DQN method for iterative learning to find the optimal solution to the problem. The results show that in the upper tier, the total system utility function and revenue obtained by the proposed algorithm are about 5.4% higher than double DQN and about 2.6% higher than Dueling DQN; In the lower tier, the user service quality obtained by using the proposed algorithm is more stable, the system utility function and Se are about 0.5-2.7% higher than DQN and Double DQN, but the convergence is faster.

An Enhanced Belief Propagation Flipping Decoder for Polar Codes with Stepping Strategy.

The Belief Propagation (BP) algorithm has the advantages of high-speed decoding and low latency. To improve the block error rate (BLER) performance of the BP-based algorithm, the BP flipping algorithm was proposed. However, the BP flipping algorithm attempts numerous useless flippings for improving the BLER performance. To reduce the number of decoding attempts needed without any loss of BLER performance, in this paper a metric is presented to evaluate the likelihood that the bits would correct the BP flipping decoding. Based on this, a BP-Step-Flipping (BPSF) algorithm is proposed which only traces the unreliable bits in the flip set (FS) to flip and skips over the reliable ones. In addition, a threshold ß is applied when the magnitude of the log-likelihood ratio (LLR) is small, and an enhanced BPSF (EBPSF) algorithm is presented to lower the BLER. With the same FS, the proposed algorithm can reduce the average number of iterations efficiently. Numerical results show the average number of iterations for EBPSF-1 decreases by 77.5% when N = 256, compared with the BP bit-flip-1 (BPF-1) algorithm at Eb/N0 = 1.5 dB.

DLSTM-Based Successive Cancellation Flipping Decoder for Short Polar Codes.

Polar code has been adopted as the control channel coding scheme for the fifth generation (5G), and the performance of short polar codes is receiving intensive attention. The successive cancellation flipping (SC flipping) algorithm suffers a significant performance loss in short block lengths. To address this issue, we propose a double long short-term memory (DLSTM) neural network to locate the first error bit. To enhance the prediction accuracy of the DLSTM network, all frozen bits are clipped in the output layer. Then, Gaussian approximation is applied to measure the channel reliability and rank the flipping set to choose the least reliable position for multi-bit flipping. To be robust under different codewords, padding and masking strategies aid the network architecture to be compatible with multiple block lengths. Numerical results indicate that the error-correction performance of the proposed algorithm is competitive with that of the CA-SCL algorithm. It has better performance than the machine learning-based multi-bit flipping SC (ML-MSCF) decoder and the dynamic SC flipping (DSCF) decoder for short polar codes.

Enhancing optical delay using cross-Kerr nonlinearity in Rydberg atoms.

A scheme to enhance the optical delay in Rydberg atoms is proposed. In the linear case, the optical delay in a four-level system can be significantly enhanced compared to that of the three-level system. However, the width of the transparent window will decrease with an increase in the optical delay. In the nonlinear case, the nonlinear dispersion becomes steep around the transparency window. The enhanced cross-Kerr nonlinearity mainly contributes to the effective dispersion, which dramatically increases the optical delay. The simulation result shows that the optical delay of the system could be enhanced tens of times; moreover, the wide transparency window remains. So the delay-bandwidth product could be significantly improved due to nonlinear dispersion. We further examine Gaussian pulse propagation in the Rydberg atoms.

DIC-Transformer: interpretation of plant disease classification results using image caption generation technology.

Disease image classification systems play a crucial role in identifying disease categories in the field of agricultural diseases. However, current plant disease image classification methods can only predict the disease category and do not offer explanations for the characteristics of the predicted disease images. Due to the current situation, this paper employed image description generation technology to produce distinct descriptions for different plant disease categories. A two-stage model called DIC-Transformer, which encompasses three tasks (detection, interpretation, and classification), was proposed. In the first stage, Faster R-CNN was utilized to detect the diseased area and generate the feature vector of the diseased image, with the Swin Transformer as the backbone. In the second stage, the model utilized the Transformer to generate image captions. It then generated the image feature vector, which is weighted by text features, to improve the performance of image classification in the subsequent classification decoder. Additionally, a dataset containing text and visualizations for agricultural diseases (ADCG-18) was compiled. The dataset contains images of 18 diseases and descriptive information about their characteristics. Then, using the ADCG-18, the DIC-Transformer was compared to 11 existing classical caption generation methods and 10 image classification models. The evaluation indicators for captions include Bleu1-4, CiderD, and Rouge. The values of BLEU-1, CIDEr-D, and ROUGE were 0.756, 450.51, and 0.721. The results of DIC-Transformer were 0.01, 29.55, and 0.014 higher than those of the highest-performing comparison model, Fc. The classification evaluation metrics include accuracy, recall, and F1 score, with accuracy at 0.854, recall at 0.854, and F1 score at 0.853. The results of DIC-Transformer were 0.024, 0.078, and 0.075 higher than those of the highest-performing comparison model, MobileNetV2. The results indicate that the DIC-Transformer outperforms other comparison models in classification and caption generation.

Dynamic N6-methyladenosine RNA methylation landscapes reveal epi-transcriptomic modulation induced by ammonia nitrogen exposure in the Pacific whiteleg shrimp Litopenaeus vannamei.

Despite the versatility of RNA m6A methylation in regulating various biological processes, its involvement in the physiological response to ammonia nitrogen toxicity in decapod crustaceans like shrimp remains enigmatic. Here, we provided the first characterization of dynamic RNA m6A methylation landscapes induced by toxic ammonia exposure in the Pacific whiteleg shrimp Litopenaeus vannamei. The global m6A methylation level showed significant decrease following ammonia exposure, and most of the m6A methyltransferases and m6A binding proteins were significantly repressed. Distinct from many well-studied model organisms, m6A methylated peaks in the transcriptome of L. vannamei were enriched not only near the termination codon and in the 3' untranslated region (UTR), but also around the start codon and in the 5' UTR. Upon ammonia exposure, 11,430 m6A peaks corresponding to 6113 genes were hypo-methylated, and 5660 m6A peaks from 3912 genes were hyper-methylated. The differentially methylated genes showing significant changes in expression were over-represented by genes associated with metabolism, cellular immune defense and apoptotic signaling pathways. Notably, the m6A-modified ammonia-responsive genes encompassed a subset of genes related to glutamine synthesis, purine conversion and urea production, implying that m6A methylation may modulate shrimp ammonia stress responses partly through these ammonia metabolic processes.

Effects of Compound Feed Attractants on Growth Performance, Feed Utilization, Intestinal Histology, Protein Synthesis, and Immune Response of White Shrimp (Litopenaeus Vannamei).

To investigate the effects of compound attractants on the growth performance, feed utilization, intestinal morphology, protein synthesis, and immune response of Litopenaeus vannamei, the following seven diets were formulated: a positive control (P), a negative control (N), and five diets with compound attractants which were labeled as A, B, C, D, and E, each with four of five tested attractants (yeast extract, squid visceral powder, fish soluble, and squid paste, shrimp paste), respectively. Shrimp (0.71 ± 0.00 g) were distributed to seven groups of four replicates and fed for 7 weeks. Results showed that the final body weight, feed intake, specific growth rate, and weight gain of shrimp in the B and D groups were the greatest. Hemolymph total antioxidant capacity of shrimp in the B, D, and E groups reached the highest level. In the hepatopancreas, the activity of total nitric oxide synthase, malondialdehyde content, the expression levels of sod, myd88, eif4e2, and raptor in shrimp fed the B diet were the highest, and the highest levels of dorsal and relish were observed in the C group. In the intestine, intestinal muscle thickness and expression levels of toll and eif2α in the C group were the highest, while the highest expression level of sod and relish occurred in the B group. In summary, the B and E diets promoted the feed intake, growth performance and the antioxidant enzyme activity of L. vannamei. The C diet enhanced the protein synthesis of shrimp. Regression analysis indicated that the WG and FI of shrimp were increased as the dietary inclusion levels of squid paste and shrimp paste increased, while they were decreased as the dietary inclusion levels of yeast extract and fish soluble increased.