A desired but challenging property of compiler verification is compositionality, in the sense that the compilation correctness of a program can be deduced incrementally from that of its substructures ranging from statements, functions, and modules. This article proposes a novel compiler verification framework based on denotational semantics for better compositionality, compared to previous approaches based on small-step operational semantics and simulation theories. Our denotational semantics is defined by semantic functions that map a syntactic component to a semantic domain composed of multiple behavioral sets , with compiler correctness established through behavior refinement between the semantic domains of the source and target programs. The main contributions of this article include proposing a denotational semantics for open modules, a novel semantic linking operator, and a refinement algebra that unifies various behavior refinements, making compiler verification structured and compositional. Furthermore, our formalization captures the full meaning of a program and bridges the gap between traditional power-domain-based denotational semantics and the practical needs of compiler verification. We apply our denotation-based framework to verify the front-end of CompCert and typical optimizations on simple prototypes of imperative languages. Our results demonstrate that the compositionality from sub-statements to statements, from functions to modules, and from modules to the whole program can be effectively achieved.

Recently, models based on the Swin Transformer architecture have been applied to gaze estimation tasks, effectively improving their performance. However, the large number of parameters and high FLOPs of the Swin Transformer model result in significant computational resource demands, limiting its application scenarios. In terms of this issue, this paper proposes the Pure Swin Transformer(PST-Net) model, based on an improved Swin-T model. Furthermore, to enhance the performance of PST-Net while reducing its time and space complexity, a new lightweight model, Hybrid Swin Transformer(HST-Net), is proposed. HST-Net integrates the powerful feature extraction capabilities of CNNs with the global information interaction capabilities of Swin Transformers and improves the fully connected layers to enhance the nonlinear fitting capability of the model, which significantly boosts its performance in gaze estimation tasks. Experimental results show that compared to PST-Net, HST-Net not only achieves significant performance improvements but also greatly reduces the number of parameters and computational costs. Compared to the existing state-of-the-art method, SUGE, HST-Net reduces the number of parameters and FLOPs by 56.4% and 75.4%, respectively, making the model more lightweight and achieving higher accuracy.

Neuropeptides are essential signaling molecules produced in the nervous system that regulate diverse physiological processes and are closely implicated in the pathogenesis of neurodegenerative and neuropsychiatric disorders. Investigating neuropeptides contributes to a better understanding of their regulatory mechanisms and offers new insights into therapeutic strategies for related diseases. Therefore, accurate identification of neuropeptides is crucial for advancing biomedical research and drug development. Due to the high cost of experimental validation, various artificial intelligence methods have been developed for rapid neuropeptide identification. However, existing approaches often suffer from high computational resource consumption, slow processing speed, and poor deploy ability. Moreover, a user-friendly web server for practical application is still lacking. To this end, we propose MSKDNP, a neuropeptide prediction model based on a multi-stage knowledge distillation framework. With only 1.2% of the parameters, MSKDNP attains performance comparable to a fully fine-tuned protein language model while achieving state-of-the-art results in neuropeptide recognition. Moreover, MSKDNP provides favorable interpretability, facilitating biological understanding. A freely accessible web server is available at https://awi.cuhk.edu.cn/∼biosequence/MSKDNP/index.php.

The fifth generation (5G) network is a crucial foundation for the industrial Internet. Key performance indicator (KPI) anomaly detection in the 5G core network (5GC) plays a pivotal role in 5G applications. Some researchers have introduced Generative Adversarial Networks (GAN)-based techniques to detect anomalies. However, these methods remain limited, such as pattern collapse. In this paper, we propose MTMG, a Masked Transformer-based Multi-GAN model, to achieve highly accurate and robust anomaly detection. We use Transformer to learn the associations between data better. Specifically, MTMG employs multiple generators and a discriminator to deflect the pattern collapse dilemma. In addition, we introduce the mask mechanism to learn the normal distribution of data better and prevent the model degradation caused by anomalies in the training set. We also adopt a root cause strategy to locate the anomalies. Experimental results demonstrate that our model outperforms the baselines significantly in terms of detection performance.

Deep learning-based appearance gaze estimation methods are gaining popularity due to their high accuracy and fewer constraints from the environment. However, existing high-precision models often rely on deeper networks, leading to problems such as large parameters, long training time, and slow convergence. In terms of this issue, this paper proposes a novel lightweight gaze estimation model FGI-Net(Fusion Global Information). The model fuses global information into the CNN, effectively compensating for the need of multi-layer convolution and pooling to indirectly capture global information, while reducing the complexity of the model, improving the model accuracy and convergence speed. To validate the performance of the model, a large number of experiments are conducted, comparing accuracy with existing classical models and lightweight models, comparing convergence speed with models of different architectures, and conducting ablation experiments. Experimental results show that compared with GazeCaps, the latest gaze estimation model, FGI-Net achieves a smaller angle error with 87.1% and 79.1% reduction in parameters and FLOPs, respectively (MPIIFaceGaze is 3.74°, EyeDiap is 5.15°, Gaze360 is 10.50° and RT-Gene is 6.02°). Moreover, compared with different architectural models such as CNN and Transformer, FGI-Net is able to quickly converge to a higher accuracy range with fewer iterations of training, when achieving optimal accuracy on the Gaze360 and EyeDiap datasets, the FGI-Net model has 25% and 37.5% fewer iterations of training compared to GazeTR, respectively.

5G Core network (5GC) employs a Service Based Architecture (SBA). This architecture decomposes the control plane into multiple independent Network Functions (NFs). NFs open interfaces to provide services to other NFs, which makes the control plane more susceptible to external malicious attacks. However, existing anomaly detection methods focus more on traffic statistics features and are difficult to apply to the 5GC control plane. In this paper, we proposed GSAD, a Graph-Sequence analysis-based Anomaly Detection method for 5GC control plane traffic. We model control plane traffic as a directed graph to depict topological and NF interaction information. Further, we use the normalizing flows with temporal dependencies to mine the sequential information in the traffic. GSAD combines the topological and sequential information to provide fine-grained detection. We evaluate our proposed framework on the 5GC testbed using Free5GC and UERANSIM in various scenarios. Experimental results demonstrate that our framework outperforms the baselines significantly in terms of detection performance.

A holographic projection system using converging spherical wave illumination has been presented. The system takes into account the combination of Fresnel holographic projection and Fourier holographic projection. The effect of pixelated spatial light modulator is analyzed. By adding the quadratic phase of diffractive lens to the phase of the generated hologram, the separation of image plane from Fourier plane is achieved. Meanwhile, the zero-order beam and high diffraction orders can be filtered out by higher pass filter and aperture placed in the Fourier plane. A holographic projection system based on liquid crystal on silicon is set up. Experimental results show that not only Fresnel holographic projection but also Fourier holographic projection can be achieved without zero order beam and higher diffraction orders in this universal system.