Spatially resolved sequencing technologies have revolutionized our understanding of biological regulatory processes within tissue microenvironments by simultaneously capturing the states of genomic regions, genes, and proteins alongside the spatial organization of cells. However, inherent heterogeneity across modalities and samples poses substantial challenges for the integrative analysis of spatial omics data, underscoring the urgent need for advanced computational methods. In this study, we propose PRESENT, a contrastive learning-based integrative framework for cross-modality representation of spatial multi-omics data. PRESENT employs omics-specific encoders consisting of graph attention networks and Bayesian neural networks coupled with distribution-aware decoders to model distinct modalities, and an inter-omics alignment module for multi-omics integration. By effectively incorporating spatial dependencies with multi-omics information across diverse species and technologies, PRESENT facilitates the accurate identification of spatial domains and the elucidation of underlying regulatory mechanisms. Furthermore, PRESENT can be extended to multi-sample integration via a two-stage training workflow, which incorporates inter-batch alignment loss, intra-batch preserving loss, batch-adversarial learning, and cyclic graph refinement strategies to eliminate batch effects while retaining biological signals. Extensive experiments on tissue samples across different anatomical regions and developmental stages demonstrate that PRESENT enables the characterization of hierarchical tissue structures from a spatiotemporal perspective.

Specific emitter identification (SEI) is a crucial task in various applications such as wireless communications and radar systems. The low-pass nature of vanilla Transformers hinders the extraction of high-frequency fingerprint features, resulting in poor SEI performance. Moreover, the introduction of additional high-frequency sensing structures can increase the computational efficiency of the already computationally intensive Transformer. To address these issues, we propose a high-frequency enhanced and low-complexity Transformer named HET. The framework integrates a multihead low-complexity self-attention (MLSA) module, a high-frequency enhanced connection, and a multihead high-frequency enhanced low-complexity self-attention (MESA) module. The MLSA module reduces the computational complexity by key and value mapping. The MESA and high-frequency enhanced connection module capture high-frequency information by reconstructing the low-frequency and high-frequency components of the features. We construct three HET variants, namely, $\text {HET}_{n}$ , $\text {HET}_{u}$ , and $\text {HET}_{m}$ , based on different enhancement methods and positions using $\text {MESA}_{n}$ , $\text {MESA}_{u}$ , and $\text {MESA}_{m}$ , respectively. Extensive experiments are conducted on the XSRP, ADS-B, and Wi-Fi datasets to evaluate the proposed models, demonstrating their competitive accuracy and faster throughput compared with popular methods. Theoretical proofs of high-frequency suppression and frequency response results confirm that the proposed framework has more gain for high-frequency information in SEI. Code is available at: https://github.com/zhailei-zl/HETmodel.

The past decade has witnessed the rapid development of deep neural networks (DNNs) for automatic modulation classification (AMC). However, most of the available works learn signal features from only a single domain via DNNs, which is not reliable enough to work in uncertain and complex electromagnetic environments. In this brief, a new cross-domain signal transformer (CDSiT) is proposed for AMC, to explore the latent association between different domains of signals. By constructing a signal fusion bottleneck (SFB), CDSiT can implicitly fuse and classify signal features with complementary structures in different domains. Extensive experiments are performed on RadioML2016.10A and RadioML2018.01A, and the results show that CDSiT outperforms its counterparts, particularly for some modulation modes that are difficult to classify before. Through ablation experiences, we also verify the effectiveness of each module in CDSiT.

Deep learning methods have achieved outstanding performance in various signal tasks. However, due to degraded signals in real electromagnetic environment, it is crucial to seek methods that can improve the representation of signal features. In this paper, a Singular Value decomposition-based Attention, SVA is proposed to explore structure of signal data for adaptively enhancing intrinsic feature. Using a deep neural network as a base model, SVA performs feature semantic subspace learning through a decomposition layer and combines it with an attention layer to achieve adaptive enhancement of signal features. Moreover, we consider the gradient explosion problem brought by SVA and optimize SVA to improve the stability of training. Extensive experimental results demon-strate that applying SVA to a generalized classification model can significantly improve its ability in representations, making its recognition performance competitive with, or even better than, the state-of-the-art task-specific models.

With 5G and Edge Computing, media services observe a very big revolution nowadays. LTE led to media services evolution through enabling video streaming anytime, anywhere, and to any connected mobile device. CDN offered HTTP-based solutions for media streaming services, however real-time communication (RTC) and interactive traffic are not fully supported. 5G and Edge Computing are now enabling new experience through an Experience Delivery Network (XDN), where content creation is in real-time, on the fly and equally uses upstream and downstream links. Real-time Communication (RTC) with 5G and Edge Computing allows negligible latency between the communicating parties and is promising not only for immersive media, but also for Cloud Gaming and Tele-health applications. In this paper, we present a 360 Immersive Media solution using Intel-incubated Open WebRTC Toolkit (OWT) and edge computing platforms, while allowing media ingestion over 5G networks from multiple cameras, media control and 360 media distribution over 5G networks. Microservices architecture is used for media ingestion, control, and distribution functions, allowing the solution deployment across multiple edge computing clusters and sites. We implemented a prototype for our solution, and we present a performance evaluation showing the gain in terms of bandwidth and latency besides the modular deployment.