Symbol-Based Over-the-Air Digital Predistortion Using Reinforcement Learning

We propose an over-the-air digital predistortion optimization algorithm using reinforcement learning. Based on a symbol-based criterion, the algorithm minimizes the errors between downsampled messages at the receiver side. The algorithm does not require any knowledge about the underlying hardware or channel. For a generalized memory polynomial power amplifier and additive white Gaussian noise channel, we show that the proposed algorithm achieves performance improvements in terms of symbol error rate compared with an indirect learning architecture even when the latter is coupled with a full sampling rate ADC in the feedback path. Furthermore, it maintains a satisfactory adjacent channel power ratio

Symbol-Based Over-the-Air Digital Predistortion Using Reinforcement Learning

We propose an over-the-air digital predistortion optimization algorithm using reinforcement learning. Based on a symbol-based criterion, the algorithm minimizes the errors between downsampled messages at the receiver side. The algorithm does not require any knowledge about the underlying hardware or channel. For a generalized memory polynomial power amplifier and additive white Gaussian noise channel, we show that the proposed algorithm achieves performance improvements in terms of symbol error rate compared with an indirect learning architecture even when the latter is coupled with a full sampling rate ADC in the feedback path. Furthermore, it maintains a satisfactory adjacent channel power ratio

tSF: Transformer-based Semantic Filter for Few-Shot Learning

Few-Shot Learning (FSL) alleviates the data shortage challenge via embedding discriminative target-aware features among plenty seen (base) and few unseen (novel) labeled samples. Most feature embedding modules in recent FSL methods are specially designed for corresponding learning tasks (e.g., classification, segmentation, and object detection), which limits the utility of embedding features. To this end, we propose a light and universal module named transformer-based Semantic Filter (tSF), which can be applied for different FSL tasks. The proposed tSF redesigns the inputs of a transformer-based structure by a semantic filter, which not only embeds the knowledge from whole base set to novel set but also filters semantic features for target category. Furthermore, the parameters of tSF is equal to half of a standard transformer block (less than 1M). In the experiments, our tSF is able to boost the performances in different classic few-shot learning tasks (about 2% improvement), especially outperforms the state-of-the-arts on multiple benchmark datasets in few-shot classification task

End-to-End Learning for Integrated Sensing and Communication

Integrated sensing and communication (ISAC) aims to unify radar and communication systems through a combination of joint hardware, joint waveforms, joint signal design, and joint signal processing. At high carrier frequencies, where ISAC is expected to play a major role, joint designs are challenging due to several hardware limitations. Model-based approaches, while powerful and flexible, are inherently limited by how well the models represent reality. Under model deficit, data-driven methods can provide robust ISAC performance. We present a novel approach for data-driven ISAC using an auto-encoder (AE) structure. The approach includes the proposal of the AE architecture, a novel ISAC loss function, and the training procedure. Numerical results demonstrate the power of the proposed AE, in particular under hardware impairments

Diverse Top-k Service Composition for Consumer Electronics With Digital Twin in MEC

Mobile Edge Computing (MEC) stands as an indispensable technology in the facilitation of 5G networks, enabling the deployment of widely-used services on edge servers situated in close proximity to consumer electronics. Within the MEC framework, a central role is attributed to edge service composition (ESC), pivotal in bolstering functionality and ameliorating user experiences. Presently, prevailing methods for ESC predominantly center on the prioritization of Quality of Service (QoS) optimization, presenting a solitary optimal composite service for consumer electronics invocation. Regrettably, this approach sidelines the significance of solution diversity within composite services, potentially resulting in service overload and the suboptimal utilization of edge resources. To surmount these challenges, this study integrates digital twin (DT) technology and diversified search mechanisms into the MEC domain, offering an innovative diversified top-k service composition methodology known as DSC-DT. By harnessing the capabilities of DT technology, DSC-DT enables the emulation and assessment of diverse composite service solutions within a virtual space. Specifically, the proposed methodology models the procedure of service composition within a DT environment as an issue of subgraph isomorphism. This is succeeded by the configuration of the diversification process as an independent set predicament within an undirected graph, efficiently resolved through a greedy algorithmic paradigm. It is noteworthy that DSC-DT accommodates a gamut of query types, including normal queries, constraint queries, and optimal queries. The efficacy and efficiency of the proposed approach are corroborated through comprehensive experiments conducted upon authentic datasets

Accurate and Robust 3D Facial Capture Using a Single RGBD Camera

This paper presents an automatic and robust approach that accurately captures high-quality 3D facial perfor-mances using a single RGBD camera. The key of our ap-proach is to combine the power of automatic facial feature detection and image-based 3D nonrigid registration tech-niques for 3D facial reconstruction. In particular, we de-velop a robust and accurate image-based nonrigid regis-tration algorithm that incrementally deforms a 3D template mesh model to best match observed depth image data and important facial features detected from single RGBD im-ages. The whole process is fully automatic and robust be-cause it is based on single frame facial registration frame-work. The system is flexible because it does not require any strong 3D facial priors such as blendshape models. We demonstrate the power of our approach by capturing a wide range of 3D facial expressions using a single RGBD camera and achieve state-of-the-art accuracy by comparing against alternative methods. 1

An exaggerated epinephrine-adrenergic receptor signaling impairs uterine decidualization in mice

Abstract(#br)Our understanding of the relationship between stress-derived epinephrine and early pregnancy failure remains incomplete. Here, we explored the effect of epinephrine exposure on early pregnancy and pseudopregnancy in mice. Increased expression of adrenergic receptors Adra1b, Adra2b and Adrb2 was observed during decidualization and post-implantation embryogenesis was delayed or survival impaired. Epinephrine treatment also impaired decidualization in both the gravid and pseudopregnant uterus, suggesting the effect on decidualization was independent of the conceptus. This included a suppression of endometrial stroma cell proliferation and of key decidualization regulators, including COX2, BMP2 and WNT4. Collectively, these data demonstrate that maternal epinephrine exposure during early pregnancy impairs uterine decidualization and embryo development, underlying early pregnancy failure